MICC: An R package for identifying chromatin interactions from ChIA-PET data

Bioinformatics

Volumn 31, Issue 23, 2015, Pages 3832-3834

  He, Chao ^a   Zhang, Michael Q ^a,b   Wang, Xiaowo ^a  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHROMATIN;

ALGORITHM; BAYES THEOREM; CHROMATIN; CHROMATIN IMMUNOPRECIPITATION; COMPUTER LANGUAGE; COMPUTER SIMULATION; DNA SEQUENCE; GENETICS; HUMAN; METABOLISM; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; CHROMATIN; CHROMATIN IMMUNOPRECIPITATION; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; PROGRAMMING LANGUAGES; SEQUENCE ANALYSIS, DNA;

EID: 84950235577     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv445     Document Type: Article

References (6)

1. Amartya, S. et al. (2012) The long-range interaction landscape of gene promoters. Nature, 489, 109-113.
2. Fullwood, M. et al. (2009) An oestrogen-receptor-α-bound human chromatin interactome. Nature, 462, 58-64.
3. Jessen, B. and Winter, A. (1935) Distribution functions and the Riemann zeta function. Trans. Am. Math. Soc., 38, 48-88.
4. Li, G. et al. (2010) ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol., 11, 1-13.
5. Li, G. et al. (2012) Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell, 148, 84-98.
6. Paulsen, J. et al. (2015) A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions. Nucleic Acids Res., 42, e143.