Tackling reference bias in bioinformatics: Introducing Biastools. While many bioinformatics methods aim to reduce reference bias, comprehensive measurement tools have been lacking. This paper introduces Biastools, a novel method for analyzing and categorizing instances of reference bias in various scenarios, including simulated reads with known donor variants, real reads with known donor variants, and real reads with unknown variants. Using Biastools, the research demonstrates that more inclusive graph genomes result in fewer biased sites and that end-to-end alignment reduces bias at indels compared to local aligners. The study further utilizes Biastools to characterize the improvements in large-scale bias achieved through T2T references. This innovative tool offers a valuable resource for evaluating and mitigating reference bias in bioinformatics research.
This paper aligns with the scope of Genome Biology, which focuses on genomics, computational biology, and high-throughput data analysis. The study presents a new tool for measuring and diagnosing reference bias in genomic data, a significant issue in bioinformatics research, thus contributing to the journal's mission of advancing knowledge in the field of genomics.