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            "name": "QUAST",
            "description": "QUAST stands for QUality ASsessment Tool.  \nIt evaluates a quality of genome assemblies by computing various metrics and providing nice reports.",
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                    "note": "# Running quast on a eukaryotic genome",
                    "cmd": "quast -ek assembly.fa --out output_prefix"
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                        "title": "QUAST: Quality assessment tool for genome assemblies",
                        "abstract": "Limitations of genome sequencing techniques have led to dozens of assembly algorithms, none of which is perfect. A number of methods for comparing assemblers have been developed, but none is yet a recognized benchmark. Further, most existing methods for comparing assemblies are only applicable to new assemblies of finished genomes; the problem of evaluating assemblies of previously unsequenced species has not been adequately considered. Here, we present QUAST - a quality assessment tool for evaluating and comparing genome assemblies. This tool improves on leading assembly comparison software with new ideas and quality metrics. QUAST can evaluate assemblies both with a reference genome, as well as without a reference. QUAST produces many reports, summary tables and plots to help scientists in their research and in their publications. In this study, we used QUAST to compare several genome assemblers on three datasets. QUAST tables and plots for all of them are available in the Supplementary Material, and interactive versions of these reports are on the QUAST website. © 2013 The Author.",
                        "date": "2013-04-15T00:00:00Z",
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                            {
                                "name": "Gurevich A."
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                            {
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                            {
                                "name": "Vyahhi N."
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                            {
                                "name": "Tesler G."
                            }
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                        "journal": "Bioinformatics"
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                    "email": "support@pacb.com",
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        {
            "name": "clasp",
            "description": "A fast and flexible fragment chainer that supports linear and sum-of-pair gap costs and uses highly time-efficient index structure, i.e., Johnson priority queues and range trees padded with Johnson priority queues. Chaining of short match fragments helps to quickly and accurately identify region of synteny that may not be found using common local alignment heuristics alone.",
            "homepage": "http://www.bioinf.uni-leipzig.de/Software/clasp/",
            "biotoolsID": "clasp",
            "biotoolsCURIE": "biotools:clasp",
            "version": [
                "1.1"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0495",
                            "term": "Local alignment"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Synteny"
                },
                {
                    "uri": "http://edamontology.org/topic_0097",
                    "term": "Nucleic acid structure analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
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            "link": [
                {
                    "url": "http://www.mybiosoftware.com/clasp-1-1-local-fragment-chainer-sum-of-pair-gap-costs.html",
                    "type": [
                        "Software catalogue"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "http://www.bioinf.uni-leipzig.de/Software/clasp/clasp.html",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/1748-7188-6-4",
                    "pmid": "21418573",
                    "pmcid": "PMC3072320",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Fast local fragment chaining using sum-of-pair gap costs",
                        "abstract": "Background: Fast seed-based alignment heuristics such as BLAST and BLAT have become indispensable tools in comparative genomics for all studies aiming at the evolutionary relations of proteins, genes, and non-coding RNAs. This is true in particular for the large mammalian genomes. The sensitivity and specificity of these tools, however, crucially depend on parameters such as seed sizes or maximum expectation values. In settings that require high sensitivity the amount of short local match fragments easily becomes intractable. Then, fragment chaining is a powerful leverage to quickly connect, score, and rank the fragments to improve the specificity.Results: Here we present a fast and flexible fragment chainer that for the first time also supports a sum-of-pair gap cost model. This model has proven to achieve a higher accuracy and sensitivity in its own field of application. Due to a highly time-efficient index structure our method outperforms the only existing tool for fragment chaining under the linear gap cost model. It can easily be applied to the output generated by alignment tools such as segemehl or BLAST. As an example we consider homology-based searches for human and mouse snoRNAs demonstrating that a highly sensitive BLAST search with subsequent chaining is an attractive option. The sum-of-pair gap costs provide a substantial advantage is this context.Conclusions: Chaining of short match fragments helps to quickly and accurately identify regions of homology that may not be found using local alignment heuristics alone. By providing both the linear and the sum-of-pair gap cost model, a wider range of application can be covered. The software clasp is available at http://www.bioinf.uni-leipzig.de/Software/clasp/. © 2011 Otto et al; licensee BioMed Central Ltd.",
                        "date": "2011-03-18T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "Otto C."
                            },
                            {
                                "name": "Hoffmann S."
                            },
                            {
                                "name": "Gorodkin J."
                            },
                            {
                                "name": "Stadler P.F."
                            }
                        ],
                        "journal": "Algorithms for Molecular Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": null,
                    "email": "christian@bioinf.uni-leipzig.de",
                    "url": "http://hoffmann.bioinf.uni-leipzig.de/LIFE/Christian.html",
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                    "typeEntity": "Person",
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                    ],
                    "note": null
                }
            ],
            "owner": "mbs_import",
            "additionDate": "2017-12-18T00:14:39Z",
            "lastUpdate": "2024-11-25T14:49:34.300028Z",
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        },
        {
            "name": "DFAST",
            "description": "Flexible prokaryotic genome annotation pipeline for faster genome publication.",
            "homepage": "https://dfast.nig.ac.jp/",
            "biotoolsID": "dfast",
            "biotoolsCURIE": "biotools:dfast",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0362",
                            "term": "Genome annotation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
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                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0097",
                    "term": "Nucleic acid structure analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
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                "Mac"
            ],
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                "Python"
            ],
            "license": "GPL-3.0",
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            "documentation": [
                {
                    "url": "https://dfast.nig.ac.jp/faq",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btx713",
                    "pmid": "29106469",
                    "pmcid": "PMC5860143",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DFAST: A flexible prokaryotic genome annotation pipeline for faster genome publication",
                        "abstract": "We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 min, with rich information such as pseudogenes, translation exceptions and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future. Availability and implementation The software is implemented in Python 3 and runs in both Python 2.7 and 3.4 - on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast-core/under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/. Contact yn@nig.ac.jp Supplementary informationSupplementary dataare available at Bioinformatics online.",
                        "date": "2018-03-15T00:00:00Z",
                        "citationCount": 770,
                        "authors": [
                            {
                                "name": "Tanizawa Y."
                            },
                            {
                                "name": "Fujisawa T."
                            },
                            {
                                "name": "Nakamura Y."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Yasukazu Nakamura",
                    "email": "yn@nig.ac.jp",
                    "url": null,
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                }
            ],
            "owner": "d.gabrielaitis",
            "additionDate": "2018-06-23T11:32:20Z",
            "lastUpdate": "2024-11-25T14:43:43.420077Z",
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        },
        {
            "name": "RIblast",
            "description": "An ultrafast RNA–RNA interaction prediction system based on a seed-and-extension approach.",
            "homepage": "https://funpopgen.github.io/veqtl-mapper/",
            "biotoolsID": "riblast",
            "biotoolsCURIE": "biotools:riblast",
            "version": [],
            "otherID": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0272",
                            "term": "Residue interaction prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0077",
                    "term": "Nucleic acids"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0099",
                    "term": "RNA"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac"
            ],
            "language": [
                "C++"
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            "license": "MIT",
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            "documentation": [
                {
                    "url": "https://github.com/fukunagatsu/RIblast",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btx287",
                    "pmid": "28459942",
                    "pmcid": "PMC5860064",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach",
                        "abstract": "Motivation: LncRNAs play important roles in various biological processes. Although more than 58 000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA-RNA interactions are difficult, computational prediction of lncRNA-RNA interactions is an indispensable technique. However, the high computational costs of existing RNA-RNA interaction prediction tools prevent their application to large-scale lncRNA datasets. Results: Here, we present 'RIblast', an ultrafast RNA-RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs. Availability and implementation: The source code of RIblast is freely available at https://github.com/fukunagatsu/RIblast . Contact: t.fukunaga@kurenai.waseda.jp or mhamada@waseda.jp. Supplementary information: Supplementary data are available at Bioinformatics online.",
                        "date": "2017-09-01T00:00:00Z",
                        "citationCount": 77,
                        "authors": [
                            {
                                "name": "Fukunaga T."
                            },
                            {
                                "name": "Hamada M."
                            }
                        ],
                        "journal": "Bioinformatics (Oxford, England)"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Tsukasa Fukunag",
                    "email": "t.fukunaga@kurenai.waseda.jp",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "typeEntity": "Person",
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                    ],
                    "note": null
                },
                {
                    "name": "Michiaki Hamada",
                    "email": "mhamada@waseda.jp",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "fundrefid": null,
                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "owner": "d.gabrielaitis",
            "additionDate": "2018-06-07T15:41:17Z",
            "lastUpdate": "2024-11-25T14:39:24.751904Z",
            "editPermission": {
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        },
        {
            "name": "BART",
            "description": "Transcription factor prediction tool with query gene sets or epigenomic profiles.",
            "homepage": "http://faculty.virginia.edu/zanglab/bart/",
            "biotoolsID": "bart",
            "biotoolsCURIE": "biotools:bart",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0438",
                            "term": "Transcriptional regulatory element prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_3173",
                    "term": "Epigenomics"
                }
            ],
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                "Linux",
                "Mac"
            ],
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                "Python"
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            "documentation": [
                {
                    "url": "http://faculty.virginia.edu/zanglab/bart/#tutorial",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/bty194",
                    "pmid": "29608647",
                    "pmcid": "PMC6084568",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BART: A transcription factor prediction tool with query gene sets or epigenomic profiles",
                        "abstract": "Summary: Identification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive enough to detect factors binding at distal enhancers. Here, we present binding analysis for regulation of transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research.",
                        "date": "2018-08-15T00:00:00Z",
                        "citationCount": 68,
                        "authors": [
                            {
                                "name": "Zhenjiawang Z."
                            },
                            {
                                "name": "Civelek M."
                            },
                            {
                                "name": "Miller C.L."
                            },
                            {
                                "name": "Sheffield N.C."
                            },
                            {
                                "name": "Guertin M.J."
