TrioVis: a visualization approach for filtering genomic variants of parent–child trios

Summary: TrioVis is a visual analytics tool developed for filtering on coverage and variant frequency for genomic variants from exome sequencing of parent–child trios. In TrioVis, the variant data are organized by grouping each variant based on the laws of Mendelian inheritance. Taking three Variant...

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Detalles Bibliográficos
Autores principales: Sakai, Ryo, Sifrim, Alejandro, Vande Moere, Andrew, Aerts, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702247/
https://www.ncbi.nlm.nih.gov/pubmed/23658417
http://dx.doi.org/10.1093/bioinformatics/btt267
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author Sakai, Ryo
Sifrim, Alejandro
Vande Moere, Andrew
Aerts, Jan
author_facet Sakai, Ryo
Sifrim, Alejandro
Vande Moere, Andrew
Aerts, Jan
author_sort Sakai, Ryo
collection PubMed
description Summary: TrioVis is a visual analytics tool developed for filtering on coverage and variant frequency for genomic variants from exome sequencing of parent–child trios. In TrioVis, the variant data are organized by grouping each variant based on the laws of Mendelian inheritance. Taking three Variant Call Format files as input, TrioVis allows the user to test different coverage thresholds (i.e. different levels of stringency), to find the optimal threshold values tailored to their hypotheses and to gain insights into the global effects of filtering through interaction. Availability: Executables, source code and sample data are available at https://bitbucket.org/biovizleuven/triovis. Screencast is available at http://vimeo.com/user6757771/triovis. Contact: ryo.sakai@esat.kuleuven.be
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spelling pubmed-37022472013-07-05 TrioVis: a visualization approach for filtering genomic variants of parent–child trios Sakai, Ryo Sifrim, Alejandro Vande Moere, Andrew Aerts, Jan Bioinformatics Applications Notes Summary: TrioVis is a visual analytics tool developed for filtering on coverage and variant frequency for genomic variants from exome sequencing of parent–child trios. In TrioVis, the variant data are organized by grouping each variant based on the laws of Mendelian inheritance. Taking three Variant Call Format files as input, TrioVis allows the user to test different coverage thresholds (i.e. different levels of stringency), to find the optimal threshold values tailored to their hypotheses and to gain insights into the global effects of filtering through interaction. Availability: Executables, source code and sample data are available at https://bitbucket.org/biovizleuven/triovis. Screencast is available at http://vimeo.com/user6757771/triovis. Contact: ryo.sakai@esat.kuleuven.be Oxford University Press 2013-07-15 2013-05-08 /pmc/articles/PMC3702247/ /pubmed/23658417 http://dx.doi.org/10.1093/bioinformatics/btt267 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Sakai, Ryo
Sifrim, Alejandro
Vande Moere, Andrew
Aerts, Jan
TrioVis: a visualization approach for filtering genomic variants of parent–child trios
title TrioVis: a visualization approach for filtering genomic variants of parent–child trios
title_full TrioVis: a visualization approach for filtering genomic variants of parent–child trios
title_fullStr TrioVis: a visualization approach for filtering genomic variants of parent–child trios
title_full_unstemmed TrioVis: a visualization approach for filtering genomic variants of parent–child trios
title_short TrioVis: a visualization approach for filtering genomic variants of parent–child trios
title_sort triovis: a visualization approach for filtering genomic variants of parent–child trios
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702247/
https://www.ncbi.nlm.nih.gov/pubmed/23658417
http://dx.doi.org/10.1093/bioinformatics/btt267
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