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HiCUP: pipeline for mapping and processing Hi-C data.

ABSTRACT: HiCUP is a pipeline for processing sequence data generated by Hi-C and Capture Hi-C (CHi-C) experiments, which are techniques used to investigate three-dimensional genomic organisation. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also produces an easy-to-interpret yet detailed quality control (QC) report that assists in refining experimental protocols for future studies. The software is freely available and has already been used for processing Hi-C and CHi-C data in several recently published peer-reviewed studies.

SUBMITTER: Wingett S

PROVIDER: S-EPMC4706059 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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HiCUP: pipeline for mapping and processing Hi-C data.

Wingett Steven S Ewels Philip P Furlan-Magaril Mayra M Nagano Takashi T Schoenfelder Stefan S Fraser Peter P Andrews Simon S

F1000Research 20151120

HiCUP is a pipeline for processing sequence data generated by Hi-C and Capture Hi-C (CHi-C) experiments, which are techniques used to investigate three-dimensional genomic organisation. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also produces an easy-to-interpret yet detailed quality control (QC) report that assists in refining experimental protocols for future studies. The software is freely available and h ...[more]

PMID: 26835000

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HiCUP: pipeline for mapping and processing Hi-C data

Project description:HiCUP is a pipeline for processing sequence data generated by Hi-C, a technique used to investigate the three-dimensional organisation of a genome. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also provides an easy-to-interpret yet detailed quality control report that may be used by researchers to refine their experimental protocol for future studies. The software is freely available and has already been used for processing Hi-C data in several recently published peer-reviewed research articles. This experiment investigates the impact of using HiCUP to remove putative PCR amplification products in heavily duplicated Capture Hi-C libraries. Examination of three Capture Hi-C libraries

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Fine mapping chromatin contacts in capture Hi-C data.

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SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data.

Project description:Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.

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Project description:Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples.Availability and implementationHiTea is available at https://github.com/parklab/HiTea and as a Docker image.Supplementary informationSupplementary data are available at Bioinformatics online.

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