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            "description": "Software for analyzing next generation sequencing data. The software can handle a number of different input types from mapped reads to imputed genotype probabilities. Most methods take genotype uncertainty into account instead of basing the analysis on called genotypes. This is especially useful for low and medium depth data.",
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                        "title": "ANGSD: Analysis of Next Generation Sequencing Data",
                        "abstract": "© 2014 Korneliussen et al.Background: High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously. Results: We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods. Conclusions: The open source c/c++ program ANGSD is available at . The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.",
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            "name": "SeqAn",
            "description": "An open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Our library applies a unique generic design that guarantees high performance, generality, extensibility, and integration with other libraries. The library is easy to use and simplifies the development of new software tools with a minimal loss of performance. Additionally, it includes several ready-made command line tools for common tasks in sequence analysis.",
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                    "term": "Comparative genomics"
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                    "term": "Metagenomics"
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                    "term": "Transcriptomics"
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                {
                    "url": "https://seqan.readthedocs.org/en/master/index.html",
                    "type": [
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                    "note": "Tutorials SeqAn 2.4.0"
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-9-11",
                    "pmid": null,
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                        "title": "SeqAn an efficient, generic C++ library for sequence analysis",
                        "abstract": "Background: The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome 1 would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. Results: To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use. Conclusion: We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms. © 2008 Döring et al; licensee BioMed Central Ltd.",
                        "date": "2008-01-09T00:00:00Z",
                        "citationCount": 185,
                        "authors": [
                            {
                                "name": "Doring A."
                            },
                            {
                                "name": "Weese D."
                            },
                            {
                                "name": "Rausch T."
                            },
                            {
                                "name": "Reinert K."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1016/j.jbiotec.2017.07.017",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "The SeqAn C++ template library for efficient sequence analysis: A resource for programmers",
                        "abstract": "© 2017 The Author(s)Background The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 (Döring et al., 2008). Results The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools. Conclusions We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.",
                        "date": "2017-11-10T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Reinert K."
                            },
                            {
                                "name": "Dadi T.H."
                            },
                            {
                                "name": "Ehrhardt M."
                            },
                            {
                                "name": "Hauswedell H."
                            },
                            {
                                "name": "Mehringer S."
                            },
                            {
                                "name": "Rahn R."
                            },
                            {
                                "name": "Kim J."
                            },
                            {
                                "name": "Pockrandt C."
                            },
                            {
                                "name": "Winkler J."
                            },
                            {
                                "name": "Siragusa E."
                            },
                            {
                                "name": "Urgese G."
                            },
                            {
                                "name": "Weese D."
                            }
                        ],
                        "journal": "Journal of Biotechnology"
                    }
                }
            ],
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                    "name": "Freie Universität Berlin",
                    "email": null,
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                    "name": "Center for Integrative Bioinformatics (CiBi)",
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                },
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                    "name": "cibi",
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            "description": "Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The antigen receptor gene profiler tool (TRIP) offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These  provide  detailed  information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny.",
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                {
                    "doi": "10.1186/s12859-020-03669-1",
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                    "note": null,
                    "metadata": {
                        "title": "TRIP - T cell receptor/immunoglobulin profiler",
                        "abstract": "© 2020 The Author(s).Background: Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These provide detailed information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny. Results: Two sets of experiments have been performed in order to evaluate the efficiency and performance of the TRIP tool. The first used a number of synthetic datasets, ranging from 250k to 1M sequences, and established the linear response time of the tool (about 6 h for 1M sequences processed through the entire BcR IG data pipeline). The reproducibility of the tool was tested comparing the results produced by the main TRIP workflow with the results from a previous pipeline used on the Galaxy platform. As expected, no significant differences were noted between the two tools; although the preselection process seems to be stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered out, with no impact on the final results. Conclusions: TRIP is a software framework that provides analytical services on antigen receptor gene sequence data. It is accurate and contains functions for data wrangling, cleaning, analysis and visualization, enabling the user to build a pipeline tailored to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler.",
                        "date": "2020-09-29T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Kotouza M.T."
                            },
                            {
                                "name": "Gemenetzi K."
                            },
                            {
                                "name": "Galigalidou C."
                            },
                            {
                                "name": "Vlachonikola E."
                            },
                            {
                                "name": "Pechlivanis N."
                            },
                            {
                                "name": "Agathangelidis A."
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                            {
                                "name": "Sandaltzopoulos R."
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                            {
                                "name": "Mitkas P.A."
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                            {
                                "name": "Stamatopoulos K."
