Title |
HiC-Pro: an optimized and flexible pipeline for Hi-C data processing
|
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Published in |
Genome Biology, December 2015
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DOI | 10.1186/s13059-015-0831-x |
Pubmed ID | |
Authors |
Nicolas Servant, Nelle Varoquaux, Bryan R. Lajoie, Eric Viara, Chong-Jian Chen, Jean-Philippe Vert, Edith Heard, Job Dekker, Emmanuel Barillot |
Abstract |
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 . |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 28% |
United Kingdom | 3 | 17% |
Belgium | 2 | 11% |
Japan | 1 | 6% |
Denmark | 1 | 6% |
Unknown | 6 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 50% |
Scientists | 8 | 44% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 2 | <1% |
Japan | 2 | <1% |
Norway | 2 | <1% |
United Kingdom | 2 | <1% |
Sweden | 1 | <1% |
Germany | 1 | <1% |
Switzerland | 1 | <1% |
Denmark | 1 | <1% |
Mexico | 1 | <1% |
Other | 2 | <1% |
Unknown | 842 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 212 | 25% |
Researcher | 129 | 15% |
Student > Master | 71 | 8% |
Student > Bachelor | 71 | 8% |
Student > Doctoral Student | 45 | 5% |
Other | 117 | 14% |
Unknown | 212 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 291 | 34% |
Agricultural and Biological Sciences | 206 | 24% |
Computer Science | 35 | 4% |
Medicine and Dentistry | 20 | 2% |
Neuroscience | 18 | 2% |
Other | 58 | 7% |
Unknown | 229 | 27% |