Improvements in the detection power of algorithms for analyzing next-generation sequencing based bulked segregant analysis data via estimating thresholds at the genomic region level
Improvements in the detection power of algorithms for analyzing next-generation sequencing based bulked segregant analysis data via estimating thresholds at the genomic region level
Abstract Next-generation sequencing based bulked segregant analysis (BSA-Seq) has been widely used in identifying genomic regions associated with a trait of interest. However, the most popular algorithms for BSA-Seq data analysis have relatively low detection power, and high sequencing depths are required for the detection of genomic regions linked to the trait. Here we estimated the confidence intervals/thresholds of the popular algorithms at the genomic region level and increased the detection power of these algorithms by at least 5 folds, which should drastically reduce the sequencing cost of BSA-Seq studies.
Zhang Jianbo、Panthee Dilip R
Mountain Horticultural Crops Research and Extension Center, Department of Horticultural Science, North Carolina State UniversityMountain Horticultural Crops Research and Extension Center, Department of Horticultural Science, North Carolina State University
农业科学研究生物科学研究方法、生物科学研究技术遗传学
Zhang Jianbo,Panthee Dilip R.Improvements in the detection power of algorithms for analyzing next-generation sequencing based bulked segregant analysis data via estimating thresholds at the genomic region level[EB/OL].(2025-03-28)[2025-05-12].https://www.biorxiv.org/content/10.1101/2023.03.12.532308.点此复制
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