Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
Gigascience. 2019 Jun 1;8(6). doi: 10.1093/gigascience/giz060.
Quantitative trait locus (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.
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.
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.
利用大量分离群体进行数量性状基因座（QTL）作图是在模式生物中鉴定与目标表型相关的遗传变异的有效方法。通过利用下一代测序技术，可以显著提高 QTL 作图的准确性，为注释新的遗传变异提供了有价值的手段。然而，为此目的建立一个全面的分析框架是一项耗时且容易出错的任务，对于在该领域经验有限的科学家来说，这带来了许多挑战。
在这里，我们介绍了 BSA4Yeast，这是一个用于通过酵母测序数据的大量分离群体分析进行 QTL 作图的综合网络应用程序。该软件提供了自动和效率优化的数据处理、最新的功能注释以及交互式网络界面，用于探索已识别的 QTL。
BSA4Yeast 使研究人员能够有效地在 QTL 区域中识别可能的候选基因，以便通过实验验证其遗传变异是否是感兴趣表型的原因。BSA4Yeast 可在 https://bsa4yeast.lcsb.uni.lu 上免费获取。
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