                            },
                            {
                                "name": "Zang C."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Chongzhi Zang",
                    "email": "zang@virginia.edu",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "owner": "d.gabrielaitis",
            "additionDate": "2018-06-03T18:22:26Z",
            "lastUpdate": "2024-11-25T14:37:18.880103Z",
            "editPermission": {
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            },
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        },
        {
            "name": "FLASH",
            "description": "Identifies paired-end reads which overlap in the middle, converting them to single long reads",
            "homepage": "http://ccb.jhu.edu/software/FLASH/",
            "biotoolsID": "flash",
            "biotoolsCURIE": "biotools:flash",
            "version": [],
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3219",
                            "term": "Read pre-processing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0232",
                            "term": "Sequence merging"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0310",
                            "term": "Sequence assembly"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                }
            ],
            "operatingSystem": [
                "Linux"
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                "C"
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            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btr507",
                    "pmid": "21903629",
                    "pmcid": "PMC3198573",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "FLASH: Fast length adjustment of short reads to improve genome assemblies",
                        "abstract": "Motivation: Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. Results: We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. © The Author 2011. Published by Oxford University Press. All rights reserved.",
                        "date": "2011-11-01T00:00:00Z",
                        "citationCount": 10908,
                        "authors": [
                            {
                                "name": "Magoc T."
                            },
                            {
                                "name": "Salzberg S.L."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "FLASh team",
                    "email": "flash.comment@gmail.com",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
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                    ],
                    "note": null
                }
            ],
            "owner": "seqwiki_import",
            "additionDate": "2017-01-13T13:14:36Z",
            "lastUpdate": "2024-11-25T14:26:32.217431Z",
            "editPermission": {
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            "validated": 1,
            "homepage_status": 0,
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        },
        {
            "name": "blastprodom",
            "description": "Recursive psi-blast against the ProDom database.",
            "homepage": "http://prodom.prabi.fr/prodom/current/documentation/blastProDom.html",
            "biotoolsID": "blastprodom",
            "biotoolsCURIE": "biotools:blastprodom",
            "version": [
                "1"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0346",
                            "term": "Sequence similarity search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0292",
                            "term": "Sequence alignment"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
                            },
                            "format": []
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web service"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
            "operatingSystem": [],
            "language": [],
            "license": null,
            "collectionID": [
                "BLAST"
            ],
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            "elixirCommunity": [],
            "link": [
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                    "type": [
                        "Software catalogue"
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                    "note": null
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            ],
            "download": [],
            "documentation": [
                {
                    "url": "http://prodom.prabi.fr/prodom/current/documentation/blastProDom.html",
                    "type": [
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                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btg111",
                    "pmid": "12724303",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Do current sequence analysis algorithms disclose multifunctional (moonlighting) proteins?",
                        "abstract": "Summary: A number of multifunctional, 'moonlighting', proteins have been analyzed by different current programs to test wheter they identify both functions. PSI-BLAST and PRODOM perform best in predicting the alternative function.",
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                                "name": "Gomez A."
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                            {
                                "name": "Pinol J."
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                            {
                                "name": "Querol E."
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                        "title": "BlastR-fast and accurate database searches for non-coding RNAs",
                        "abstract": "We present and validate BlastR, a method for efficiently and accurately searching non-coding RNAs. Our approach relies on the comparison of di-nucleotides using BlosumR, a new log-odd substitution matrix. In order to use BlosumR for comparison, we recoded RNA sequences into protein-like sequences. We then showed that BlosumR can be used along with the BlastP algorithm in order to search non-coding RNA sequences. Using Rfam as a gold standard, we benchmarked this approach and show BlastR to be more sensitive than BlastN. We also show that BlastR is both faster and more sensitive than BlastP used with a single nucleotide log-odd substitution matrix. BlastR, when used in combination with WU-BlastP, is about 5 more accurate than WU-BlastN and about 50 times slower. The approach shown here is equally effective when combined with the NCBI-Blast package. The software is an open source freeware available from www.tcoffee.org/blastr.html. © 2011 The Author(s).",
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                            {
                                "name": "Bussotti G."
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                            {
                                "name": "Raineri E."
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                            {
                                "name": "Erb I."
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                            {
                                "name": "Zytnicki M."
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                            {
                                "name": "Wilm A."
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                            {
                                "name": "Beaudoing E."
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                            {
                                "name": "Bucher P."
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                            {
                                "name": "Notredame C."
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                    "term": "Proteins"
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                    "term": "Gene and protein families"
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                    "metadata": {
                        "title": "BLAST2SRS, a web server for flexible retrieval of related protein sequences in the SWISS-PROT and SPTrEMBL databases",
                        "abstract": "SRS (Sequence Retrieval System) is a widely used keyword search engine for querying biological databases. BLAST2 is the most widely used tool to query databases by sequence similarity search. These tools allow users to retrieve sequences by shared keyword or by shared similarity, with many public web servers available. However, with the increasingly large datasets available it is now quite common that a user is interested in some subset of homologous sequences but has no efficient way to restrict retrieval to that set. By allowing the user to control SRS from the BLAST output, BLAST2SRS (http://blast2srs.embl.de/) aims to meet this need. This server therefore combines the two ways to search sequence databases: similarity and keyword. © Oxford University Press 2001.",
                        "date": "2003-07-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Bimpikis K."
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                            {
                                "name": "Budd A."
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                            {
                                "name": "Linding R."
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                            {
                                "name": "Gibson T.J."
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                        ],
                        "journal": "Nucleic Acids Research"
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                    "metadata": {
                        "title": "BLASTO: A tool for searching orthologous groups",
                        "abstract": "We present BLAST on Orthologous groups (BLASTO), a modified BLAST tool for searching orthologous group data. It treats each orthologous group as a unit and outputs a ranked list of orthologous groups instead of single sequences. By filtering out redundancy and putative paralogs, sequence comparisons to orthologous groups, instead of to single sequences in the database, can improve both functional prediction and phylogenetic inference. BLASTO computes the significance score of each orthologous group based on the individual BLAST hits in the orthologous group, using the number of taxa in the group as an optional weight. This allows users to control the species diversity of the orthologous groups. BLASTO incorporates the best-known multispecies ortholog databases, including NCBI Clusters of Orthologous Group, NCBI euKaryotic Orthologous Group database, OrthoMCL, MultiParanoid and TIGR Eukaryotic Gene Orthologues database, and offers a useful platform to integrate orthology information into functional inference and evolutionary studies of individual sequences. BLASTO is accessible online at http://oxytricha.princeton.edu/BlastO. © 2007 The Author(s).",
                        "date": "2007-07-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Zhou Y."
                            },
                            {
                                "name": "Landweber L.F."
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                        "journal": "Nucleic Acids Research"
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                    "metadata": {
                        "title": "H-BLAST: A fast protein sequence alignment toolkit on heterogeneous computers with GPUs",
                        "abstract": "Motivation: The sequence alignment is a fundamental problem in bioinformatics. BLAST is a routinely used tool for this purpose with over 118 000 citations in the past two decades. As the size of bio-sequence databases grows exponentially, the computational speed of alignment softwares must be improved. Results: We develop the heterogeneous BLAST (H-BLAST), a fast parallel search tool for a heterogeneous computer that couples CPUs and GPUs, to accelerate BLASTX and BLASTP - basic tools of NCBI-BLAST. H-BLAST employs a locally decoupled seed-extension algorithm for better performance on GPUs, and offers a performance tuning mechanism for better efficiency among various CPUs and GPUs combinations. H-BLAST produces identical alignment results as NCBI-BLAST and its computational speed is much faster than that of NCBI-BLAST. Speedups achieved by H-BLAST over sequential NCBI-BLASTP (resp. NCBI-BLASTX) range mostly from 4 to 10 (resp. 5 to 7.2). With 2 CPU threads and 2 GPUs, H-BLAST can be faster than 16-threaded NCBI-BLASTX. Furthermore, H-BLAST is 1.5-4 times faster than GPU-BLAST.",
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                        "authors": [
                            {
                                "name": "Ye W."
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                            {
                                "name": "Chen Y."
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                            {
                                "name": "Zhang Y."
                            },
                            {
                                "name": "Xu Y."
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                        "journal": "Bioinformatics"
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                    "metadata": {
                        "title": "Basic local alignment search tool",
                        "abstract": "A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straight-forward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity. © 1990, Academic Press Limited. All rights reserved.",
                        "date": "1990-01-01T00:00:00Z",
                        "citationCount": 77792,
                        "authors": [
                            {
                                "name": "Altschul S.F."
                            },
                            {
                                "name": "Gish W."
                            },
                            {
                                "name": "Miller W."
                            },
                            {
                                "name": "Myers E.W."
                            },
                            {
                                "name": "Lipman D.J."
                            }
                        ],
                        "journal": "Journal of Molecular Biology"
                    }
                },
                {
                    "doi": "10.1093/nar/gkw343",
                    "pmid": "27137889",
                    "pmcid": "PMC4987906",
                    "type": [
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                    "note": null,
                    "metadata": {
                        "title": "The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update",
                        "abstract": "High-throughput data production technologies, particularly ‘next-generation’ DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.",
                        "date": "2016-07-08T00:00:00Z",
                        "citationCount": 1410,
                        "authors": [
                            {
                                "name": "Afgan E."
                            },
                            {
                                "name": "Baker D."
                            },
                            {
                                "name": "van den Beek M."
                            },
                            {
                                "name": "Blankenberg D."
                            },
                            {
                                "name": "Bouvier D."
                            },
                            {
                                "name": "Cech M."
                            },
                            {
                                "name": "Chilton J."
                            },
                            {
                                "name": "Clements D."
                            },
                            {
                                "name": "Coraor N."
                            },
                            {
                                "name": "Eberhard C."
                            },
                            {
                                "name": "Gruning B."
                            },
                            {
                                "name": "Guerler A."
                            },
                            {
                                "name": "Hillman-Jackson J."
                            },
                            {
                                "name": "Kuster G.V."
                            },
                            {
                                "name": "Rasche E."
                            },
                            {
                                "name": "Soranzo N."
                            },
                            {
                                "name": "Turaga N."
                            },
                            {
                                "name": "Taylor J."
                            },
                            {
                                "name": "Nekrutenko A."