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                    "term": "DNA structural variation"
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                    "metadata": {
                        "title": "Accurate detection of complex structural variations using single-molecule sequencing",
                        "abstract": "© 2018 The Author(s).Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.",
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                        "authors": [
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                                "name": "Sedlazeck F.J."
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                            {
                                "name": "Rescheneder P."
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                            {
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                            {
                                "name": "Fang H."
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                            {
                                "name": "Nattestad M."
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                            {
                                "name": "Von Haeseler A."
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                            {
                                "name": "Schatz M.C."
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                    "term": "Genetics"
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                    "term": "Gene regulation"
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                    "metadata": {
                        "title": "The Subread aligner: Fast, accurate and scalable read mapping by seed-and-vote",
                        "abstract": "Read alignment is an ongoing challenge for the analysis of data from sequencing technologies. This article proposes an elegantly simple multi-seed strategy, called seed-and-vote, for mapping reads to a reference genome. The new strategy chooses the mapped genomic location for the read directly from the seeds. It uses a relatively large number of short seeds (called subreads) extracted from each read and allows all the seeds to vote on the optimal location. When the read length is <160 bp, overlapping subreads are used. More conventional alignment algorithms are then used to fill in detailed mismatch and indel information between the subreads that make up the winning voting block. The strategy is fast because the overall genomic location has already been chosen before the detailed alignment is done. It is sensitive because no individual subread is required to map exactly, nor are individual subreads constrained to map close by other subreads. It is accurate because the final location must be supported by several different subreads. The strategy extends easily to find exon junctions, by locating reads that contain sets of subreads mapping to different exons of the same gene. It scales up efficiently for longer reads. © 2013 The Author(s) 2013.",
                        "date": "2013-05-01T00:00:00Z",
                        "citationCount": 1098,
                        "authors": [
                            {
                                "name": "Liao Y."
                            },
                            {
                                "name": "Smyth G.K."
                            },
                            {
                                "name": "Shi W."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
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            "credit": [
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                    "name": "Wei Shi",
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                    "url": null,
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            "name": "IMPACT",
            "description": "A Graphical User Interface for a software used to assess adaptive evolution in protein-coding genes. This software makes use of several known bioinformatics software, e.g., ConTest, Jmol, PhyML, ATV, etc.",
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                    "term": "Molecular evolution"
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                    "term": "Protein structure analysis"
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            ],
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            "download": [
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            ],
            "publication": [
                {
                    "doi": "10.1093/jhered/esr003",
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                    "metadata": {
                        "title": "IMPACT: Integrated multiprogram platform for analyses in con test",
                        "abstract": "In this paper, we introduce a new Graphical User Interface that estimates evolutionary rates on protein sequences by assessing changes in biochemical constraints. We describe IMPACT, a platform-independent (tested in Linux, Windows, and MacOS) easy to install software written in Java. IMPACT integrates the use of a built-in multiple sequence alignment editor, with programs that perform phylogenetic and protein structure analyses (ConTest, PhyML, ATV, and Jmol) allowing the user to quickly and efficiently perform evolutionary analyses on protein sequences, including the detection of selection (negative and positive) signatures at the amino acid scale, which can provide fundamental insight about species evolution and ecological fitness. IMPACT provides the user with a working platform that combines a number of bioinformatics tools and utilities in one place, transferring information directly among the various programs and therefore increasing the overall performance of evolutionary analyses on proteins. © 2011 The American Genetic Association. All rights reserved.",
                        "date": "2011-05-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Maldonado E."
                            },
                            {
                                "name": "Dutheil J.Y."
                            },
                            {
                                "name": "Da Fonseca R.R."
                            },
                            {
                                "name": "Vasconcelos V."
                            },
                            {
                                "name": "Antunes A."
                            }
                        ],
                        "journal": "Journal of Heredity"
                    }
                }
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            "credit": [
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                            "term": "Nucleic acid structure comparison"
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                            "term": "Sequence analysis"
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                            "uri": "http://edamontology.org/operation_0475",
                            "term": "Nucleic acid structure prediction"
                        },
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                            "uri": "http://edamontology.org/operation_2481",
                            "term": "Nucleic acid structure analysis"
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                            "uri": "http://edamontology.org/operation_0279",
                            "term": "Nucleic acid folding analysis"
                        }
                    ],
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                    "term": "Nucleic acids"
                },
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                    "uri": "http://edamontology.org/topic_0097",
                    "term": "Nucleic acid structure analysis"
                },
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                    "uri": "http://edamontology.org/topic_0082",
                    "term": "Structure prediction"
                },
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