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BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data
BACKGROUND: Quantitative trait locus (QTL) mapping using bulk segregants is an effective approach for identifying genetic variants associated with phenotypes of interest in model organisms. By exploiting next-generation sequencing technology, the QTL mapping accuracy can be improved significantly, p...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571488/ https://www.ncbi.nlm.nih.gov/pubmed/31141611 http://dx.doi.org/10.1093/gigascience/giz060 |
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author | Zhang, Zhi Jung, Paul P Grouès, Valentin May, Patrick Linster, Carole Glaab, Enrico |
author_facet | Zhang, Zhi Jung, Paul P Grouès, Valentin May, Patrick Linster, Carole Glaab, Enrico |
author_sort | Zhang, Zhi |
collection | PubMed |
description | BACKGROUND: Quantitative trait locus (QTL) mapping using bulk segregants is an effective approach for identifying genetic variants associated with phenotypes of interest in model organisms. By exploiting next-generation sequencing technology, the QTL mapping accuracy can be improved significantly, providing a valuable means to annotate new genetic variants. However, setting up a comprehensive analysis framework for this purpose is a time-consuming and error-prone task, posing many challenges for scientists with limited experience in this domain. RESULTS: Here, we present BSA4Yeast, a comprehensive web application for QTL mapping via bulk segregant analysis of yeast sequencing data. The software provides an automated and efficiency-optimized data processing, up-to-date functional annotations, and an interactive web interface to explore identified QTLs. CONCLUSIONS: BSA4Yeast enables researchers to identify plausible candidate genes in QTL regions efficiently in order to validate their genetic variations experimentally as causative for a phenotype of interest. BSA4Yeast is freely available at https://bsa4yeast.lcsb.uni.lu. |
format | Online Article Text |
id | pubmed-6571488 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-65714882019-06-19 BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data Zhang, Zhi Jung, Paul P Grouès, Valentin May, Patrick Linster, Carole Glaab, Enrico Gigascience Technical Note BACKGROUND: Quantitative trait locus (QTL) mapping using bulk segregants is an effective approach for identifying genetic variants associated with phenotypes of interest in model organisms. By exploiting next-generation sequencing technology, the QTL mapping accuracy can be improved significantly, providing a valuable means to annotate new genetic variants. However, setting up a comprehensive analysis framework for this purpose is a time-consuming and error-prone task, posing many challenges for scientists with limited experience in this domain. RESULTS: Here, we present BSA4Yeast, a comprehensive web application for QTL mapping via bulk segregant analysis of yeast sequencing data. The software provides an automated and efficiency-optimized data processing, up-to-date functional annotations, and an interactive web interface to explore identified QTLs. CONCLUSIONS: BSA4Yeast enables researchers to identify plausible candidate genes in QTL regions efficiently in order to validate their genetic variations experimentally as causative for a phenotype of interest. BSA4Yeast is freely available at https://bsa4yeast.lcsb.uni.lu. Oxford University Press 2019-05-29 /pmc/articles/PMC6571488/ /pubmed/31141611 http://dx.doi.org/10.1093/gigascience/giz060 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Technical Note Zhang, Zhi Jung, Paul P Grouès, Valentin May, Patrick Linster, Carole Glaab, Enrico BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
title | BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
title_full | BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
title_fullStr | BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
title_full_unstemmed | BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
title_short | BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
title_sort | bsa4yeast: web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571488/ https://www.ncbi.nlm.nih.gov/pubmed/31141611 http://dx.doi.org/10.1093/gigascience/giz060 |
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