henhaobio
Sign in

BioinformaticsBioinformaticsPeer-reviewed journal article

pyBedGraph: a Python package for fast operations on 1-dimensional genomic signal tracks

Henry B. Zhang, Minji KimiD, Jeffrey H. ChuangiD, Yijun RuaniD

PDFDOIPreprintCode

Abstract

MotivationModern genomic research relies heavily on next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed.\n\nResultsWe developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph file. When tested on 8 ChIP-seq and ATAC-seq datasets, pyBedGraph is on average 245 times faster than the existing program. Notably, pyBedGraph can look up the exact mean signal of 1 million regions in ~0.26 second on a conventional laptop. An approximate mean for 10,000 regions can be computed in ~0.0012 second with an error rate of less than 5 percent.\n\nAvailabilitypyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license.

Citations
3

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISStovner '19Li '20Pedersen '17Wilks '21Telenius '18Pjanic '17
63% semantic matchGenomics
Endre Bakken Stovner, Pål Sætrom·Bioinformatics·Aug 1, 2019· 94 citations