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        {
            "name": "TrieDedup",
            "description": "A fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing.",
            "homepage": "https://github.com/lolrenceH/TrieDedup",
            "biotoolsID": "triededup",
            "biotoolsCURIE": "biotools:triededup",
            "version": [],
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3198",
                            "term": "Read mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2409",
                            "term": "Data handling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2403",
                            "term": "Sequence analysis"
                        }
                    ],
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                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Script"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_3519",
                    "term": "PCR experiment"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python"
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                {
                    "doi": "10.1186/s12859-024-05775-w",
                    "pmid": "38637756",
                    "pmcid": "PMC11025179",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "TrieDedup: a fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing",
                        "abstract": "Background: High-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, ‘N’s. PCR duplicates introduced in library preparation are conventionally removed in genomics studies, and several deduplication tools have been developed for this purpose. Two identical reads may appear different due to ambiguous bases and the existing tools cannot address ‘N’s correctly or efficiently. Results: Here we proposed and implemented TrieDedup, which uses the trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base ‘N’s, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage. Conclusions: The TrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment, and repertoire diversity analysis of large-scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors.",
                        "date": "2024-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Hu J."
                            },
                            {
                                "name": "Luo S."
                            },
                            {
                                "name": "Tian M."
                            },
                            {
                                "name": "Ye A.Y."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Adam Yongxin Ye",
                    "email": "yeyx2626@gmail.com",
                    "url": null,
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                {
                    "name": "Jianqiao Hu",
                    "email": null,
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            "owner": "Pub2Tools",
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        },
        {
            "name": "AACompIdent",
            "description": "Protein identification by amino acid composition, and optionally pI, Mw, species, UniProtKB keyword and calibration protein. Several constellations are available, corresponding to various amino acid analysis techniques.",
            "homepage": "https://web.expasy.org/aacompident",
            "biotoolsID": "aacompident",
            "biotoolsCURIE": "biotools:aacompident",
            "version": [],
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2479",
                            "term": "Protein sequence analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
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            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
            ],
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            "collectionID": [
                "Proteomics"
            ],
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            "documentation": [
                {
                    "url": "https://web.expasy.org/aacompident/aacomp-doc.html",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": null,
                    "pmid": "9636313",
                    "pmcid": null,
                    "type": [
                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "From proteins to proteomes: Large scale protein identification by two-dimensional electrophoresis and amino acid analysis",
                        "abstract": "Separation and identification of proteins by two-dimensional (2-D) electrophoresis can be used for protein-based gene expression analysis. In this report single protein spots, from polyvinylidene difluoride blots of micropreparative E. coli 2-D gels, were rapidly and economically identified by matching their amino acid composition, estimated pI and molecular weight against all E. coli entries in the SWISS-PROT database. Thirty proteins from an E. coli 2-D map were analyzed and identities assigned. Three of the proteins were unknown. By protein sequencing analysis, 20 of the 27 proteins were correctly identified. Importantly, correct identifications showed unambiguous “correct” score patterns. While incorrect protein identifications also showed distinctive score patterns, indicating that protein must be identified by other means. These techniques allow large-scale screening of the protein complement of simple organisms, or tissues in normal and disease states. The computer program described here is accessible via the World Wide Web at URL address (http://expasy.hcuge.ch/). © 1996 Nature Publishing Group.",
                        "date": "1996-01-01T00:00:00Z",
                        "citationCount": 689,
                        "authors": [
                            {
                                "name": "Wilkins M.R."
                            },
                            {
                                "name": "Pasquali C."
                            },
                            {
                                "name": "Appel R.D."
                            },
                            {
                                "name": "Ou K."
                            },
                            {
                                "name": "Golaz O."
                            },
                            {
                                "name": "Sanchez J.-C."
                            },
                            {
                                "name": "Yan J.X."
                            },
                            {
                                "name": "Gooley A.A."
                            },
                            {
                                "name": "Hughes G."
                            },
                            {
                                "name": "Humphery-Smith I."
                            },
                            {
                                "name": "Williams K.L."
                            },
                            {
                                "name": "Hochstrasser D.F."
