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Related Experiment Video

Updated: Jun 25, 2025

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Pairtools: From sequencing data to chromosome contacts.

, Nezar Abdennur^1,2, Geoffrey Fudenberg³

¹Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States of America.

Plos Computational Biology

|May 29, 2024

Summary

This summary is machine-generated.

Pairtools is a new suite of tools designed for efficient processing of 3D genome organization sequencing data. It offers flexible pipelines for analyzing chromosome conformation capture (3C+) data, improving performance and versatility.

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Last Updated: Jun 25, 2025

Capturing Chromosome Conformation Across Length Scales

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Published on: January 20, 2023

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Published on: April 23, 2011

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

The 3D genome organization field generates vast amounts of chromosome conformation capture (3C+) data.
Processing this massive and heterogeneous data into contact pairs requires high-performance and flexible computational tools.

Purpose of the Study:

To introduce pairtools, a flexible suite of command-line interface (CLI) tools for efficient contact extraction from 3C+ sequencing data.
To provide modular tools that can be chained into customizable data processing pipelines.

Main Methods:

Pairtools offers core operations including parsing .sam alignments into Hi-C pairs, sorting, and duplicate removal.
Auxiliary tools support contact pair manipulation, filtration, and quality control for feature-rich 3C+ pipelines.
Protocol-specific tools are available for restriction-based protocols, haplotype-resolved contacts, and single-cell Hi-C.

Main Results:

Benchmarking demonstrates pairtools' advantages in high-performance and flexible 3C+ data analysis compared to existing pipelines.
The modular CLI design allows for flexible chaining of operations into complex analysis workflows.
Integration with Python data analysis libraries enhances pairtools' versatility.

Conclusions:

Pairtools provides a robust and adaptable foundation for diverse 3C+ data analysis pipelines.
Its efficiency and flexibility address the computational challenges posed by large-scale 3D genome organization data.