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10.1093/bioinformatics/btw240 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btw240 C5013904!5013904 !27153668     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27153668 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Bioinformatics 2016 ; 32 (17 ): 2692-5 Nephropedia Template TP {{tp|p=27153668 |t=2016. FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data |pdf=|usr=}}{{27153668 }} gab.com Text FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27153668 #FOAvip MOTIVATION: How chromatin folds in three-dimensional (3D) space is closely related to transcription regulation. As powerful tools to study such 3D chromatin conformation, the recently developed Hi-C technologies enable a genome-wide measurement of pair-wise chromatin interaction. However, methods for the detection of biologically meaningful chromatin interactions, i.e. peak calling, from Hi-C data, are still under development. 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 loci manifesting in high resolution Hi-C data, achieves substantially improved robustness and enhanced statistical power in peak calling. Superior to peak callers that ignore spatial dependency both methodologically and in performance, our previous Bayesian framework suffers from heavy computational costs due to intensive computation incurred by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure. RESULTS: In this work, we have developed FastHiC, a novel approach based on simulated field approximation, which approximates the joint distribution of the hidden peak status by a set of independent random variables, leading to more tractable computation. Performance comparisons in real data analysis showed that FastHiC not only speeds up our original Bayesian method by more than five times, bus also achieves higher peak calling accuracy. AVAILABILITY AND IMPLEMENTATION: FastHiC is freely accessible at:http://www.unc.edu/?yunmli/FastHiC/ CONTACTS: : yunli@med.unc.edu or ming.hu@nyumc.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27153668 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27153668 #FOAvip English Wikipedia <ref>{{Cite pmid|27153668 }}</ref> FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data #MMPMID27153668 Xu Z ; Zhang G ; Wu C ; Li Y ; Hu M Bioinformatics 2016[Sep]; 32 (17 ): 2692-5 PMID27153668 show ga MOTIVATION: How chromatin folds in three-dimensional (3D) space is closely related to transcription regulation. As powerful tools to study such 3D chromatin conformation, the recently developed Hi-C technologies enable a genome-wide measurement of pair-wise chromatin interaction. However, methods for the detection of biologically meaningful chromatin interactions, i.e. peak calling, from Hi-C data, are still under development. 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 loci manifesting in high resolution Hi-C data, achieves substantially improved robustness and enhanced statistical power in peak calling. Superior to peak callers that ignore spatial dependency both methodologically and in performance, our previous Bayesian framework suffers from heavy computational costs due to intensive computation incurred by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure. RESULTS: In this work, we have developed FastHiC, a novel approach based on simulated field approximation, which approximates the joint distribution of the hidden peak status by a set of independent random variables, leading to more tractable computation. Performance comparisons in real data analysis showed that FastHiC not only speeds up our original Bayesian method by more than five times, bus also achieves higher peak calling accuracy. AVAILABILITY AND IMPLEMENTATION: FastHiC is freely accessible at:http://www.unc.edu/?yunmli/FastHiC/ CONTACTS: : yunli@med.unc.edu or ming.hu@nyumc.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |*Algorithms [MESH] |*Chromatin [MESH] |Bayes Theorem [MESH] |Chromosome Mapping [MESH] |Chromosomes [MESH] |Computer Simulation [MESH] |Genome [MESH] |Models, Theoretical [MESH]FastHiC: a fast and accurate algorithm to detect long-range chromosoma(epmc).pdf DeepDyve Pubget Overpricing