Reference : Web-based QTL linkage analysis and bulk segregant analysis of yeast sequencing data
Scientific journals : Article
Life sciences : Biotechnology
Life sciences : Multidisciplinary, general & others
Human health sciences : Neurology
Human health sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/39435
Web-based QTL linkage analysis and bulk segregant analysis of yeast sequencing data
English
Zhang, Zhi []
Jung, Paul []
Groues, Valentin mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
May, Patrick mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Linster, Carole mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2019
GigaScience
BioMed Central
8
6
giz060
Yes
International
2047-217X
London
United Kingdom
[en] QTL ; NGS ; sequencing ; bulk segregant analysis ; mapping ; yeast ; statistics ; analysis ; web-application
[en] Quantitative Trait Loci (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. Findings: 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. Conclusion: 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Enzymology & Metabolism (Linster Group)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/39435
10.1093/gigascience/giz060
http://dx.doi.org/10.1093/gigascience/giz060
http://gigadb.org/dataset/100595
FnR ; FNR11651464 > Enrico Glaab > PD-Strat > 01/06/2018 > 31/05/2021 > 2018

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