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                        "title": "Probability-based protein identification by searching sequence databases using mass spectrometry data",
                        "abstract": "Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.",
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                                "name": "Perkins D.N."
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                                "name": "Pappin D.J.C."
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                        "title": "Error tolerant searching of uninterpreted tandem mass spectrometry data",
                        "abstract": "An error tolerant mode for database matching of uninterpreted tandem mass spectrometry data is described. Selected database entries are searched without enzyme specificity, using a comprehensive list of chemical and post-translational modifications, together with a residue substitution matrix. The modifications are tested serially, to avoid the catastrophic loss of discrimination that would occur if all the permutations of large numbers of modifications in combination were possible. The new mode has been coded as an extension to the Mascot search engine, and tested against a number of Liquid chromatography-tandem mass spectrometry datasets. The results show a number of additional peptide matches, but require careful interpretation. The most significant limitation of this approach is that it can only reveal new matches to proteins that already have at least one significant peptide match.",
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                        "abstract": "A new result report for Mascot search results is described. A greedy set cover algorithm is used to create a minimal set of proteins, which is then grouped into families on the basis of shared peptide matches. Protein families with multiple members are represented by dendrograms, generated by hierarchical clustering using the score of the nonshared peptide matches as a distance metric. The peptide matches to the proteins in a family can be compared side by side to assess the experimental evidence for each protein. If the evidence for a particular family member is considered inadequate, the dendrogram can be cut to reduce the number of distinct family members. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc.",
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                                "name": "Emery P.A."
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                    "url": "https://github.com/vallenderlab/MicrobiomeR",
                    "note": null,
                    "type": "Repository"
                }
            ],
            "biotoolsID": "MicrobiomeR",
            "additionDate": "2019-08-13T22:07:01Z",
            "name": "MicrobiomeR",
            "license": "MIT",
            "documentation": [
                {
                    "url": "https://microbiomer.vallenderlab.science/",
                    "note": null,
                    "type": "General"
                }
            ],
            "maturity": null,
            "operatingSystem": [
                "Linux",
                "Windows"
            ]
        },
        {
            "collectionID": [],
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            "topic": [
                {
                    "term": "Population genetics",
                    "uri": "http://edamontology.org/topic_3056"
                }
            ],
            "owner": "grenaud",
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            "publication": [
                {
                    "doi": "10.1534/genetics.119.302057",
                    "version": null,
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                    "metadata": {
                        "title": "Joint Estimates of Heterozygosity and Runs of Homozygosity for Modern and Ancient Samples",
                        "abstract": "Copyright © 2019 by the Genetics Society of America.Both the total amount and the distribution of heterozygous sites within individual genomes are informative about the genetic diversity of the population they belong to. Detecting true heterozygous sites in ancient genomes is complicated by the generally limited coverage achieved and the presence of post-mortem damage inflating sequencing errors. Additionally, large runs of homozygosity found in the genomes of particularly inbred individuals and of domestic animals can skew estimates of genome-wide heterozygosity rates. Current computational tools aimed at estimating runs of homozygosity and genome-wide heterozygosity levels are generally sensitive to such limitations. Here, we introduce ROHan, a probabilistic method which substantially improves the estimate of heterozygosity rates both genome-wide and for genomic local windows. It combines a local Bayesian model and a Hidden Markov Model at the genome-wide level and can work both on modern and ancient samples. We show that our algorithm outperforms currently available methods for predicting heterozygosity rates for ancient samples. Specifically, ROHan can delineate large runs of homozygosity (at megabase scales) and produce a reliable confidence interval for the genome-wide rate of heterozygosity outside of such regions from modern genomes with a depth of coverage as low as 5-6× and down to 7-8× for ancient samples showing moderate DNA damage. We apply ROHan to a series of modern and ancient genomes previously published and revise available estimates of heterozygosity for humans, chimpanzees and horses.",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Renaud G."
                            },
                            {
                                "name": "Hanghoj K."
                            },
                            {
                                "name": "Korneliussen T.S."
                            },
                            {
                                "name": "Willerslev E."
                            },
                            {
                                "name": "Orlando L."