                            },
                            {
                                "name": "Goecks J."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.7490/f1000research.1114334.1",
                    "pmid": null,
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                    "type": [
                        "Other"
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                {
                    "name": "Galaxy Support Team",
                    "email": "galaxy@pasteur.fr",
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        {
            "name": "BLANT",
            "description": "Basic Local Alignment for Networks Tool.",
            "homepage": "https://github.com/waynebhayes/BLANT",
            "biotoolsID": "BLANT",
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            "version": [],
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            "toolType": [
                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0084",
                    "term": "Phylogeny"
                }
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                "Mac"
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                "C"
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            "license": "Unlicense",
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            "cost": "Free of charge",
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            "link": [
                {
                    "url": "https://github.com/waynebhayes/BLANT/issues",
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                        "Issue tracker"
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            ],
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                {
                    "url": "https://github.com/waynebhayes/BLANT/releases",
                    "type": "Source code",
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                    "version": null
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            "documentation": [
                {
                    "url": "https://github.com/waynebhayes/BLANT/blob/master/README.md",
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                        "General"
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                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btz603",
                    "pmid": "31373347",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BLANT - Fast graphlet sampling tool",
                        "abstract": "BLAST creates local sequence alignments by first building a database of small k-letter sub-sequences called k-mers. Identical k-mers from different regions provide 'seeds' for longer local alignments. This seed-and-extend heuristic makes BLAST extremely fast and has led to its almost exclusive use despite the existence of more accurate, but slower, algorithms. In this paper, we introduce the Basic Local Alignment for Networks Tool (BLANT). BLANT is the analog of BLAST, but for networks: given an input graph, it samples small, induced, k-node sub-graphs called k-graphlets. Graphlets have been used to classify networks, quantify structure, align networks both locally and globally, identify topology-function relationships and build taxonomic trees without the use of sequences. Given an input network, BLANT produces millions of graphlet samples in seconds - orders of magnitude faster than existing methods. BLANT offers sampled graphlets in various forms: distributions of graphlets or their orbits; graphlet degree or graphlet orbit degree vectors, the latter being compatible with ORCA; or an index to be used as the basis for seed-and-extend local alignments. We demonstrate BLANT's usefelness by using its indexing mode to find functional similarity between yeast and human PPI networks.",
                        "date": "2019-12-15T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Maharaj S."
                            },
                            {
                                "name": "Tracy B."
                            },
                            {
                                "name": "Hayes W.B."
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                        ],
                        "journal": "Bioinformatics"
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                }
            ],
            "credit": [
                {
                    "name": "Wayne B. Hayes",
                    "email": "whayes@uci.edu",
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        {
            "name": "OMBlast",
            "description": "Alignment tool for optical mapping using a seed-and-extend approach.",
            "homepage": "https://github.com/TF-Chan-Lab/OMBlast",
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                {
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                            "uri": "http://edamontology.org/operation_0491",
                            "term": "Pairwise sequence alignment"
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                        {
                            "uri": "http://edamontology.org/operation_3798",
                            "term": "Read binning"
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                        {
                            "uri": "http://edamontology.org/operation_3216",
                            "term": "Scaffolding"
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                    ],
                    "input": [],
                    "output": [],
                    "note": null,
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            ],
            "toolType": [
                "Command-line tool"
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                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
                },
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
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            "link": [
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                {
                    "url": "https://github.com/TF-Chan-Lab/OMBlast/blob/master/OMBlastManual.pdf",
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                {
                    "url": "https://github.com/TF-Chan-Lab/OMBlast/blob/master/README.md",
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btw620",
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                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "OMBlast: Alignment tool for optical mapping using a seed-and-extend approach",
                        "abstract": "Motivation: Optical mapping is a technique for capturing fluorescent signal patterns of long DNA molecules (in the range of 0.1–1 Mbp). Recently, it has been complementing the widely used short-read sequencing technology by assisting with scaffolding and detecting large and complex structural variations (SVs). Here, we introduce a fast, robust and accurate tool called OMBlast for aligning optical maps, the set of signal locations on the molecules generated from optical mapping. Our method is based on the seed-and-extend approach from sequence alignment, with modifications specific to optical mapping. Results: Experiments with both synthetic and our real data demonstrate that OMBlast has higher accuracy and faster mapping speed than existing alignment methods. Our tool also shows significant improvement when aligning data with SVs. Availability and Implementation: OMBlast is implemented for Java 1.7 and is released under a GPL license. OMBlast can be downloaded from https://github.com/aldenleung/OMBlast and run directly on machines equipped with a Java virtual machine.",
                        "date": "2017-01-01T00:00:00Z",
                        "citationCount": 25,
                        "authors": [
                            {
                                "name": "Leung A.K.-Y."
                            },
                            {
                                "name": "Kwok T.-P."
                            },
                            {
                                "name": "Wan R."
                            },
                            {
                                "name": "Xiao M."
                            },
                            {
                                "name": "Kwok P.-Y."
                            },
                            {
                                "name": "Yip K.Y."
                            },
                            {
                                "name": "Chan T.-F."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Kevin Y. Yip",
                    "email": "kevinyip@cse.cuhk.edu.hk",
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                    "name": "Ting-Fung Chan",
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        {
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                            "uri": "http://edamontology.org/operation_0310",
                            "term": "Sequence assembly"
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2977",
                                "term": "Nucleic acid sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                            ]
                        }
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
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                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "note": "Assemble proteins from paired-end reads",
                    "cmd": "plass assemble reads_1.fastq.gz reads_2.fastq.gz assembly.fas tmp"
                }
            ],
            "toolType": [
                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                },
                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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            ],
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            "download": [
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                    "url": "https://mmseqs.com/plass/plass-static_sse41.tar.gz",
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                    "version": "Latest git static build for linux"
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                {
                    "url": "https://mmseqs.com/plass/plass-osx-sse41.tar.gz",
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                    "version": "Latest git static build for macOS"
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                {
                    "url": "https://hub.docker.com/r/soedinglab/plass",
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            "publication": [
                {
                    "doi": "10.1038/s41592-019-0437-4",
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                    "note": null,
                    "metadata": {
                        "title": "Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold",
                        "abstract": "The open-source de novo protein-level assembler, Plass (https://plass.mmseqs.com), assembles six-frame-translated sequencing reads into protein sequences. It recovers 2–10 times more protein sequences from complex metagenomes and can assemble huge datasets. We assembled two redundancy-filtered reference protein catalogs, 2 billion sequences from 640 soil samples (soil reference protein catalog) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (marine eukaryotic reference catalog), the largest free collections of protein sequences.",
                        "date": "2019-07-01T00:00:00Z",
                        "citationCount": 222,
                        "authors": [
                            {
                                "name": "Steinegger M."
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                            {
                                "name": "Mirdita M."
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                            {
                                "name": "Soding J."
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                        "journal": "Nature Methods"
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                    "type": [
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                    "name": "Martin Steinegger",
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                    "orcidid": "http://orcid.org/0000-0001-8637-6719",
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                    "name": "Johannes Söding",
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                    "orcidid": "http://orcid.org/0000-0001-9642-8244",
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        {
            "name": "BJASS",
            "description": "A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates | Liu, Y., Chen, X., & Li, G. (2019). A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates. Statistical methods in medical research, 0962280219864710 | This program implements the BJASS joint screening method for DLBCL data (stored in matlab.mat) described in Section 4 of the paper",
            "homepage": "https://github.com/yiucla/BJASS",
            "biotoolsID": "BJASS",
            "biotoolsCURIE": "biotools:BJASS",
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                {
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                            "term": "Enrichment analysis"
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                        {
                            "uri": "http://edamontology.org/operation_3557",
                            "term": "Imputation"
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                            "uri": "http://edamontology.org/operation_3659",
                            "term": "Regression analysis"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates",
                        "abstract": "In an ultra-high dimensional setting with a huge number of covariates, variable screening is useful for dimension reduction before applying a more refined method for model selection and statistical analysis. This paper proposes a new sure joint screening procedure for right-censored time-to-event data based on a sparsity-restricted semiparametric accelerated failure time model. Our method, referred to as Buckley-James assisted sure screening (BJASS), consists of an initial screening step using a sparsity-restricted least-squares estimate based on a synthetic time variable and a refinement screening step using a sparsity-restricted least-squares estimate with the Buckley-James imputed event times. The refinement step may be repeated several times to obtain more stable results. We show that with any fixed number of refinement steps, the BJASS procedure retains all important variables with probability tending to 1. Simulation results are presented to illustrate its performance in comparison with some marginal screening methods. Real data examples are provided using a diffuse large-B-cell lymphoma (DLBCL) data and a breast cancer data. We have implemented the BJASS method using Matlab and made it available to readers through Github https://github.com/yiucla/BJASS.",
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                        "authors": [
                            {
                                "name": "Liu Y."
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                            {
                                "name": "Chen X."
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                            {
                                "name": "Li G."
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                        "journal": "Statistical Methods in Medical Research"
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        {
            "name": "BlastFrost",
            "description": "BlastFrost is a highly efficient method for querying 100,000s of genome assemblies. BlastFrost builds on the recently developed Bifrost, which generates a dynamic data structure for compacted and colored de Bruijn graphs from bacterial genomes. BlastFrost queries a Bifrost data structure for sequences of interest, and extracts local subgraphs, thereby enabling the efficient identification of the presence or absence of individual genes or single nucleotide sequence variants.",
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                            "uri": "http://edamontology.org/operation_0224",
                            "term": "Query and retrieval"
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                            "uri": "http://edamontology.org/operation_0525",
                            "term": "Genome assembly"
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                            "uri": "http://edamontology.org/operation_3227",
                            "term": "Variant calling"
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                        {
                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
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                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
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                    "term": "Sequence assembly"
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                    "metadata": {
                        "title": "BlastFrost: fast querying of 100,000s of bacterial genomes in Bifrost graphs",
                        "abstract": "BlastFrost is a highly efficient method for querying 100,000s of genome assemblies, building on Bifrost, a dynamic data structure for compacted and colored de Bruijn graphs. BlastFrost queries a Bifrost data structure for sequences of interest and extracts local subgraphs, enabling the identification of the presence or absence of individual genes or single nucleotide sequence variants. We show two examples using Salmonella genomes: finding within minutes the presence of genes in the SPI-2 pathogenicity island in a collection of 926 genomes and identifying single nucleotide polymorphisms associated with fluoroquinolone resistance in three genes among 190,209 genomes. BlastFrost is available at https://github.com/nluhmann/BlastFrost/tree/master/data.",
                        "date": "2021-12-01T00:00:00Z",
                        "citationCount": 13,
                        "authors": [
                            {
                                "name": "Luhmann N."
                            },
                            {
                                "name": "Holley G."
