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Bayesian Inference of Spatial Organizations of Chromosomes

Ming Hu, Ke Deng, Zhaohui Qin, Jesse Dixon, Siddarth Selvaraj, Jennifer Fang, Bing Ren, Jun S. Liu, Amos Tanay
2013 PLoS Computational Biology  
Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell.  ...  Here we describe a novel Bayesian probabilistic approach, denoted as "Bayesian 3D constructor for Hi-C data" (BACH), to infer the consensus 3D chromosomal structure.  ...  (G) Spatial organization of H3K36me3. OR = 10.91, p-value = 1.0e-7. (H) Spatial organization of H3K27me3. OR = 2.17, p-value = 0.0706. (I) Spatial organization of H3K4me3.  ... 
doi:10.1371/journal.pcbi.1002893 pmid:23382666 pmcid:PMC3561073 fatcat:znpjgb7vnngnnhgcxk2qbn6hde

Inferential modeling of 3D chromatin structure

Siyu Wang, Jinbo Xu, Jianyang Zeng
2015 Nucleic Acids Research  
to decode the 3D organization of the genome.  ...  Understanding 3D spatial arrangements of chromosomes and revealing longrange chromatin interactions are critical to decipher these biological processes.  ...  We thank Mr Huichao Gong, Mr Chao He and Mr Yuexin Wu for their helpful discussions at the early stage of this research.  ... 
doi:10.1093/nar/gkv100 pmid:25690896 pmcid:PMC4417147 fatcat:adrcqsbbnza7blmslvvennwbuy

Bayesian Reconstruction of Chromatin Conformation from FISH and Hi-C Data

Keyao Pan, Mark Bathe
2013 Biophysical Journal  
Single-cell imaging and chromosome conformation capture-based techniques provide information on chromosome conformations and their spatial organizations.  ...  Here, we present such objective, Bayesian procedure that infers the least-biased distribution of chromatin conformational states from FISH and Hi-C datasets accounting both for heterogeneity in the underlying  ... 
doi:10.1016/j.bpj.2012.11.3228 fatcat:xqyixntkbjg3hjn3cllyrvqixm

Hierarchical Markov Random Field model captures spatial dependency in gene expression, demonstrating regulation via the 3D genome [article]

Naihui Zhou, Iddo Friedberg, Mark S Kaiser
2019 bioRxiv   pre-print
The inference of PhiMRF follows a Bayesian framework, and we introduce the Spatial Interaction Estimate (SIE) to measure the strength of spatial dependency in gene expression.  ...  We also report high inter-chromosomal spatial correlations in the majority of chromosome pairs, as well as the whole genome.  ...  Bayesian Inference 244 The goal of our Bayesian framework is to simulate from the posterior distributions p(α|y), p(η|y) and p(τ 2 |y).  ... 
doi:10.1101/2019.12.16.878371 fatcat:og5mvg3mfna6fhhwjiwyo2lq6q

Computational inference of physical spatial organization of eukaryotic genomes

Bingxiang Xu, Zhihua Zhang
2016 Quantitative Biology  
Here, we review the current computational models that simulate chromosome dynamics, and explain the physical and topological properties of chromosomal conformation, as inferred from these newly generated  ...  Results: Thanks to recent technological developments, we can now probe chromatin interaction in substantial detail, boosting research interest in modeling genome spatial organization.  ...  This work was supported by grants from the National Nature Science COMPLIANCE WITH ETHICS GUIDELINES The authors Bingxiang Xu and Zhihua Zhang declare that they have no conflict of interests.  ... 
doi:10.1007/s40484-016-0082-1 fatcat:qqmw3srpsfa4rn4gqfzosy66qy

Seevolution: visualizing chromosome evolution

A. Esteban-Marcos, A. E. Darling, M. A. Ragan
2009 Bioinformatics  
As methods to infer evolutionary histories of genomes become increasingly complex, visualization of the evolutionary process will not only be useful for communication, but will also serve as an exploratory  ...  Multiple organisms related by a phylogeny can be visualized simultaneously.  ...  Each segment can be assigned a color and a texture, and Seevolution uses these colors to communicate information about breakpoints of rearrangement, spatial organization of the chromosome such as distance  ... 
doi:10.1093/bioinformatics/btp096 pmid:19233896 pmcid:PMC2660879 fatcat:bf6xmxxzxndmllnwnqgsd4y72a

Bayesian inference of chromatin structure ensembles from population-averaged contact data

Simeon Carstens, Michael Nilges, Michael Habeck
2020 Proceedings of the National Academy of Sciences of the United States of America  
Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way.  ...  Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation.  ...  our approach to deliver insights into chromatin organization of great biological relevance. chromosome conformation capture | genome structure modeling | Bayesian inference | model comparison I n recent  ... 
doi:10.1073/pnas.1910364117 pmid:32193349 fatcat:acuu3ksa3ra7nd5y6fldc5325e