                            }
                        ],
                        "journal": "Bio/Technology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ExPASy helpdesk",
                    "email": null,
                    "url": "https://www.expasy.org/support",
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        },
        {
            "name": "Infernal cmscan (EBI)",
            "description": "Infernal (\"INFERence of RNA ALignment\") is for searching DNA sequence databases (e.g. Rfam) for RNA structure and sequence similarities.",
            "homepage": "https://www.ebi.ac.uk/jdispatcher/rna/infernal_cmscan",
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                            "uri": "http://edamontology.org/operation_2403",
                            "term": "Sequence analysis"
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                "Web service"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
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                "Windows",
                "Mac"
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            "collectionID": [
                "EBI Tools",
                "Job Dispatcher Tools"
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                }
            ],
            "download": [
                {
                    "url": "http://eddylab.org/infernal/",
                    "type": "Downloads page",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "http://www.ebi.ac.uk/about/terms-of-use",
                    "type": [
                        "Terms of use"
                    ],
                    "note": null
                },
                {
                    "url": "https://www.ebi.ac.uk/jdispatcher/help",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "http://eddylab.org/infernal/",
                    "type": [
                        "General"
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btt509",
                    "pmid": null,
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                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Infernal 1.1: 100-fold faster RNA homology searches",
                        "abstract": "Summary: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables ∼100-fold acceleration over the previous version and ∼10 000-fold acceleration over exhaustive non-filtered CM searches. © The Author 2013. Published by Oxford University Press. All rights reserved.",
                        "date": "2013-11-15T00:00:00Z",
                        "citationCount": 1788,
                        "authors": [
                            {
                                "name": "Nawrocki E.P."
                            },
                            {
                                "name": "Eddy S.R."
                            }
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                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkae241",
                    "pmid": "38597606",
                    "pmcid": null,
                    "type": [
                        "Other"
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                {
                    "doi": "10.1093/nar/gkac240",
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                    "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": 867,
                        "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": "Sean Eddy",
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        },
        {
            "name": "RADAR (EBI)",
            "description": "Identify gapped approximate repeats and complex repeat architectures involving many different types of repeats.",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_0237",
                            "term": "Repeat sequence analysis"
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                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                    "note": null,
                    "cmd": null
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            ],
            "toolType": [
                "Web application",
                "Web service"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0157",
                    "term": "Sequence composition, complexity and repeats"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
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                    "doi": "10.1002/1097-0134(20001101)41:2<224::aid-prot70>3.0.co;2-z",
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                    "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": 867,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
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                            {
                                "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": "Andreas Heger",
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        {
            "name": "PfamScan (EBI)",
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                            "uri": "http://edamontology.org/operation_2478",
                            "term": "Nucleic acid sequence analysis"
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                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
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                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0858",
                                "term": "Sequence signature matches"
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                    "term": "Sequence analysis"
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                        "title": "Predicting active site residue annotations in the Pfam database",
                        "abstract": "Background: Approximately 5% of Pfam families are enzymatic, but only a small fraction of the sequences within these families (<0.5%) have had the residues responsible for catalysis determined. To increase the active site annotations in the Pfam database, we have developed a strict set of rules, chosen to reduce the rate of false positives, which enable the transfer of experimentally determined active site residue data to other sequences within the same Pfam family. Description: We have created a large database of predicted active site residues. On comparing our active site predictions to those found in UniProtKB, Catalytic Site Atlas, PROSITE and MEROPS we find that we make many novel predictions. On investigating the small subset of predictions made by these databases that are not predicted by us, we found these sequences did not meet our strict criteria for prediction. We assessed the sensitivity and specificity of our methodology and estimate that only 3% of our predicted sequences are false positives. Conclusion: We have predicted 606110 active site residues, of which 94% are not found in UniProtKB, and have increased the active site annotations in Pfam by more than 200 fold. Although implemented for Pfam, the tool we have developed for transferring the data can be applied to any alignment with associated experimental active site data and is available for download. Our active site predictions are re-calculated at each Pfam release to ensure they are comprehensive and up to date. They provide one of the largest available databases of active site annotation. © 2007 Mistry et al; licensee BioMed Central Ltd.",
                        "date": "2007-08-09T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Mistry J."
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                            {
                                "name": "Bateman A."
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                            {
                                "name": "Finn R.D."
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                        "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.",
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                        "authors": [
                            {
                                "name": "Madeira F."
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                            {
                                "name": "Pearce M."
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                            {
                                "name": "Basutkar P."
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                                "name": "Madhusoodanan N."