                            },
                            {
                                "name": "Achtman M."
                            }
                        ],
                        "journal": "Genome Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Nina Luhmann",
                    "email": "N.Luhmann@warwick.ac.uk",
                    "url": null,
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        {
            "name": "BLAT",
            "description": "Fast, accurate spliced alignment of DNA sequences.",
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                {
                    "uri": "http://edamontology.org/topic_0080",
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                    "url": "http://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html#BLATAlign",
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            "publication": [
                {
                    "doi": "10.1101/gr.229202",
                    "pmid": "11932250",
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BLAT - The BLAST-like alignment tool",
                        "abstract": "Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT's speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome.",
                        "date": "2002-01-01T00:00:00Z",
                        "citationCount": 6684,
                        "authors": [
                            {
                                "name": "Kent W.J."
                            }
                        ],
                        "journal": "Genome Research"
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                }
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                    "name": "Server and website access problems",
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        },
        {
            "name": "LAST",
            "description": "Short read alignment program incorporating quality scores",
            "homepage": "http://last.cbrc.jp/",
            "biotoolsID": "last",
            "biotoolsCURIE": "biotools:last",
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            ],
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0292",
                            "term": "Sequence alignment"
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            ],
            "toolType": [
                "Web service"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0797",
                    "term": "Comparative genomics"
                }
            ],
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                "Windows",
                "Mac"
            ],
            "language": [
                "C++"
            ],
            "license": "GPL-3.0",
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            "maturity": "Mature",
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                    "url": "http://last.cbrc.jp/doc/",
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                        "General"
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                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkq010",
                    "pmid": "20110255",
                    "pmcid": "PMC2853142",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Incorporating sequence quality data into alignment improves DNA read mapping",
                        "abstract": "New DNA sequencing technologies have achieved breakthroughs in throughput, at the expense of higher error rates. The primary way of interpreting biological sequences is via alignment, but standard alignment methods assume the sequences are accurate. Here, we describe how to incorporate the per-base error probabilities reported by sequencers into alignment. Unlike existing tools for DNA read mapping, our method models both sequencer errors and real sequence differences. This approach consistently improves mapping accuracy, even when the rate of real sequence difference is only 0.2%. Furthermore, when mapping Drosophila melanogaster reads to the Drosophila simulans genome, it increased the amount of correctly mapped reads from 49 to 66%. This approach enables more effective use of DNA reads from organisms that lack reference genomes, are extinct or are highly polymorphic. © The Author(s) 2010. Published by Oxford University Press.",
                        "date": "2010-01-27T00:00:00Z",
                        "citationCount": 63,
                        "authors": [
                            {
                                "name": "Frith M.C."
                            },
                            {
                                "name": "Wan R."
                            },
                            {
                                "name": "Horton P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "LAST Support",
                    "email": "last-align@googlegroups.com",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "note": null
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            ],
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        },
        {
            "name": "VAAST",
            "description": "Variant Annotation, Analysis and Search Tool",
            "homepage": "http://www.yandell-lab.org/software/vaast.html",
            "biotoolsID": "vaast",
            "biotoolsCURIE": "biotools:vaast",
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            "otherID": [
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                    "value": "RRID:SCR_002179",
                    "type": "rrid",
                    "version": null
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                        {
                            "uri": "http://edamontology.org/operation_3226",
                            "term": "Variant prioritisation"
                        }
                    ],
                    "input": [],
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                    "note": null,
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            ],
            "toolType": [
                "Suite"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "DNA structural variation"
                }
            ],
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                {
                    "url": "http://www.yandell-lab.org/software/VAAST/VAAST_Users_Guide.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1016/j.ajhg.2011.05.017",
                    "pmid": "21700266",
                    "pmcid": "PMC3135802",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Using VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency",
                        "abstract": "We have identified two families with a previously undescribed lethal X-linked disorder of infancy; the disorder comprises a distinct combination of an aged appearance, craniofacial anomalies, hypotonia, global developmental delays, cryptorchidism, and cardiac arrhythmias. Using X chromosome exon sequencing and a recently developed probabilistic algorithm aimed at discovering diseasecausing variants, we identified in one family a c.109T>C (p.Ser37Pro) variant in NAA10, a gene encoding the catalytic subunit of the major human N-terminal acetyltransferase (NAT). A parallel effort on a second unrelated family converged on the same variant. The absence of this variant in controls, the amino acid conservation of this region of the protein, the predicted disruptive change, and the co-occurrence in two unrelated families with the same rare disorder suggest that this is the pathogenic mutation. We confirmed this by demonstrating a significantly impaired biochemical activity of the mutant hNaa10p, and from this we conclude that a reduction in acetylation by hNaa10p causes this disease. Here we provide evidence of a human genetic disorder resulting from direct impairment of N-terminal acetylation, one of the most common protein modifications in humans. © 2011 by The American Society of Human Genetics. All rights reserved.",
                        "date": "2011-07-15T00:00:00Z",
                        "citationCount": 190,
                        "authors": [
                            {
                                "name": "Alan F. Rope"
                            },
                            {
                                "name": "Wang K."
                            },
                            {
                                "name": "Evjenth R."
                            },
                            {
                                "name": "Jinchuan Xing"
                            },
                            {
                                "name": "Johnston J.J."
                            },
                            {
                                "name": "Swensen J.J."
                            },
                            {
                                "name": "Johnson W.E."
                            },
                            {
                                "name": "Moore B."
                            },
                            {
                                "name": "Huff C.D."
                            },
                            {
                                "name": "Bird L.M."
                            },
                            {
                                "name": "Carey J.C."
                            },
                            {
                                "name": "Opitz J.M."
                            },
                            {
                                "name": "Stevens C.A."
                            },
                            {
                                "name": "Jiang T."
                            },
                            {
                                "name": "Schank C."
                            },
                            {
                                "name": "Fain H.D."
                            },
                            {
                                "name": "Robison R."
                            },
                            {
                                "name": "Dalley B."
                            },
                            {
                                "name": "Chin S."
                            },
                            {
                                "name": "South S.T."
                            },
                            {
                                "name": "Theodore J. Pysher"
                            },
                            {
                                "name": "Jorde L.B."
                            },
                            {
                                "name": "Hakonarson H."
                            },
                            {
                                "name": "Lillehaug J.R."
                            },
                            {
                                "name": "Biesecker L.G."
                            },
                            {
                                "name": "Yandell M."
                            },
                            {
                                "name": "Arnesen T."
                            },
                            {
                                "name": "Lyon G.J."
                            }
                        ],
                        "journal": "American Journal of Human Genetics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Mark Yandell",
                    "email": "myandell@genetics.utah.edu",
                    "url": null,
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        },
        {
            "name": "PZLAST",
            "description": "An ultra-fast amino acid sequence similarity search server against public metagenomes.",
            "homepage": "https://pzlast.riken.jp/meta",
            "biotoolsID": "pzlast",
            "biotoolsCURIE": "biotools:pzlast",
            "version": [],
            "otherID": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0346",
                            "term": "Sequence similarity search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0310",
                            "term": "Sequence assembly"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2495",
                            "term": "Expression analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3837",
                    "term": "Metagenomic sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "download": [],
            "documentation": [
                {
                    "url": "https://pzlast.riken.jp/meta/api",
                    "type": [
                        "API documentation"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btab492",
                    "pmid": "34240105",
                    "pmcid": "PMC8570820",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PZLAST: an ultra-fast amino acid sequence similarity search server against public metagenomes",
                        "abstract": "Summary:: Similarity searches of amino acid sequences against the public metagenomic data can provide users insights about the function of sequences based on the environmental distribution of similar sequences. However, a considerable reduction in the amount of data or the accuracy of the result was necessary to conduct sequence similarity searches against public metagenomic data, because of the vast data size more than Terabytes. Here, we present an ultra-fast service for the highly accurate amino acid sequence similarity search, called PZLAST, which can search the user's amino acid sequences to several Terabytes of public metagenomic sequences in ∼10-20 min. PZLAST accomplishes its search speed by using PEZY-SC2, which is a Multiple Instruction Multiple Data many-core processor. Results of PZLAST are summarized by the ontology-based environmental distribution of similar sequences. PZLAST can be used to predict the function of sequences and mine for homologs of functionally important gene sequences.",
                        "date": "2021-11-01T00:00:00Z",
                        "citationCount": 4,
                        "authors": [
                            {
                                "name": "Mori H."
                            },
                            {
                                "name": "Ishikawa H."
                            },
                            {
                                "name": "Higashi K."
                            },
                            {
                                "name": "Kato Y."
                            },
                            {
                                "name": "Ebisuzaki T."
                            },
                            {
                                "name": "Kurokawa K."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Hiroshi Mori",
                    "email": "hmori@nig.ac.jp",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-0806-7704",
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Ken Kurokawa",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-8662-9958",
                    "gridid": null,
                    "rorid": null,
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                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Toshikazu Ebisuzaki",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-3918-1166",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
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                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Ken Kurokawa",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-8662-9958",
                    "gridid": null,
                    "rorid": null,
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                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Hitoshi Ishikawa",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "rorid": null,
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                    "typeRole": [],
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                }
            ],
            "owner": "Jennifer",
            "additionDate": "2021-11-22T22:55:18.983573Z",
            "lastUpdate": "2024-11-24T20:16:05.736551Z",
            "editPermission": {
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            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "name": "BEAST",
            "description": "The Bayesian Evolutionary Analysis Sampling Trees is a cross-platform program for Bayesian analysis of molecular sequences using MCMC (Markov chain Monte Carlo). It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology.",
            "homepage": "http://beast.bio.ed.ac.uk/",
            "biotoolsID": "beast",
            "biotoolsCURIE": "biotools:beast",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0492",
                            "term": "Multiple sequence alignment"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0540",
                            "term": "Phylogenetic tree generation (from molecular sequences)"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0547",
                            "term": "Phylogenetic tree generation (maximum likelihood and Bayesian methods)"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3293",
                    "term": "Phylogenetics"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Java"
            ],
            "license": null,
            "collectionID": [
                "EBI Training Tools"
            ],
            "maturity": null,
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            "elixirPlatform": [],
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            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://beast.community/first_tutorial",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/molbev/mss075",
                    "pmid": "22367748",
                    "pmcid": "PMC3408070",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Bayesian phylogenetics with BEAUti and the BEAST 1.7",
                        "abstract": "Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk. © The Author 2012. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.",
                        "date": "2012-08-01T00:00:00Z",
                        "citationCount": 8490,
                        "authors": [
                            {
                                "name": "Drummond A.J."
