Measuring, visualizing, and diagnosing reference bias with biastools

Abstract

AbstractMany bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor’s variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using , we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use to characterize how T2T references improve large-scale bias.