Bedshift: perturbation of genomic interval sets

Abstract

Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. Many tools have been developed to analyze region sets, including computing similarity metrics to compare them. However, there is no way to objectively evaluate the effectiveness of region set similarity metrics. In this paper we present Bedshift, a command-line tool and Python API to generate new BED files by making random perturbations to an original BED file. Perturbed files have known similarity to the original file and are therefore useful to benchmark similarity metrics. To demonstrate, we used Bedshift to create an evaluation dataset of hundreds of perturbed files generated by shifting, adding, and dropping regions from a reference BED file. Then, we compared four similarity metrics: Jaccard score, coverage score, Euclidean distance, and cosine similarity. Our results highlight differences in behavior among these metrics, such as that Jaccard score is most sensitive to added or dropped regions, while coverage score is most sensitive to shifted regions. Together, we show that Bedshift is a useful tool for creating randomized region sets for a variety of uses.AvailabilityBSD2-licensed source code and documentation can be found at https://bedshift.databio.org.