F-Seq2: improving the feature density based peak caller with dynamic statistics

Abstract

Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing technologies. Peak calling is one of the first essential steps in analyzing these features by delineating regions such as open chromatin regions and transcription factor binding sites. Our original peak calling software, F-Seq, has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive sites sequencing (DNase-seq) data. However, F-Seq lacks support for user-input control dataset nor reporting test statistics, limiting its ability to capture systematic and experimental biases and accurately estimate background distributions. Here we present an improved version, F-Seq2, which combined the power of kernel density estimation and a dynamic "continuous" Poisson distribution to robustly account for local biases and solve ties when ranking candidate peaks. In F-score and motif distance analysis, we demonstrated the superior performance of F-Seq2 than other competing peak callers used by the ENCODE Consortium on simulated and real ATAC-seq and ChIP-seq datasets. The output of F-Seq2 is suitable for irreproducible discovery rate (IDR) analysis as the test statistics calculated for individual candidate summit and ties are robustly solved.