Title |
HiCdat: a fast and easy-to-use Hi-C data analysis tool
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Published in |
BMC Bioinformatics, September 2015
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DOI | 10.1186/s12859-015-0678-x |
Pubmed ID | |
Authors |
Marc W. Schmid, Stefan Grob, Ueli Grossniklaus |
Abstract |
The study of nuclear architecture using Chromosome Conformation Capture (3C) technologies is a novel frontier in biology. With further reduction in sequencing costs, the potential of Hi-C in describing nuclear architecture as a phenotype is only about to unfold. To use Hi-C for phenotypic comparisons among different cell types, conditions, or genetic backgrounds, Hi-C data processing needs to be more accessible to biologists. HiCdat provides a simple graphical user interface for data pre-processing and a collection of higher-level data analysis tools implemented in R. Data pre-processing also supports a wide range of additional data types required for in-depth analysis of the Hi-C data (e.g. RNA-Seq, ChIP-Seq, and BS-Seq). HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 2% |
Germany | 1 | <1% |
France | 1 | <1% |
Italy | 1 | <1% |
Portugal | 1 | <1% |
Denmark | 1 | <1% |
Russia | 1 | <1% |
Spain | 1 | <1% |
United States | 1 | <1% |
Other | 0 | 0% |
Unknown | 123 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 37 | 28% |
Researcher | 28 | 21% |
Student > Master | 16 | 12% |
Student > Bachelor | 13 | 10% |
Professor | 7 | 5% |
Other | 18 | 14% |
Unknown | 14 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 46 | 35% |
Biochemistry, Genetics and Molecular Biology | 43 | 32% |
Computer Science | 18 | 14% |
Engineering | 2 | 2% |
Medicine and Dentistry | 2 | 2% |
Other | 5 | 4% |
Unknown | 17 | 13% |