                            }
                        ],
                        "date": "2019-07-01T00:00:00Z",
                        "journal": "Genetics"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [],
            "biotoolsCURIE": "biotools:ROHan",
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            "elixir_badge": 0,
            "homepage": "http://grenaud.github.io/ROHan/",
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            "otherID": [],
            "description": "Joint inference of Runs of Homozygosity and rates of heterozygosity",
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            "language": [
                "C++"
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            "biotoolsID": "ROHan",
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            "name": "ROHan",
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            "documentation": [],
            "maturity": "Mature",
            "operatingSystem": [
                "Linux"
            ]
        },
        {
            "collectionID": [],
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            "topic": [
                {
                    "term": "DNA polymorphism",
                    "uri": "http://edamontology.org/topic_2885"
                }
            ],
            "owner": "grenaud",
            "cost": "Free of charge",
            "relation": [],
            "download": [],
            "validated": 0,
            "publication": [
                {
                    "doi": "10.1186/s13059-015-0776-0",
                    "version": null,
                    "pmid": null,
                    "type": null,
                    "pmcid": null,
                    "metadata": {
                        "title": "Schmutzi: Estimation of contamination and endogenous mitochondrial consensus calling for ancient DNA",
                        "abstract": "© 2015 Renaud et al. Ancient DNA is typically highly degraded with appreciable cytosine deamination, and contamination with present-day DNA often complicates the identification of endogenous molecules. Together, these factors impede accurate assembly of the endogenous ancient mitochondrial genome. We present schmutzi, an iterative approach to jointly estimate present-day human contamination in ancient human DNA datasets and reconstruct the endogenous mitochondrial genome. By using sequence deamination patterns and fragment length distributions, schmutzi accurately reconstructs the endogenous mitochondrial genome sequence even when contamination exceeds 50 %. Given sufficient coverage, schmutzi also produces reliable estimates of contamination across a range of contamination rates. Availability: https://bioinf.eva.mpg.de/schmutzi/license:GPLv3.",
                        "citationCount": 62,
                        "authors": [
                            {
                                "name": "Renaud G."
                            },
                            {
                                "name": "Slon V."
                            },
                            {
                                "name": "Duggan A.T."
                            },
                            {
                                "name": "Kelso J."
                            }
                        ],
                        "date": "2015-10-12T00:00:00Z",
                        "journal": "Genome Biology"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [],
            "biotoolsCURIE": "biotools:schmutzi",
            "elixirPlatform": [],
            "elixirNode": [],
            "version": [],
            "elixir_badge": 0,
            "homepage": "https://grenaud.github.io/schmutzi/",
            "function": [],
            "lastUpdate": "2019-08-15T15:05:13.954738Z",
            "otherID": [],
            "description": "Joint estimation of contamination for ancient DNA and endogenous mitochondrial genome inference",
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                "Command-line tool"
            ],
            "editPermission": {
                "type": "private",
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            },
            "language": [
                "C++",
                "Perl"
            ],
            "link": [],
            "biotoolsID": "schmutzi",
            "additionDate": "2019-08-15T15:04:01Z",
            "name": "schmutzi",
            "license": "GPL-3.0",
            "documentation": [],
            "maturity": "Mature",
            "operatingSystem": [
                "Linux",
                "Mac"
            ]
        },
        {
            "collectionID": [],
            "accessibility": [
                "Open access"
            ],
            "topic": [
                {
                    "term": "Population genetics",
                    "uri": "http://edamontology.org/topic_3056"
                }
            ],
            "owner": "grenaud",
            "cost": "Free of charge",
            "relation": [],
            "download": [],
            "validated": 0,
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btx749",
                    "version": null,
                    "pmid": null,
                    "type": null,
                    "pmcid": null,
                    "metadata": {
                        "title": "Glactools: A command-line toolset for the management of genotype likelihoods and allele counts",
                        "abstract": "© 2017 The Author. Published by Oxford University Press. All rights reserved. Motivation Research projects involving population genomics routinely need to store genotyping information, population allele counts, combine files from different samples, query the data and export it to various formats. This is often done using bespoke in-house scripts, which cannot be easily adapted to new projects and seldom constitute reproducible workflows. Results We introduce glactools, a set of command-line utilities that can import data from genotypes or population-wide allele counts into an intermediate representation, compute various operations on it and export the data to several file formats used by population genetics software. This intermediate format can take two forms, one to store per-individual genotype likelihoods and a second for allele counts from one or more individuals. glactools allows users to perform operations such as intersecting datasets, merging individuals into populations, creating subsets, perform queries (e.g. return sites where a given population does not share an allele with a second one) and compute summary statistics to answer biologically relevant questions.",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Renaud G."
                            }
                        ],
                        "date": "2018-04-15T00:00:00Z",
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [],
            "biotoolsCURIE": "biotools:glactools",
            "elixirPlatform": [],
            "elixirNode": [],
            "version": [],
            "elixir_badge": 0,
            "homepage": "https://grenaud.github.io/glactools/",
            "function": [],
            "lastUpdate": "2019-08-15T15:00:40.986275Z",
            "otherID": [],
            "description": "glactools: A set of programs for the management of allele counts and genotype likelihoods",
            "toolType": [
                "Command-line tool"
            ],
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "language": [
                "C++"
            ],
            "link": [],
            "biotoolsID": "glactools",
            "additionDate": "2019-08-15T14:59:38Z",
            "name": "glactools",
            "license": "GPL-3.0",
            "documentation": [],
            "maturity": "Mature",
            "operatingSystem": [
                "Linux"
            ]
        },
        {
            "collectionID": [],
            "accessibility": [],
            "topic": [
                {
                    "term": "Mapping",
                    "uri": "http://edamontology.org/topic_0102"
                }
            ],
            "owner": "grenaud",
            "cost": "Free of charge",
            "relation": [],
            "download": [],
            "validated": 0,
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btu719",
                    "version": null,
                    "pmid": "25359895",
                    "type": null,
                    "pmcid": null,
                    "metadata": {
                        "title": "DeML: Robust demultiplexing of Illumina sequences using a likelihood-based approach",
                        "abstract": "© The Author 2014. Published by Oxford University Press. All rights reserved.Motivation: Pooling multiple samples increases the efficiency and lowers the cost of DNA sequencing. One approach to multiplexing is to use short DNA indices to uniquely identify each sample. After sequencing, reads must be assigned in silico to the sample of origin, a process referred to as demultiplexing. Demultiplexing software typically identifies the sample of origin using a fixed number of mismatches between the read index and a reference index set. This approach may fail or misassign reads when the sequencing quality of the indices is poor. Results: We introduce deML, a maximum likelihood algorithm that demultiplexes Illumina sequences. deML computes the likelihood of an observed index sequence being derived from a specified sample. A quality score which reflects the probability of the assignment being correct is generated for each read. Using these quality scores, even very problematic datasets can be demultiplexed and an error threshold can be set.",
                        "citationCount": 49,
                        "authors": [
                            {
                                "name": "Renaud G."
