HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

By N. Servant, Nelle Varoquaux, B. Lajoie, E. Viara, Chongjian Chen, Jean-Philippe Vert, Edith Heard, Job Dekker, E. Barillot. Genome Biology, 2015

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HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/c15f30a3e84910a28cc560e7db097fd99339e8c1

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Citations in the corpus

Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments (2016, neutral): “Although pipelines for Hi-C data analysis exist (Lieberman-Aiden et al., 2009; Schmid et al., 2015; Servant et al., 2015; Sauria et al., 2015), these packages are not designed to process datasets at the terabase scale or to annotate the structural features that these maps reflect.”
Genome-wide mapping and analysis of chromosome architecture (2016, background): “Since ICE was introduced, several efforts have been made to improve its computational efficiency 80,81 .”
Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization (2020, methodology): “Among the above tools, HiC-Pro is the only one with the ability to analyse cHiC and HiChIP data. scHiCNorm (25) and scHiCluster(26) provide support for single-cell Hi-C data normalization and clustering, but suffer from the lack of a tool suite to guide researchers through the workflow of processing”
HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient (2017, positive): “…replicates are available, it is a commonpracticetouseeitherPearsonorSpearmancorrelationco-efficients between the two Hi-C data matrices as a metric for data quality (Hu et al. 2012; Imakaev et al. 2012; Gorkin et al. 2014; Rao et al. 2014; Ay and Noble 2015; Dixon et al. 2015; Servant et al. 2015).”
Cooler: scalable storage for Hi-C data and other genomically-labeled arrays (2019, methodology): “Indeed, for HiC data, a two-file text format based on a bin file and upper triangular element file was introduced by the HiCPro pipeline (30).”