Optical Tweezers Controlled Nanopore Detection of Nucleosomes along a DNA

Gautam Soni, Cees Dekker
2013 Biophysical Journal  
Single-cell imaging and chromosome conformation capture-based techniques provide information on chromosome conformations and their spatial organizations.  ...  Here, we present such objective, Bayesian procedure that infers the least-biased distribution of chromatin conformational states from FISH and Hi-C datasets accounting both for heterogeneity in the underlying  ... 
doi:10.1016/j.bpj.2012.11.3225 fatcat:of6lliqglnedhhrfz4l73wv4o4

The Architects of the Archaeal Chromatin

Rosalie Driessen, Ramon van der Valk, Geri Moolenaar, Nora Goosen, Remus Dame
2013 Biophysical Journal  
Single-cell imaging and chromosome conformation capture-based techniques provide information on chromosome conformations and their spatial organizations.  ...  Here, we present such objective, Bayesian procedure that infers the least-biased distribution of chromatin conformational states from FISH and Hi-C datasets accounting both for heterogeneity in the underlying  ... 
doi:10.1016/j.bpj.2012.11.3226 fatcat:jlo4hhxrmnednean75kjaqfuii

Population Properties of Self-Avoiding Polymer Chain Models of Chromosomes in a Confined Space of Nucleus

Gamze Gursoy, Yun Xu, Amy Kenter, Jie Liang
2013 Biophysical Journal  
Single-cell imaging and chromosome conformation capture-based techniques provide information on chromosome conformations and their spatial organizations.  ...  Here, we present such objective, Bayesian procedure that infers the least-biased distribution of chromatin conformational states from FISH and Hi-C datasets accounting both for heterogeneity in the underlying  ... 
doi:10.1016/j.bpj.2012.11.3229 fatcat:dd3stakm7zeatcwo4q4ys7fj5a

Modeling Long Chromatin Fibers based on In-Vivo Nucleosome Positioning Maps

Robert Schöpflin, Vladimir B. Teif, Oliver Müller, Christin Weinberg, Karsten Rippe, Gero Wedemann
2013 Biophysical Journal  
Single-cell imaging and chromosome conformation capture-based techniques provide information on chromosome conformations and their spatial organizations.  ...  Here, we present such objective, Bayesian procedure that infers the least-biased distribution of chromatin conformational states from FISH and Hi-C datasets accounting both for heterogeneity in the underlying  ... 
doi:10.1016/j.bpj.2012.11.3227 fatcat:sfycdykbfnhkhb27stuctenq54

FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data

Zheng Xu, Guosheng Zhang, Cong Wu, Yun Li, Ming Hu
2016 Bioinformatics  
by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure.  ...  In our previous work, we have developed a novel hidden Markov random field (HMRF) based Bayesian method, which through explicitly modeling the non-negligible spatial dependency among adjacent pairs of  ...  Introduction The spatial organizations of chromosomes play a critical role in transcription regulation.  ... 
doi:10.1093/bioinformatics/btw240 pmid:27153668 pmcid:PMC5013904 fatcat:px6xcpuy5vb6be3vuako2nvltq

Predicting the spatial organization of chromosomes using epigenetic data

Raphaël Mourad, Olivier Cuvier
2015 Genome Biology  
Chromosome folding can reinforce the demarcation between euchromatin and heterochromatin.  ...  Such computational approaches reinforce the idea of a linkage between epigenetically marked chromatin domains and their segregation into distinct compartments at the megabase scale or topological domains  ...  Introduction The ability to probe the spatial organization of chromosomes through the combination of chromosome conformation capture methods with high-throughput sequencing (3C-Hi-C) has revealed how chromosomes  ... 
doi:10.1186/s13059-015-0752-8 pmid:26319942 pmcid:PMC4552987 fatcat:qdh4rowbcvbjjh56xxremyluji

Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model

Philippe Broët, Sylvia Richardson
2006 Computer applications in the biosciences : CABIOS  
Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them.  ...  between sequences along the chromosome.  ...  Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/btl035 pmid:16455750 fatcat:jn5ugv7tqfbvne7an3wjvnwna4

GEM: A manifold learning based framework for reconstructing spatial organizations of chromosomes [article]

Guangxiang Zhu, Wenxuan Deng, Hailin Hu, Rui Ma, Sai Zhang, Jinglin Yang, Jian Peng, Tommy Kaplan, Jianyang Zeng
2017 bioRxiv   pre-print
Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation.  ...  organizations of chromosomes from Hi-C data.  ...  In [19] , an expectation-maximization based algorithm was proposed to infer the 3D chromatin organizations under a Bayesian inference framework.  ... 
doi:10.1101/161208 fatcat:nkx7kzvqazhdrdwjlv57lhpk3q
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