Reference : BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant ana...

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Biotechnology Life sciences : Multidisciplinary, general & others Human health sciences : Neurology Human health sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/39435

Title :	BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data
Language :	English
Author, co-author :	Zhang, Zhi []
	Jung, Paul []
	Groues, Valentin [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	May, Patrick [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Linster, Carole [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Glaab, Enrico [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Publication date :	2019
Journal title :	GigaScience
Publisher :	BioMed Central
Volume :	8
Issue/season :	6
Pages :	giz060
Peer reviewed :	Yes
Audience :	International
ISSN :	2047-217X
City :	London
Country :	United Kingdom
Keywords :	[en] QTL ; NGS ; sequencing ; bulk segregant analysis ; mapping ; yeast ; statistics ; analysis ; web-application
Abstract :	[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.
Research centres :	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)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	R-AGR-3362 > PD-Strat > 01/06/2018 - 31/05/2021 > GLAAB Enrico
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/39435
DOI :	10.1093/gigascience/giz060
Other URL :	http://dx.doi.org/10.1093/gigascience/giz060
	http://gigadb.org/dataset/100595
FnR project :	FnR ; FNR11651464 > Enrico Glaab > PD-Strat > 01/06/2018 > 31/05/2021 > 2018

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