BAMscale: quantification of next-generation sequencing peaks and generation of scaled coverage tracks

Abstract

Background: Next-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. We sought to develop a tool for rapid quantification of sequencing peaks, and an efficient method to produce coverage tracks for accurate visualization that can be intuitively interpreted by experimentalists with minimal bioinformatics background. We demonstrate its strength by integrating data from several types of sequencing approaches. Results: We have developed BAMscale, a one-step tool that processes a wide set of sequencing datasets. To demonstrate the usefulness of BAMscale, we analyzed multiple sequencing datasets from chromatin immunoprecipitation sequencing (ChIP-seq) data, chromatin state change data (Assay for Transposase-Accessible Chromatin using sequencing: ATAC-seq, DNA double strand break mapping sequencing: END-seq), DNA replication data (Okazaki fragments sequencing: OK-seq, Nascent-strand sequencing: NS-seq, single-cell replication timing sequencing: scRepli-seq) and RNA-seq data. The outputs consist of raw and normalized peak scores (multiple normalizations) in text format and scaled bigWig coverage tracks that are directly accessible to data visualization programs. Our tool can effectively analyze large sequencing datasets (~100Gb size) in minutes, outperforming currently available tools.Conclusions: BAMscale is a tool that can be used to accurately quantify and normalize identified peaks directly from BAM files, as well as create coverage tracks for visualization in genome browsers. BAMScale can be implemented for a wide set of methods for calculating coverage tracks such as ChIP-seq / ATAC-seq, as well as splice aware RNA-seq, END-seq and OK-seq for which no dedicated software is available. BAMscale is freely available on github (https://github.com/ncbi/BAMscale).

The tissue-specific chromatin accessibility landscape of Papaver somniferum

“…The filtered, sorted and scaled bam files were converted to the bigwig format for visualization using the BAMscale with default parameters (Pongor et al, 2020). Genome browser images were created using the Integrative Genomics Viewer (IGV) v2.8.10 (Thorvaldsdóttir et al, 2013) and bigwig files were processed as described above.…”