BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations

Wang, L., Lu, Z., Regulski, M., Jiao, Y., Chen, J., Ware, D., Xin, Z. (August 2020) BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations. Bioinformatics. ISSN 1367-4803

URL: https://pubmed.ncbi.nlm.nih.gov/32777814/
DOI: 10.1093/bioinformatics/btaa709

Abstract

SUMMARY: With the advance of next-generation sequencing (NGS) technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci (QTL) but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the linked region (the region linked to the causal mutation) and an interactive Shiny web application for visualizing the results. We deeply sequenced a sorghum male-sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked sorghum F2 populations and 1 rice F2 population and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that the BSAseq workflow will be broadly applicable to the identification of causal mutations for many phenotypes of interest. AVAILABILITY: BSAseq is freely available on https://www.sciapps.org/page/bsa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Item Type: Paper
Additional Information: 1367-4811 Wang, Liya Lu, Zhenyuan Regulski, Michael Jiao, Yinping Chen, Junping Ware, Doreen Xin, Zhanguo Journal Article England Bioinformatics. 2020 Aug 10:btaa709. doi: 10.1093/bioinformatics/btaa709.
CSHL Authors:
Communities: CSHL labs > Ware lab
Depositing User: Matthew Dunn
Date: 10 August 2020
Date Deposited: 30 Nov 2020 19:47
Last Modified: 30 Nov 2020 19:47
Related URLs:
URI: https://repository.cshl.edu/id/eprint/39756

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