                            },
                            {
                                "name": "Stenzel U."
                            },
                            {
                                "name": "Maricic T."
                            },
                            {
                                "name": "Wiebe V."
                            },
                            {
                                "name": "Kelso J."
                            }
                        ],
                        "date": "2015-03-01T00:00:00Z",
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [],
            "biotoolsCURIE": "biotools:deML",
            "elixirPlatform": [],
            "elixirNode": [],
            "version": [],
            "elixir_badge": 0,
            "homepage": "http://grenaud.github.io/deML/",
            "function": [],
            "lastUpdate": "2019-08-15T14:55:52.230758Z",
            "otherID": [],
            "description": "deML is a maximum likelihood demultiplexing algorithm that allows probabilistic sample assignments. We compute the likelihood of assignment to various samples and assign to the most likely sample. We then compute the assignment quality to avoid ambiguous assignments. This is similar to how mapping quality solves the problem of ambiguous assignments.",
            "toolType": [
                "Command-line tool"
            ],
            "editPermission": {
                "type": "private",
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            },
            "language": [
                "C++"
            ],
            "link": [],
            "biotoolsID": "deML",
            "additionDate": "2019-08-15T14:50:15Z",
            "name": "deML",
            "license": null,
            "documentation": [],
            "maturity": "Mature",
            "operatingSystem": [
                "Linux",
                "Mac"
            ]
        },
        {
            "collectionID": [],
            "accessibility": [],
            "topic": [
                {
                    "term": "Sequence analysis",
                    "uri": "http://edamontology.org/topic_0080"
                }
            ],
            "owner": "telatin",
            "cost": null,
            "relation": [],
            "download": [
                {
                    "url": "https://cpan.metacpan.org/authors/id/P/PR/PROCH/FASTX-Reader-0.60.tar.gz",
                    "note": null,
                    "version": "0.6",
                    "type": "Source package"
                }
            ],
            "validated": 0,
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            "homepage_status": 0,
            "credit": [],
            "biotoolsCURIE": "biotools:FASTXReader",
            "elixirPlatform": [],
            "elixirNode": [],
            "version": [
                "0.6"
            ],
            "elixir_badge": 0,
            "homepage": "https://metacpan.org/pod/FASTX::Reader",
            "function": [
                {
                    "note": null,
                    "input": [
                        {
                            "data": {
                                "term": "Sequence",
                                "uri": "http://edamontology.org/data_2044"
                            },
                            "format": [
                                {
                                    "term": "FASTQ",
                                    "uri": "http://edamontology.org/format_1930"
                                },
                                {
                                    "term": "FASTA",
                                    "uri": "http://edamontology.org/format_1929"
                                }
                            ]
                        }
                    ],
                    "operation": [
                        {
                            "term": "Data retrieval",
                            "uri": "http://edamontology.org/operation_2422"
                        }
                    ],
                    "cmd": "#!/usr/bin/env perl\nuse FASTX::Reader\nmy $reader = FASTX::Reader->new( filename => 'input.fastq' );\nwhile (my $read = $reader->getRead() ) {\n   print $read{name} , \" (\", $read{comment}, \")\\n\";\n   print $read{seq} , \"\\t\", $read{qual}, \"\\n\";\n}",
                    "output": []
                }
            ],
            "lastUpdate": "2019-08-15T10:32:26.992879Z",
            "otherID": [],
            "description": "a Perl module to parse FASTA/FASTQ files",
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                "Command-line tool"
            ],
            "editPermission": {
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            },
            "language": [
                "Perl"
            ],
            "link": [
                {
                    "url": "https://github.com/telatin/FASTQ-Parser",
                    "note": null,
                    "type": "Repository"
                }
            ],
            "biotoolsID": "FASTXReader",
            "additionDate": "2019-08-15T10:32:00Z",
            "name": "FASTX::Reader",
            "license": "MIT",
            "documentation": [],
            "maturity": "Mature",
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ]
        }
    ],
    "next": "?page=2"
}