                            },
                            {
                                "name": "Suchard M.A."
                            },
                            {
                                "name": "Xie D."
                            },
                            {
                                "name": "Rambaut A."
                            }
                        ],
                        "journal": "Molecular Biology and Evolution"
                    }
                },
                {
                    "doi": "10.1093/ve/vey016",
                    "pmid": "29942656",
                    "pmcid": "PMC6007674",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": null
                },
                {
                    "doi": "10.1186/1471-2148-7-214",
                    "pmid": "17996036",
                    "pmcid": "PMC2247476",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BEAST: Bayesian evolutionary analysis by sampling trees",
                        "abstract": "Background. The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results. BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. Conclusion. BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis. © 2007 Drummond and Rambaut; licensee BioMed Central Ltd.",
                        "date": "2007-12-01T00:00:00Z",
                        "citationCount": 10833,
                        "authors": [
                            {
                                "name": "Drummond A.J."
                            },
                            {
                                "name": "Rambaut A."
                            }
                        ],
                        "journal": "BMC Evolutionary Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": null,
                    "email": null,
                    "url": "http://groups.google.com/group/beast-users",
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "owner": "ebi_training_import",
            "additionDate": "2017-01-17T15:07:46Z",
            "lastUpdate": "2024-11-24T20:07:46.590581Z",
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                ]
            },
            "validated": 1,
            "homepage_status": 0,
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            "confidence_flag": null
        },
        {
            "name": "Blast2GO",
            "description": "Bioinformatics platform for high-quality functional annotation and analysis of genomic datasets. It allows analyzing and visualizing newly sequenced genomes by combining state-of-the-art methodologies, standard resources and algorithms. It allows to gain biological insights fast and easy even for completely novel genomes. Perform out-of-the-box the entire workflow of functional annotation of your transcriptomic datasets including its analysis and biological interpretation.",
            "homepage": "https://www.blast2go.com/",
            "biotoolsID": "blast2go",
            "biotoolsCURIE": "biotools:blast2go",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3672",
                            "term": "Gene functional annotation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1383",
                                "term": "Sequence alignment (nucleic acid)"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3753",
                                "term": "Over-representation data"
                            },
                            "format": []
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3511",
                    "term": "Nucleic acid sites, features and motifs"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": null,
            "collectionID": [
                "BLAST"
            ],
            "maturity": null,
            "cost": null,
            "accessibility": null,
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "https://www.blast2go.com/blast2go-pro",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/bti610",
                    "pmid": "16081474",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research",
                        "abstract": "Summary: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. © The Author 2005. Published by Oxford University Press. All rights reserved.",
                        "date": "2005-09-15T00:00:00Z",
                        "citationCount": 9378,
                        "authors": [
                            {
                                "name": "Conesa A."
                            },
                            {
                                "name": "Gotz S."
                            },
                            {
                                "name": "Garcia-Gomez J.M."
                            },
                            {
                                "name": "Terol J."
                            },
                            {
                                "name": "Talon M."
                            },
                            {
                                "name": "Robles M."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkn176",
                    "pmid": "18445632",
                    "pmcid": "PMC2425479",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "High-throughput functional annotation and data mining with the Blast2GO suite",
                        "abstract": "Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data. © 2008 The Author(s).",
                        "date": "2008-06-01T00:00:00Z",
                        "citationCount": 3322,
                        "authors": [
                            {
                                "name": "Gotz S."
                            },
                            {
                                "name": "Garcia-Gomez J.M."
                            },
                            {
                                "name": "Terol J."
                            },
                            {
                                "name": "Williams T.D."
                            },
                            {
                                "name": "Nagaraj S.H."
                            },
                            {
                                "name": "Nueda M.J."
                            },
                            {
                                "name": "Robles M."
                            },
                            {
                                "name": "Talon M."
                            },
                            {
                                "name": "Dopazo J."
                            },
                            {
                                "name": "Conesa A."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": null,
                    "email": "contact@biobam.com",
                    "url": "https://www.biobam.com/biobam-contact/",
                    "orcidid": null,
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "owner": "ELIXIR-EE",
            "additionDate": "2017-03-01T15:34:10Z",
            "lastUpdate": "2024-11-24T15:59:58.360493Z",
            "editPermission": {
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                "authors": [
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                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "BLAST",
            "description": "A tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.",
            "homepage": "https://blast.ncbi.nlm.nih.gov/Blast.cgi",
            "biotoolsID": "blast",
            "biotoolsCURIE": "biotools:blast",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0346",
                            "term": "Sequence similarity search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0292",
                            "term": "Sequence alignment"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3331",
                                    "term": "BLAST XML results format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3836",
                                    "term": "BLAST XML v2 results format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3475",
                                    "term": "TSV"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3752",
                                    "term": "CSV"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1966",
                                    "term": "ASN.1 sequence format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2333",
                                    "term": "Binary format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application",
                "Web API"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": null,
            "collectionID": [
                "BLAST"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=References",
                    "type": [
                        "Citation instructions"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1016/S0022-2836(05)80360-2",
                    "pmid": "2231712",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Basic local alignment search tool",
                        "abstract": "A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straight-forward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity. © 1990, Academic Press Limited. All rights reserved.",
                        "date": "1990-01-01T00:00:00Z",
                        "citationCount": 77781,
                        "authors": [
                            {
                                "name": "Altschul S.F."
                            },
                            {
                                "name": "Gish W."
                            },
                            {
                                "name": "Miller W."
                            },
                            {
                                "name": "Myers E.W."
                            },
                            {
                                "name": "Lipman D.J."
                            }
                        ],
                        "journal": "Journal of Molecular Biology"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btw305",
                    "pmid": "27256311",
                    "pmcid": "PMC5013910",
                    "type": [
                        "Benchmarking study"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Benchmarking the next generation of homology inference tools",
                        "abstract": "Motivation: Over the last decades, vast numbers of sequences were deposited in public databases. Bioinformatics tools allow homology and consequently functional inference for these sequences. New profile-based homology search tools have been introduced, allowing reliable detection of remote homologs, but have not been systematically benchmarked. To provide such a comparison, which can guide bioinformatics workflows, we extend and apply our previously developed benchmark approach to evaluate the 'next generation' of profile-based approaches, including CS-BLAST, HHSEARCH and PHMMER, in comparison with the non-profile based search tools NCBI-BLAST, USEARCH, UBLAST and FASTA. Method: We generated challenging benchmark datasets based on protein domain architectures within either the PFAM + Clan, SCOP/Superfamily or CATH/Gene3D domain definition schemes. From each dataset, homologous and non-homologous protein pairs were aligned using each tool, and standard performance metrics calculated. We further measured congruence of domain architecture assignments in the three domain databases. Results: CSBLAST and PHMMER had overall highest accuracy. FASTA, UBLAST and USEARCH showed large trade-offs of accuracy for speed optimization. Conclusion: Profile methods are superior at inferring remote homologs but the difference in accuracy between methods is relatively small. PHMMER and CSBLAST stand out with the highest accuracy, yet still at a reasonable computational cost. Additionally, we show that less than 0.1% of Swiss-Prot protein pairs considered homologous by one database are considered non-homologous by another, implying that these classifications represent equivalent underlying biological phenomena, differing mostly in coverage and granularity. Availability and Implementation: Benchmark datasets and all scripts are placed at (http://sonnhammer.org/download/Homology-benchmark).",
                        "date": "2016-09-01T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Saripella G.V."
                            },
                            {
                                "name": "Sonnhammer E.L.L."
                            },
                            {
                                "name": "Forslund K."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkn201",
                    "pmid": "18440982",
                    "pmcid": "PMC2447716",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "NCBI BLAST: a better web interface.",
                        "abstract": "Basic Local Alignment Search Tool (BLAST) is a sequence similarity search program. The public interface of BLAST, http://www.ncbi.nlm.nih.gov/blast, at the NCBI website has recently been reengineered to improve usability and performance. Key new features include simplified search forms, improved navigation, a list of recent BLAST results, saved search strategies and a documentation directory. Here, we describe the BLAST web application's new features, explain design decisions and outline plans for future improvement.",
                        "date": "2008-01-01T00:00:00Z",
                        "citationCount": 2950,
                        "authors": [
                            {
                                "name": "Johnson M."
                            },
                            {
                                "name": "Zaretskaya I."
                            },
                            {
                                "name": "Raytselis Y."
                            },
                            {
                                "name": "Merezhuk Y."
                            },
                            {
                                "name": "McGinnis S."
                            },
                            {
                                "name": "Madden T.L."
                            }
                        ],
                        "journal": "Nucleic acids research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkt282",
                    "pmid": "23609542",
                    "pmcid": "PMC3692093",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BLAST: a more efficient report with usability improvements.",
                        "abstract": "The Basic Local Alignment Search Tool (BLAST) website at the National Center for Biotechnology (NCBI) is an important resource for searching and aligning sequences. A new BLAST report allows faster loading of alignments, adds navigation aids, allows easy downloading of subject sequences and reports and has improved usability. Here, we describe these improvements to the BLAST report, discuss design decisions, describe other improvements to the search page and database documentation and outline plans for future development. The NCBI BLAST URL is http://blast.ncbi.nlm.nih.gov.",
                        "date": "2013-01-01T00:00:00Z",
                        "citationCount": 887,
                        "authors": [
                            {
                                "name": "Boratyn G.M."
                            },
                            {
                                "name": "Camacho C."
                            },
                            {
                                "name": "Cooper P.S."
                            },
                            {
                                "name": "Coulouris G."
                            },
                            {
                                "name": "Fong A."
                            },
                            {
                                "name": "Ma N."
                            },
                            {
                                "name": "Madden T.L."
                            },
                            {
                                "name": "Matten W.T."
                            },
                            {
                                "name": "McGinnis S.D."
                            },
                            {
                                "name": "Merezhuk Y."
                            },
                            {
                                "name": "Raytselis Y."
                            },
                            {
                                "name": "Sayers E.W."
                            },
                            {
                                "name": "Tao T."
                            },
                            {
                                "name": "Ye J."
