Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data

William W Greenwald,Naoki Nariai,Erin N Smith,Paola Benaglio,Kelly A Frazer,He Li

doi:10.1186/s12859-017-1621-0

William W Greenwald, Naoki Nariai + Show 4 more

Open Access

https://doi.org/10.1186/s12859-017-1621-0

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Abstract

BackgroundGenomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments. While many file types and analysis tools exist for storing and manipulating single locus NGS data, there is currently no file standard or analysis tool suite for manipulating and storing paired-genomic-loci: the data type resulting from “genomic interaction” studies. As genomic interaction sequencing data are becoming prevalent, a standard file format and tools for working with these data conveniently and efficiently are needed.ResultsThis article details a file standard and novel software tool suite for working with paired-genomic-loci data. We present the paired-genomic-loci (PGL) file standard for genomic-interactions data, and the accompanying analysis tool suite “pgltools”: a cross platform, pypy compatible python package available both as an easy-to-use UNIX package, and as a python module, for integration into pipelines of paired-genomic-loci analyses.ConclusionsPgltools is a freely available, open source tool suite for manipulating paired-genomic-loci data. Source code, an in-depth manual, and a tutorial are available publicly at www.github.com/billgreenwald/pgltools, and a python module of the operations can be installed from PyPI via the PyGLtools module.

Highlights

Genomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments
Numerous experimental methodologies have been developed in the past decade to study 3D configurations of the human genome, including Hi-C and ChIA-PET [1, 2]
These “genomic interaction” data have provided key insights into the regulation of gene expression, and suggest that chromatin interactions are driven by discrete, yet spatially-associated, epigenetic features [3, 4]

Summary

Introduction

Genomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments. While many file types and analysis tools exist for storing and manipulating single locus NGS data, there is currently no file standard or analysis tool suite for manipulating and storing pairedgenomic-loci: the data type resulting from “genomic interaction” studies. Numerous experimental methodologies have been developed in the past decade to study 3D configurations of the human genome, including Hi-C and ChIA-PET [1, 2] These “genomic interaction” data have provided key insights into the regulation of gene expression, and suggest that chromatin interactions are driven by discrete, yet spatially-associated, epigenetic features [3, 4]. File standards and tool suites have become essential to conduct efficient bioinformatics analyses; for example, single locus information can be encoded in the BED file format and manipulated using bedtools, enabling a wide variety of bioinformatics inquiries [5] The matrix and triplet sparse matrix formats effectively communicate coverage depth across

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