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

Liya Wang, Zhenyuan Lu, Michael Regulski, Yinping Jiao, Junping Chen, Doreen Ware, Zhanguo Xin

Research output: Contribution to journalArticlepeer-review

Abstract

SUMMARY: With the advance of next-generation sequencing 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 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 AND IMPLEMENTATION: BSAseq is freely available on https://www.sciapps.org/page/bsa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)382-387
Number of pages6
JournalBioinformatics
Volume37
Issue number3
DOIs
StatePublished - Apr 20 2021

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