                            },
                            {
                                "name": "Zaretskaya I."
                            }
                        ],
                        "journal": "Nucleic acids research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "BLAST Support",
                    "email": "blast-help@ncbi.nlm.nih.gov",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "owner": "seqwiki_import",
            "additionDate": "2017-01-13T13:13:50Z",
            "lastUpdate": "2024-11-24T15:55:00.707215Z",
            "editPermission": {
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                    "ELIXIR-EE",
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                ]
            },
            "validated": 1,
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            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "BLAZE",
            "description": "Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE.",
            "homepage": "https://github.com/shimlab/BLAZE",
            "biotoolsID": "blaze",
            "biotoolsCURIE": "biotools:blaze",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3933",
                            "term": "Demultiplexing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3799",
                            "term": "Quantification"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3185",
                            "term": "Base-calling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3200",
                            "term": "DNA barcoding"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3495",
                                "term": "RNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3752",
                                    "term": "CSV"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Script"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_2229",
                    "term": "Cell biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0099",
                    "term": "RNA"
                },
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Python"
            ],
            "license": "GPL-3.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
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            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1186/s13059-023-02907-y",
                    "pmid": "37024980",
                    "pmcid": "PMC10077662",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE",
                        "abstract": "Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "You Y."
                            },
                            {
                                "name": "Prawer Y.D.J."
                            },
                            {
                                "name": "De Paoli-Iseppi R."
                            },
                            {
                                "name": "Hunt C.P.J."
                            },
                            {
                                "name": "Parish C.L."
                            },
                            {
                                "name": "Shim H."
                            },
                            {
                                "name": "Clark M.B."
                            }
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                        "journal": "Genome Biology"
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                    "name": "Heejung Shim",
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                    "name": "Michael B. Clark",
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                    "uri": "http://edamontology.org/topic_3954",
                    "term": "Echography"
                },
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                    "uri": "http://edamontology.org/topic_3444",
                    "term": "MRI"
                },
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                    "uri": "http://edamontology.org/topic_3384",
                    "term": "Medical imaging"
                }
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                "Linux",
                "Windows"
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            "link": [
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            "publication": [
                {
                    "doi": "10.1016/j.cmpb.2023.107604",
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                    "metadata": {
                        "title": "FLUST: A fast, open source framework for ultrasound blood flow simulations",
                        "abstract": "Background and objective:Ultrasound based blood velocity estimation is a continuously developing frontier, where the vast number of possible acquisition setups and velocity estimators makes it challenging to assess which combination is better suited for a given imaging application. FLUST, the Flow-Line based Ultrasound Simulation Tool, may be used to address this challenge, providing a common platform for evaluation of velocity estimation schemes on in silico data. However, the FLUST approach had some limitations in its original form, including reduced robustness for phase sensitive setups and the need for manual selection of integrity parameters. In addition, implementation of the technique and therefore also documentation of signal integrity was left to potential users of the approach. Methods: In this work, several improvements to the FLUST technique are proposed and investigated, and a robust, open source simulation framework developed. The software supports several transducer types and acquisition setups, in addition to a range of different flow phantoms. The main goal of this work is to offer a robust, computationally cheap and user-friendly framework to simulate ultrasound data from stationary blood velocity fields and thereby facilitate design and evaluation of estimation schemes, including acquisition design, velocity estimation and other post-processing steps. Results: The technical improvements proposed in this work resulted in reduced interpolation errors, reduced variability in signal power, and also automatic selection of spatial and temporal discretization parameters. Results are presented illustrating the challenges and the effectiveness of the solutions. The integrity of the improved simulation framework is validated in an extensive study, with results indicating that speckle statistics, spatial and temporal correlation and frequency content all correspond well with theoretical predictions. Finally, an illustrative example shows how FLUST may be used throughout the design and optimization process of a velocity estimator. Conclusions: The FLUST framework is available as a part of the UltraSound ToolBox (USTB), and the results in this paper demonstrate that it can be used as an efficient and reliable tool for the development and validation of ultrasound-based velocity estimation schemes.",
                        "date": "2023-08-01T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Ekroll I.K."
                            },
                            {
                                "name": "Saris A.E.C.M."
                            },
                            {
                                "name": "Avdal J."
                            }
                        ],
                        "journal": "Computer Methods and Programs in Biomedicine"
                    }
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                {
                    "name": "Ingvild Kinn Ekroll",
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        {
            "name": "Maast",
            "description": "Genotyping thousands of microbial strains. Type SNPs from a set of whole genome assemblies and sequencing reads from beginning to end in one single command line.",
            "homepage": "https://github.com/zjshi/Maast",
            "biotoolsID": "maast",
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                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
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                            "uri": "http://edamontology.org/operation_0484",
                            "term": "SNP detection"
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                            "uri": "http://edamontology.org/operation_0525",
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                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                },
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                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
                },
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                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
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                    "term": "Sequence assembly"
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                    "doi": "10.1186/s13059-023-03030-8",
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                    "metadata": {
                        "title": "Maast: genotyping thousands of microbial strains efficiently",
                        "abstract": "Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast’s utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Shi Z.J."
                            },
                            {
                                "name": "Nayfach S."
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                            {
                                "name": "Pollard K.S."
                            }
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                        "journal": "Genome Biology"
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        {
            "name": "BLAST (EBI)",
            "description": "Find regions of sequence similarity and alignments between a query sequence and database sequences.",
            "homepage": "https://www.ebi.ac.uk/jdispatcher/sss/ncbiblast",
            "biotoolsID": "blast_ebi",
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                            "uri": "http://edamontology.org/operation_0346",
                            "term": "Sequence similarity search"
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                            "term": "Sequence alignment"
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                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
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                        }
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
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                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web API",
                "Web application",
                "Web service"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                }
            ],
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            ],
            "download": [
                {
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                    "type": "Downloads page",
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                }
            ],
            "documentation": [
                {
                    "url": "http://www.ebi.ac.uk/about/terms-of-use",
                    "type": [
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                },
                {
                    "url": "https://www.ebi.ac.uk/jdispatcher/help",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://blast.ncbi.nlm.nih.gov/Blast.cgi",
                    "type": [
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            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-10-421",
                    "pmid": "20003500",
                    "pmcid": "PMC2803857",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BLAST+: Architecture and applications",
                        "abstract": "Background: Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications.Results: We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site.Conclusion: The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications. © 2009 Camacho et al; licensee BioMed Central Ltd.",
                        "date": "2009-12-15T00:00:00Z",
                        "citationCount": 12681,
                        "authors": [
                            {
                                "name": "Camacho C."
                            },
                            {
                                "name": "Coulouris G."
                            },
                            {
                                "name": "Avagyan V."
                            },
                            {
                                "name": "Ma N."
                            },
                            {
                                "name": "Papadopoulos J."
                            },
                            {
                                "name": "Bealer K."
                            },
                            {
                                "name": "Madden T.L."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkae241",
                    "pmid": "38597606",
                    "pmcid": "PMC11223882",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024",
                        "abstract": "The EMBL-EBI Job Dispatcher sequence analysis tools framework (https://www.ebi.ac.uk/jdispatcher) enables the scientific community to perform a diverse range of sequence analyses using popular bioinformatics applications. Free access to the tools and required sequence datasets is provided through user-friendly web applications, as well as via RESTful and SOAP-based APIs. These are integrated into popular EMBL-EBI resources such as UniProt, InterPro, ENA and Ensembl Genomes. This paper overviews recent improvements to Job Dispatcher, including its brand new website and documentation, enhanced visualisations, improved job management, and a rising trend of user reliance on the service from low- and middle-income regions.",
                        "date": "2024-07-05T00:00:00Z",
                        "citationCount": 60,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Eusebi A."
                            },
                            {
                                "name": "Niewielska A."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Lopez R."
                            },
                            {
                                "name": "Butcher S."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkac240",
                    "pmid": "35412617",
                    "pmcid": "PMC9252731",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
                        "date": "2022-07-05T00:00:00Z",
                        "citationCount": 1248,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Basutkar P."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Edbali O."
                            },
                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "EMBL-EBI",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
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                    ],
                    "note": null
                },
                {
                    "name": "NCBI",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "typeEntity": "Institute",
                    "typeRole": [
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                    ],
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                },
                {
                    "name": "Job Dispatcher",
                    "email": null,
                    "url": "https://www.ebi.ac.uk/jdispatcher",
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                    "typeEntity": "Project",
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                }
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            "additionDate": "2015-01-29T15:47:52Z",
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                "authors": [
                    "biomadeira",
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        },
        {
            "name": "eTBLAST",
            "description": "Textual similarity search engine. This server can parse and summarize the results of an abstract similarity search to find appropriate journals for publication, authors with expertise in a given field, and documents similar to a submitted query.",
            "homepage": "http://etest.vbi.vt.edu/etblast3/",
            "biotoolsID": "etblast",
            "biotoolsCURIE": "biotools:etblast",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2409",
                            "term": "Data handling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0360",
                            "term": "Structural similarity search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0224",
                            "term": "Query and retrieval"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0218",
                    "term": "Natural language processing"
                },
                {
                    "uri": "http://edamontology.org/topic_3068",
                    "term": "Literature and language"
                }
            ],
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            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btl388",
                    "pmid": "16926219",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Text similarity: An alternative way to search MEDLINE",
                        "abstract": "Motivation: The most widely used literature search techniques, such as those offered by NCBI's PubMed system, require significant effort on the part of the searcher, and inexperienced searchers do not use these systems as effectively as experienced users. Improved literature search engines can save researchers time and effort by making it easier to locate the most important and relevant literature. Results: We have created and optimized a new, hybrid search system for Medline that takes natural text as input and then delivers results with high precision and recall. The combination of a fast, low-sensitivity weighted keyword-based first pass algorithm to cast a wide net to gather an initial set of literature, followed by a unique sentence-alignment based similarity algorithm to rank order those results was developed that is sensitive, fast and easy to use. Several text similarity search algorithms, both standard and novel, were implemented and tested in order to determine which obtained the best results in information retrieval exercises. © 2006 Oxford University Press.",
                        "date": "2006-09-15T00:00:00Z",
                        "citationCount": 80,
                        "authors": [
                            {
                                "name": "Lewis J."
                            },
                            {
                                "name": "Ossowski S."
