Evaluation of Algorithm Performance in ChIP-Seq Peak Detection

TLDR

Next‑generation sequencing combined with chromatin immunoprecipitation (ChIP‑seq) has transformed genome‑wide protein‑DNA interaction studies, prompting the creation of dozens of specialized computational tools and making method selection a challenging task. The study compares the performance of eleven peak‑calling programs on common transcription‑factor ChIP‑seq datasets, evaluating sensitivity, accuracy, and usability. The authors benchmarked the eleven programs using identical empirical datasets, assessing each tool’s sensitivity, accuracy, and usability. The analysis offers an unbiased assessment of available technologies, guiding researchers in selecting appropriate ChIP‑seq peak‑calling tools.

Abstract

Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.