PMID- 25690896
OWN - NLM
STAT- MEDLINE
DCOM- 20150721
LR  - 20240322
IS  - 1362-4962 (Electronic)
IS  - 0305-1048 (Print)
IS  - 0305-1048 (Linking)
VI  - 43
IP  - 8
DP  - 2015 Apr 30
TI  - Inferential modeling of 3D chromatin structure.
PG  - e54
LID - 10.1093/nar/gkv100 [doi]
AB  - For eukaryotic cells, the biological processes involving regulatory DNA elements 
      play an important role in cell cycle. Understanding 3D spatial arrangements of 
      chromosomes and revealing long-range chromatin interactions are critical to 
      decipher these biological processes. In recent years, chromosome conformation 
      capture (3C) related techniques have been developed to measure the interaction 
      frequencies between long-range genome loci, which have provided a great 
      opportunity to decode the 3D organization of the genome. In this paper, we 
      develop a new Bayesian framework to derive the 3D architecture of a chromosome 
      from 3C-based data. By modeling each chromosome as a polymer chain, we define the 
      conformational energy based on our current knowledge on polymer physics and use 
      it as prior information in the Bayesian framework. We also propose an 
      expectation-maximization (EM) based algorithm to estimate the unknown parameters 
      of the Bayesian model and infer an ensemble of chromatin structures based on 
      interaction frequency data. We have validated our Bayesian inference approach 
      through cross-validation and verified the computed chromatin conformations using 
      the geometric constraints derived from fluorescence in situ hybridization (FISH) 
      experiments. We have further confirmed the inferred chromatin structures using 
      the known genetic interactions derived from other studies in the literature. Our 
      test results have indicated that our Bayesian framework can compute an accurate 
      ensemble of 3D chromatin conformations that best interpret the distance 
      constraints derived from 3C-based data and also agree with other sources of 
      geometric constraints derived from experimental evidence in the previous studies. 
      The source code of our approach can be found in 
      https://github.com/wangsy11/InfMod3DGen.
CI  - (c) The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic 
      Acids Research.
FAU - Wang, Siyu
AU  - Wang S
AD  - Department of Automation, Tsinghua University, Beijing 100084, P.R. China.
FAU - Xu, Jinbo
AU  - Xu J
AD  - Toyota Technological Institute at Chicago, 6045 S Kenwood, IL 60637, USA.
FAU - Zeng, Jianyang
AU  - Zeng J
AD  - Institute for Interdisciplinary Information Sciences, Tsinghua University, 
      Beijing 100084, P.R. China MOE Key Laboratory of Bioinformatics, Tsinghua 
      University, Beijing 100084, P.R. China zengjy321@tsinghua.edu.cn.
LA  - eng
GR  - R01 GM089753/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150217
PL  - England
TA  - Nucleic Acids Res
JT  - Nucleic acids research
JID - 0411011
RN  - 0 (Chromatin)
SB  - IM
MH  - Algorithms
MH  - Bayes Theorem
MH  - Chromatin/*chemistry
MH  - *Models, Molecular
PMC - PMC4417147
EDAT- 2015/02/19 06:00
MHDA- 2015/07/22 06:00
PMCR- 2015/02/17
CRDT- 2015/02/19 06:00
PHST- 2015/01/30 00:00 [accepted]
PHST- 2014/12/25 00:00 [received]
PHST- 2015/02/19 06:00 [entrez]
PHST- 2015/02/19 06:00 [pubmed]
PHST- 2015/07/22 06:00 [medline]
PHST- 2015/02/17 00:00 [pmc-release]
AID - gkv100 [pii]
AID - 10.1093/nar/gkv100 [doi]
PST - ppublish
SO  - Nucleic Acids Res. 2015 Apr 30;43(8):e54. doi: 10.1093/nar/gkv100. Epub 2015 Feb 
      17.