                            },
                            {
                                "name": "Hicks J."
                            },
                            {
                                "name": "Errami M."
                            },
                            {
                                "name": "Garner H.R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkm221",
                    "pmid": "17452348",
                    "pmcid": "PMC1933238",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "ETBLAST: A web server to identify expert reviewers, appropriate journals and similar publications",
                        "abstract": "Authors, editors and reviewers alike use the biomedical literature to identify appropriate journals in which to publish, potential reviewers for papers or grants, and collaborators (or competitors) with similar interests. Traditionally, this process has either relied upon personal expertise and knowledge or upon a somewhat unsystematic and laborious process of manually searching through the literature for trends. To help with these tasks, we report three utilities that parse and summarize the results of an abstract similarity search to find appropriate journals for publication, authors with expertise in a given field, and documents similar to a submitted query. The utilities are based upon a program, eTBLAST, designed to identify similar documents within literature databases such as (but not limited to) MEDLINE. These services are freely accessible through the Internet at http://invention.swmed.edu/etblast/ etblast.shtml, where users can upload a file or paste text such as an abstract into the browser interface. © 2007 The Author(s).",
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                {
                    "url": "https://github.com/vtsynergy/iBLAST/issues",
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            "publication": [
                {
                    "doi": "10.1371/JOURNAL.PONE.0249410",
                    "pmid": "33886589",
                    "pmcid": "PMC8062096",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "iBLAST: Incremental BLAST of new sequences via automated e-value correction",
                        "abstract": "© 2021 Dash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Search results from local alignment search tools use statistical scores that are sensitive to the size of the database to report the quality of the result. For example, NCBI BLAST reports the best matches using similarity scores and expect values (i.e., e-values) calculated against the database size. Given the astronomical growth in genomics data throughout a genomic research investigation, sequence databases grow as new sequences are continuously being added to these databases. As a consequence, the results (e.g., best hits) and associated statistics (e.g., e-values) for a specific set of queries may change over the course of a genomic investigation. Thus, to update the results of a previously conducted BLAST search to find the best matches on an updated database, scientists must currently rerun the BLAST search against the entire updated database, which translates into irrecoverable and, in turn, wasted execution time, money, and computational resources. To address this issue, we devise a novel and efficient method to redeem past BLAST searches by introducing iBLAST. iBLAST leverages previous BLAST search results to conduct the same query search but only on the incremental (i.e., newly added) part of the database, recomputes the associated critical statistics such as e-values, and combines these results to produce updated search results. Our experimental results and fidelity analyses show that iBLAST delivers search results that are identical to NCBI BLAST at a substantially reduced computational cost, i.e., iBLAST performs (1 + δ)/δ times faster than NCBI BLAST, where δ represents the fraction of database growth. We then present three different use cases to demonstrate that iBLAST can enable efficient biological discovery at a much faster speed with a substantially reduced computational cost.",
                        "date": "2021-04-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Dash S."
                            },
                            {
                                "name": "Rahman S.R."
                            },
                            {
                                "name": "Hines H.M."
                            },
                            {
                                "name": "Feng W.-C."
                            }
                        ],
                        "journal": "PLoS ONE"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Sajal Dash",
                    "email": "dashs@ornl.gov",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-5308-914X",
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                },
                {
                    "name": "Wu-chun Feng",
                    "email": "feng@cs.vt.edu",
                    "url": null,
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        {
            "name": "BLAST-QC",
            "description": "BLAST-QC is a tool for automated analysis of BLAST results.  It is a quality control filter and parser for NCBI BLAST XML results.",
            "homepage": "https://github.com/torkian/blast-QC",
            "biotoolsID": "blast-qc",
            "biotoolsCURIE": "biotools:blast-qc",
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                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_1812",
                            "term": "Parsing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0495",
                            "term": "Local alignment"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3372",
                    "term": "Software engineering"
                }
            ],
            "operatingSystem": [],
            "language": [
                "C",
                "Java",
                "Python"
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            "license": "MIT",
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            ],
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            "publication": [
                {
                    "doi": "10.1186/S40793-020-00361-Y",
                    "pmid": "33902722",
                    "pmcid": "PMC8066848",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BLAST-QC: Automated analysis of BLAST results",
                        "abstract": "© 2020 The Author(s).Background: The Basic Local Alignment Search Tool (BLAST) from NCBI is the preferred utility for sequence alignment and identification for bioinformatics and genomics research. Among researchers using NCBI's BLAST software, it is well known that analyzing the results of a large BLAST search can be tedious and time-consuming. Furthermore, with the recent discussions over the effects of parameters such as '-max_target_seqs' on the BLAST heuristic search process, the use of these search options are questionable. This leaves using a stand-alone parser as one of the only options of condensing these large datasets, and with few available for download online, the task is left to the researcher to create a specialized piece of software anytime they need to analyze BLAST results. The need for a streamlined and fast script that solves these issues and can be easily implemented into a variety of bioinformatics and genomics workflows was the initial motivation for developing this software. Results: In this study, we demonstrate the effectiveness of BLAST-QC for analysis of BLAST results and its desirability over the other available options. Applying genetic sequence data from our bioinformatic workflows, we establish BLAST_QC's superior runtime when compared to existing parsers developed with commonly used BioPerl and BioPython modules, as well as C and Java implementations of the BLAST_QC program. We discuss the 'max_target_seqs' parameter, the usage of and controversy around the use of the parameter, and offer a solution by demonstrating the ability of our software to provide the functionality this parameter was assumed to produce, as well as a variety of other parsing options. Executions of the script on example datasets are given, demonstrating the implemented functionality and providing test-cases of the program. BLAST-QC is designed to be integrated into existing software, and we establish its effectiveness as a module of workflows or other processes. Conclusions: BLAST-QC provides the community with a simple, lightweight and portable Python script that allows for easy quality control of BLAST results while avoiding the drawbacks of other options. This includes the uncertain results of applying the -max_target_seqs parameter or relying on the cumbersome dependencies of other options like BioPerl, Java, etc. which add complexity and run time when running large data sets of sequences. BLAST-QC is ideal for use in high-throughput workflows and pipelines common in bioinformatic and genomic research, and the script has been designed for portability and easy integration into whatever type of processes the user may be running.",
                        "date": "2020-08-12T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Torkian B."
                            },
                            {
                                "name": "Hann S."
                            },
                            {
                                "name": "Preisner E."
                            },
                            {
                                "name": "Norman R.S."
                            }
                        ],
                        "journal": "Environmental Microbiomes"
                    }
                }
            ],
            "credit": [
                {
                    "name": "R. Sean Norman",
                    "email": "rsnorman@mailbox.sc.edu",
                    "url": null,
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        },
        {
            "name": "BlastKOALA",
            "description": "BlastKOALA (KEGG Orthology And Links Annotation) is a KEGG Web service for annotating genomes and understanding organisms.",
            "homepage": "https://www.kegg.jp/blastkoala/",
            "biotoolsID": "blastkoala",
            "biotoolsCURIE": "biotools:blastkoala",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0362",
                            "term": "Genome annotation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0533",
                            "term": "Expression profile pathway mapping"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                }
            ],
            "operatingSystem": [],
            "language": [],
            "license": null,
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            "maturity": null,
            "cost": "Free of charge (with restrictions)",
            "accessibility": "Open access (with restrictions)",
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            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "https://www.kegg.jp/blastkoala/help_blastkoala.html",
                    "type": [
                        "Quick start guide"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1016/j.jmb.2015.11.006",
                    "pmid": "26585406",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences",
                        "abstract": "© 2015 The Authors.BlastKOALA and GhostKOALA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules to infer high-level functions of the organism or the ecosystem. Both servers are made freely available at the KEGG Web site (http://www.kegg.jp/blastkoala/). In BlastKOALA, the KO assignment is performed by a modified version of the internally used KOALA algorithm after the BLAST search against a non-redundant dataset of pangenome sequences at the species, genus or family level, which is generated from the KEGG GENES database by retaining the KO content of each taxonomic category. In GhostKOALA, which utilizes more rapid GHOSTX for database search and is suitable for metagenome annotation, the pangenome dataset is supplemented with Cd-hit clusters including those for viral genes. The result files may be downloaded and manipulated for further KEGG Mapper analysis, such as comparative pathway analysis using multiple BlastKOALA results.",
                        "date": "2016-02-22T00:00:00Z",
                        "citationCount": 948,
                        "authors": [
                            {
                                "name": "Kanehisa M."
                            },
                            {
                                "name": "Sato Y."
                            },
                            {
                                "name": "Morishima K."
                            }
                        ],
                        "journal": "Journal of Molecular Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Minoru Kanehisa",
                    "email": "kanehisa@kuicr.kyoto-u.ac.jp",
                    "url": null,
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                }
            ],
            "owner": "Kigaard",
            "additionDate": "2021-05-26T19:30:25Z",
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        },
        {
            "name": "BAIT",
            "description": "BAIT (Bioinformatic Analysis of Inherited Templates) is a software to create strand inheritance plots in data derived from the Strand-Seq sequencing protocol. The software is designed to be flexible with a range of species, and basic template folders can called to read in species-specific data.",
            "homepage": "http://sourceforge.net/projects/bait/",
            "biotoolsID": "bait",
            "biotoolsCURIE": "biotools:bait",
            "version": [
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            "otherID": [
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                    "type": "rrid",
                    "version": null
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2429",
                            "term": "Mapping"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2945",
                            "term": "Analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0654",
                    "term": "DNA"
                },
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "R"
            ],
            "license": null,
            "collectionID": [],
            "maturity": null,
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            "elixirPlatform": [],
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            "elixirCommunity": [],
            "link": [
                {
                    "url": "http://www.mybiosoftware.com/bait-1-0-organizing-genomes-and-mapping-rearrangements-in-single-cells.html",
                    "type": [
                        "Software catalogue"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "https://sourceforge.net/p/bait/wiki/Home/",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1038/nmeth.2206",
                    "pmid": "23042453",
                    "pmcid": "PMC3580294",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution",
                        "abstract": "DNA rearrangements such as sister chromatid exchanges (SCEs) are sensitive indicators of genomic stress and instability, but they are typically masked by single-cell sequencing techniques. We developed Strand-seq to independently sequence parental DNA template strands from single cells, making it possible to map SCEs at orders-of-magnitude greater resolution than was previously possible. On average, murine embryonic stem (mES) cells exhibit eight SCEs, which are detected at a resolution of up to 23 bp. Strikingly, Strand-seq of 62 single mES cells predicts that the mm9 mouse reference genome assembly contains at least 17 incorrectly oriented segments totaling nearly 1% of the genome. These misoriented contigs and fragments have persisted through several iterations of the mouse reference genome and have been difficult to detect using conventional sequencing techniques. The ability to map SCE events at high resolution and fine-tune reference genomes by Strand-seq dramatically expands the scope of single-cell sequencing. © 2012 Nature America, Inc. All rights reserved.",
                        "date": "2012-11-01T00:00:00Z",
                        "citationCount": 74,
                        "authors": [
                            {
                                "name": "Falconer E."
