Hi-Corrector
Computational Tool
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General Information
DescriptionA fast, scalable and memory-efficient package for normalizing large-scale Hi-C data
Homepagehttp://zhoulab.usc.edu/Hi-Corrector/
Publications
Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data
Wenyuan Li; Ke Gong; Qingjiao Li; Frank Alber; Xianghong Jasmine Zhou
Summary: Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, resulting in very large matrices of chromatin contacts. Such large-size matrices, however, pose a great challenge on the memory usage and speed of its normalization. Therefore, there is an urgent need for fast and memory-efficient methods for normalization of Hi-C data. We developed Hi-Corrector, an easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability—the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency—the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed—the parallel version can run very fast on multiple computing nodes with limited local memory.
Availability and implementation: The sequential version is implemented in ANSI C and can be easily compiled on any system; the parallel version is implemented in ANSI C with the MPI library (a standardized and portable parallel environment designed for solving large-scale scientific problems). The package is freely available at http://zhoulab.usc.edu/Hi-Corrector/.
Contact: alber@usc.edu or xjzhou@usc.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
DOI: https://doi.org/10.1093/bioinformatics/btu747
Metrics:
8
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