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                        "title": "A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data",
                        "abstract": "Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. © The Author 2011. Published by Oxford University Press. All rights reserved.",
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                    "note": "HTSlib: C library for reading/writing high-throughput sequencing data.",
                    "metadata": {
                        "title": "HTSlib: C library for reading/writing high-Throughput sequencing data",
                        "abstract": "Background: Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health. Findings: We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading. Conclusion: Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded >1 million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust, and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT/BSD license.",
                        "date": "2021-02-01T00:00:00Z",
                        "citationCount": 116,
                        "authors": [
                            {
                                "name": "Bonfield J.K."
                            },
                            {
                                "name": "Marshall J."
                            },
                            {
                                "name": "Danecek P."
                            },
                            {
                                "name": "Li H."
                            },
                            {
                                "name": "Ohan V."
                            },
                            {
                                "name": "Whitwham A."
                            },
                            {
                                "name": "Keane T."
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                        "journal": "GigaScience"
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                                "term": "Nucleic acid sequence"
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                    "term": "Phylogeny"
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                    "note": "Installation with dpkg (root privileges are required)",
                    "version": null
                }
            ],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-12-124",
                    "pmid": "21526987",
                    "pmcid": "PMC3114741",
                    "type": [
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Proteinortho: Detection of (Co-)orthologs in large-scale analysis",
                        "abstract": "Background: Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.Results: The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes.Conclusions: Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware. © 2011 Lechner et al; licensee BioMed Central Ltd.",
                        "date": "2011-04-28T00:00:00Z",
                        "citationCount": 656,
                        "authors": [
                            {
                                "name": "Lechner M."
                            },
                            {
                                "name": "Findeiss S."
                            },
                            {
                                "name": "Steiner L."
                            },
                            {
                                "name": "Marz M."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Prohaska S.J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Marcus Lechner",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "note": null
                },
                {
                    "name": "Sonja J Prohaska",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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            ],
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            "owner": "klemmp",
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            "description": "Prediction system for GPI-anchored proteins.",
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            "biotoolsID": "predgpi",
            "biotoolsCURIE": "biotools:predgpi",
            "version": [
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            "otherID": [],
            "relation": [],
            "function": [
                {
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                            "uri": "http://edamontology.org/operation_3351",
                            "term": "Molecular surface analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
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                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                                "term": "Protein report"
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                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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                    "note": "Prediction",
                    "cmd": null
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            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3542",
                    "term": "Protein secondary structure"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                }
            ],
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                "Windows",
                "Mac"
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            "collectionID": [
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            ],
            "maturity": "Mature",
            "cost": "Free of charge",
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            "documentation": [
                {
                    "url": "http://gpcr.biocomp.unibo.it/predgpi",
                    "type": [
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            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-9-392",
                    "pmid": null,
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                    "type": [
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PredGPI: A GPI-anchor predictor",
                        "abstract": "Background: Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called ω-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes. Results: Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature. Conclusion: PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes. © 2008 Pierleoni et al; licensee BioMed Central Ltd.",
                        "date": "2008-09-23T00:00:00Z",
                        "citationCount": 463,
                        "authors": [
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Martelli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
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                {
                    "name": "Andrea Pierleoni",
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                },
                {
                    "name": "Rita Casadio",
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                },
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
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        },
        {
            "name": "TPpred 2.0",
            "description": "Mitochondrial targeting peptide prediction.",
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                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        },
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                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0253",
                            "term": "Sequence feature detection"
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                        {
                            "uri": "http://edamontology.org/operation_0422",
                            "term": "Protein cleavage site prediction"
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