                            },
                            {
                                "name": "Hills M."
                            },
                            {
                                "name": "Naumann U."
                            },
                            {
                                "name": "Poon S.S.S."
                            },
                            {
                                "name": "Chavez E.A."
                            },
                            {
                                "name": "Sanders A.D."
                            },
                            {
                                "name": "Zhao Y."
                            },
                            {
                                "name": "Hirst M."
                            },
                            {
                                "name": "Lansdorp P.M."
                            }
                        ],
                        "journal": "Nature Methods"
                    }
                }
            ],
            "credit": [
                {
                    "name": null,
                    "email": null,
                    "url": "http://www.terryfoxlab.ca/",
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "owner": "mbs_import",
            "additionDate": "2017-12-18T00:03:48Z",
            "lastUpdate": "2021-04-14T19:51:37Z",
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            "validated": 1,
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        },
        {
            "name": "BAlaS",
            "description": "BAlaS is an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design.",
            "homepage": "https://balas.app",
            "biotoolsID": "balas",
            "biotoolsCURIE": "biotools:balas",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3907",
                            "term": "Information extraction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0321",
                            "term": "Protein structure validation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0248",
                            "term": "Residue interaction calculation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                },
                {
                    "uri": "http://edamontology.org/topic_3957",
                    "term": "Protein interaction experiment"
                },
                {
                    "uri": "http://edamontology.org/topic_0130",
                    "term": "Protein folding, stability and design"
                },
                {
                    "uri": "http://edamontology.org/topic_3336",
                    "term": "Drug discovery"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Elm",
                "Python",
                "C++"
            ],
            "license": "MIT",
            "collectionID": [],
            "maturity": null,
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            "elixirPlatform": [],
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            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/wells-wood-research/balas",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/BIOINFORMATICS/BTAA026",
                    "pmid": "31930404",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BAlaS: Fast, interactive and accessible computational alanine-scanning using BudeAlaScan",
                        "abstract": "© 2020 The Author(s). Published by Oxford University Press. All rights reserved.Motivation: In experimental protein engineering, alanine-scanning mutagenesis involves the replacement of selected residues with alanine to determine the energetic contribution of each side chain to forming an interaction. For example, it is often used to study protein-protein interactions. However, such experiments can be time-consuming and costly, which has led to the development of programmes for performing computational alanine-scanning mutagenesis (CASM) to guide experiments. While programmes are available for this, there is a need for a real-time web application that is accessible to non-expert users. Results: Here, we present BAlaS, an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design.",
                        "date": "2020-05-01T00:00:00Z",
                        "citationCount": 7,
                        "authors": [
                            {
                                "name": "Wood C.W."
                            },
                            {
                                "name": "Ibarra A.A."
                            },
                            {
                                "name": "Bartlett G.J."
                            },
                            {
                                "name": "Wilson A.J."
                            },
                            {
                                "name": "Wilson A.J."
                            },
                            {
                                "name": "Woolfson D.N."
                            },
                            {
                                "name": "Woolfson D.N."
                            },
                            {
                                "name": "Woolfson D.N."
                            },
                            {
                                "name": "Sessions R.B."
                            },
                            {
                                "name": "Sessions R.B."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Christopher W Wood",
                    "email": "chris.wood@ed.ac.uk",
                    "url": null,
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                },
                {
                    "name": "Richard B Sessions",
                    "email": "r.sessions@bristol.ac.uk",
                    "url": null,
                    "orcidid": null,
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                }
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            "owner": "Kigaard",
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        },
        {
            "name": "BYASE",
            "description": "BYASE is a Python package that uses Bayesian inference to identify gene-level and isoform-level ASE (Allele-specific expression) in polyploid (diploid or higher) organisms from single-end or paired-end RNA-seq data.",
            "homepage": "https://github.com/ncjllld/byase",
            "biotoolsID": "byase",
            "biotoolsCURIE": "biotools:byase",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3454",
                            "term": "Phasing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3223",
                            "term": "Differential gene expression analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3661",
                            "term": "SNP annotation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Desktop application",
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                },
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_3295",
                    "term": "Epigenetics"
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                    "term": "Gene transcripts"
                }
            ],
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                {
                    "doi": "10.1093/BIOINFORMATICS/BTAA636",
                    "pmid": "32678892",
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                    "metadata": {
                        "title": "BYASE: A Python library for estimating gene and isoform level allele-specific expression",
                        "abstract": "© 2020 Oxford University Press. All rights reserved.Allele-specific expression (ASE) is involved in many important biological mechanisms. We present a python package BYASE and its graphical user interface (GUI) tool BYASE-GUI for the identification of ASE from singleend and paired-end RNA-seq data based on Bayesian inference, which can simultaneously report differences in gene-level and isoform-level expression. BYASE uses both phased SNPs and non-phased SNPs, and supports polyploid organisms. Availability and implementation: The source codes of BYASE and BYASE-GUI are freely available at https://github. com/ncjllld/byase and https://github.com/ncjllld/byase_gui.",
                        "date": "2020-10-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Dong L."
                            },
                            {
                                "name": "Wang J."
                            },
                            {
                                "name": "Wang G."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Guohua Wang",
                    "email": "ghwang@hit.edu.cn",
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        {
            "name": "Blast2Fish",
            "description": "Blast2Fish is a reference-based annotation web tool for transcriptome analysis of non-model teleost fish. It is a novel approach for performing functional enrichment analysis on non-model teleost fish transcriptome data.",
            "homepage": "http://blast2fish.ntou.edu.tw",
            "biotoolsID": "blast2fish",
            "biotoolsCURIE": "biotools:blast2fish",
            "version": [],
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2436",
                            "term": "Gene-set enrichment analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3644",
                            "term": "de Novo sequencing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0524",
                            "term": "De-novo assembly"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
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                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                },
                {
                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
                }
            ],
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                {
                    "doi": "10.1186/S12859-020-3507-9",
                    "pmid": "32366294",
                    "pmcid": "PMC7199347",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Blast2Fish: A reference-based annotation web tool for transcriptome analysis of non-model teleost fish",
                        "abstract": "© 2020 The Author(s).Background: Transcriptome analysis by next-generation sequencing has become a popular technique in recent years. This approach is quite suitable for non-model organism study, as de novo assembly is independent of prior genomic sequences of organisms. De novo sequencing has benefited many studies on commercially important fish species. However, to understand the functions of these assembled sequences, they still need to be annotated with existing sequence databases. By combining Basic Local Alignment Search Tool (BLAST) and Gene Ontology analysis, we were able to identify homologous sequences of assembled sequences and describe their characteristics using pre-defined tags for each gene, though the above conventional annotation results obtained for non-model assembled sequences was still associated with a lack of pre-defined tags and poorly documented records in the database. Results: We introduced Blast2Fish, a novel approach for performing functional enrichment analysis on non-model teleost fish transcriptome data. The Blast2Fish pipeline was designed to be a reference-based enrichment method. Instead of annotating the BLAST single top hit by a pre-defined gene-to-tag database, we included 500 hits to search related PubMed articles and parse biological terms. These descriptive terms were then sorted and recorded as annotations for the query. The results showed that Blast2Fish was capable of providing meaningful annotations on immunology topics for non-model fish transcriptome analysis. Conclusion: Blast2Fish provides a novel approach for annotating sequences of non-model fish. The reference-based strategy allows annotation to be performed without pre-defined tags for each gene. This method strongly benefits non-model teleost fish studies for gene functional enrichment analysis.",
                        "date": "2020-05-04T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Tso C.-H."
                            },
                            {
                                "name": "Wu J.-L."
                            },
                            {
                                "name": "Lu M.-W."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Ming-Wei Lu",
                    "email": "mingwei@ntou.edu.tw",
                    "url": null,
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        {
            "name": "BLAMM",
            "description": "BLAS Accelerated Motif Matching or blamm is a tool to find Position Weight Matrix (PWM) occurrences in DNA sequences.",
            "homepage": "https://github.com/biointec/blamm",
            "biotoolsID": "blamm",
            "biotoolsCURIE": "biotools:blamm",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0239",
                            "term": "Sequence motif recognition"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3359",
                            "term": "Splitting"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3211",
                            "term": "Genome indexing"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3316",
                    "term": "Computer science"
                },
                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_3570",
                    "term": "Pure mathematics"
                }
            ],
            "operatingSystem": [],
            "language": [
                "C++"
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                {
                    "doi": "10.1186/S12859-020-3348-6",
                    "pmid": "32164557",
                    "pmcid": "PMC7068855",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BLAMM: BLAS-based algorithm for finding position weight matrix occurrences in DNA sequences on CPUs and GPUs",
                        "abstract": "© 2020 The Author(s).Background: The identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed. Results: We propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated with high efficiency using optimized BLAS library implementations. The algorithm is easy to parallelize and implement on CPUs and GPUs and has a runtime that is independent of the selected p-value. In terms of single-core performance, it is competitive with state-of-the-art software for PWM matching while being much more efficient when using multithreading. Additionally, BLAMM requires negligible memory. For example, both strands of the entire human genome can be scanned for 1404 PWMs in the JASPAR database in 13 min with a p-value of 10-4 using a 36-core machine. On a dual GPU system, the same task can be performed in under 5 min. Conclusions: BLAMM is an efficient tool for identifying PWM matches in large DNA sequences. Its C++ source code is available under the GNU General Public License Version 3 at https://github.com/biointec/blamm.",
                        "date": "2020-03-11T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Fostier J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jan Fostier",
                    "email": "jan.fostier@ugent.be",
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