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                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
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                    "doi": "10.1006/jmbi.2000.4042",
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                    "metadata": {
                        "title": "T-coffee: A novel method for fast and accurate multiple sequence alignment",
                        "abstract": "We describe a new method (T-Coffee) for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives. The method is broadly based on the popular progressive approach to multiple alignment but avoids the most serious pitfalls caused by the greedy nature of this algorithm. With T-Coffee we pre-process a data set of all pair-wise alignments between the sequences. This provides us with a library of alignment information that can be used to guide the progressive alignment. Intermediate alignments are then based not only on the sequences to be aligned next but also on how all of the sequences align with each other. This alignment information can be derived from heterogeneous sources such as a mixture of alignment programs and/or structure superposition. Here, we illustrate the power of the approach by using a combination of local and global pair-wise alignments to generate the library. The resulting alignments are significantly more reliable, as determined by comparison with a set of 141 test cases, than any of the popular alternatives that we tried. The improvement, especially clear with the more difficult test cases, is always visible, regardless of the phylogenetic spread of the sequences in the tests. (C) 2000 Academic Press.",
                        "date": "2000-09-08T00:00:00Z",
                        "citationCount": 5714,
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                            {
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                            {
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                            {
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                        "journal": "Journal of Molecular Biology"
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                    "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": 867,
                        "authors": [
                            {
                                "name": "Madeira F."
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                            {
                                "name": "Pearce M."
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                            {
                                "name": "Tivey A.R.N."
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                            {
                                "name": "Basutkar P."
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                            {
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                            {
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                    "term": "DNA polymorphism"
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                        "title": "MegaSSR: a web server for large scale microsatellite identification, classification, and marker development",
                        "abstract": "Next-generation sequencing technologies have opened new avenues for using genomic data to study and develop molecular markers and improve genetic resources. Simple Sequence Repeats (SSRs) as genetic markers are increasingly used in molecular diversity and molecular breeding programs that require bioinformatics pipelines to analyze the large amounts of data. Therefore, there is an ongoing need for online tools that provide computational resources with minimal effort and maximum efficiency, including automated development of SSR markers. These tools should be flexible, customizable, and able to handle the ever-increasing amount of genomic data. Here we introduce MegaSSR (https://bioinformatics.um6p.ma/MegaSSR), a web server and a standalone pipeline that enables the design of SSR markers in any target genome. MegaSSR allows users to design targeted PCR-based primers for their selected SSR repeats and includes multiple tools that initiate computational pipelines for SSR mining, classification, comparisons, PCR primer design, in silico PCR validation, and statistical visualization. MegaSSR results can be accessed, searched, downloaded, and visualized with user-friendly web-based tools. These tools provide graphs and tables showing various aspects of SSR markers and corresponding PCR primers. MegaSSR will accelerate ongoing research in plant species and assist breeding programs in their efforts to improve current genomic resources.",
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                    "term": "Protein expression"
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                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
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                    "metadata": {
                        "title": "SynLinker: An integrated system for designing linkers and synthetic fusion proteins",
                        "abstract": "Summary: Synthetic fusion proteins have shown great potential in various biotechnological and (bio)pharmaceutical applications. They usually contain more than two protein domains joined by a linker peptide sequence which is often selected intuitively or in ad hoc manner. Thus, we developed an integrated web-based system, SynLinker, to provide appropriate linker candidates for constructing fusion proteins. We compiled a total of 2260 linker sequences comprising of natural linkers extracted from a set of non-redundant multi-domain proteins in Protein Data Bank and artificial/empirical linkers collected from literature and patents. Multiple query interface allows users to search for the desired linker candidates based on selection criteria and their preferences. In addition, a selected linker can be combined with two domain structures which are uploaded and appended at its N and C terminals, thereby predicting a de novo structure of the fusion protein. Hence, SynLinker can serve as a systematic tool for researchers who are interested in designing synthetic fusion proteins. Availability and implementation: SynLinker is freely available at http://bioinfo.bti.a-star.edu.sg/synlinker.",
                        "date": "2015-05-25T00:00:00Z",
                        "citationCount": 30,
                        "authors": [
                            {
                                "name": "Liu C."
                            },
                            {
                                "name": "Chin J.X."
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                            {
                                "name": "Lee D.-Y."
                            }
                        ],
                        "journal": "Bioinformatics"
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            "name": "COVIDep",
            "description": "COVIDep provides an up-to-date set of B-cell and T-cell epitopes that can serve as potential vaccine targets for SARS-CoV-2. The identified epitopes are experimentally-derived from the 2003 SARS virus and have a close genetic match with the available SARS-CoV-2 sequences. COVIDep is flexible and user-friendly, comprising an intuitive graphical interface and interactive visualizations.",
            "homepage": "https://covidep.ust.hk",
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