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Modeling transcription factor binding sites with Gibbs Sampling and Minimum Description Length encoding

J Schug, G C Overton
1997 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
We have developed a method using Gibbs Sampling and the Minimum Description Length principle for automatically and reliably creating weight matrix models of binding sites from a database (TRANSFAC) of  ...  Here we describe the foundation for the methods we will use to develop weight matrix models for transcription factor binding sites.  ...  The authors wish to thank Chip Lawrence for many enlightening conversations and suggestions and the ISMB-97 referees for their helpful comments. 1  ... 
pmid:9322048 fatcat:idflojdn6jcmdiyjzsnzqpafjy

Human-mouse genome comparisons to locate regulatory sites

Wyeth W. Wasserman, Michael Palumbo, William Thompson, James W. Fickett, Charles E. Lawrence
2000 Nature Genetics  
second, the discovery of transcription-factor binding sites (response elements) from expression data has not yet been generalized from single-celled organisms to multicellular organisms.  ...  Also we found that in using this restriction, the binding specificities of all three major muscle-specific transcription factors (MYF, SRF and MEF2) can be computationally identified.  ...  Acknowledgements We thank our colleagues at SmithKline Beecham and the Wadsworth Center for input, and the Computational Molecular Biology Core at the Wadsworth Center and I. Auger for assistance.  ... 
doi:10.1038/79965 pmid:11017083 fatcat:bz6mrkcrdfeifnbri5iibc372i

Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements

Kannan Tharakaraman, Leonardo Mariño-Ramírez, Sergey L Sheetlin, David Landsman, John L Spouge
2006 BMC Bioinformatics  
Datasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM.  ...  We describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling.  ...  Alignments of transcription factor binding sites Figure 3 Alignments of transcription factor binding sites.  ... 
doi:10.1186/1471-2105-7-408 pmid:16961919 pmcid:PMC1599759 fatcat:cc25tgoj7jdwragxyfn5uualo4

GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif

Chao Pei, Shu-Lin Wang, Jianwen Fang, Wei Zhang
2017 Journal of Computational Biology  
Transcription factors (TFs) play a key role in gene regulation by binding to target promoter sequences.  ...  Gibbs Sampling with Maximal Cliques for hunting DNA Motif).  ...  Identification of regulatory elements, especially the binding sites in DNA for transcription factors (TFs), is a vital task in this goal (Das and Dai, 2007) .  ... 
doi:10.1089/cmb.2017.0100 pmid:29116820 pmcid:PMC5749607 fatcat:x5rd4ww5ojdzxbuxe3zd6hg36u

A phylogenetic Gibbs sampler that yields centroid solutions for cis-regulatory site prediction

Lee A. Newberg, William A. Thompson, Sean Conlan, Thomas M. Smith, Lee Ann McCue, Charles E. Lawrence
2007 Computer applications in the biosciences : CABIOS  
Results: We describe here a Gibbs sampler that employs a full phylogenetic model and reports an ensemble centroid solution.  ...  Availability: The software is freely available at http://bayesweb.  ...  , R01-HG01257 to CEL, and 2P20-RR01-5578-06 to Walter Atwood.  ... 
doi:10.1093/bioinformatics/btm241 pmid:17488758 pmcid:PMC2268014 fatcat:si7j4ssenfew7dbizsirtzuyxq

Identifying target sites for cooperatively binding factors

D. GuhaThakurta, G. D. Stormo
2001 Bioinformatics  
The method utilizes a Gibbs sampling strategy to model the cooperativity between two transcription factors and defines position weight matrices for the binding sites.  ...  In cases where binding site patterns are weak and cannot be identified by other available methods, Co-Bind, by virtue of modeling the cooperativity between factors, can identify those sites efficiently  ...  We thank Chip Lawrence for providing the 1.01.009 version of Gibbs Motif Sampler and instructions on its use. We thank Xiaole Liu and Jun Liu for providing the BioProspector program.  ... 
doi:10.1093/bioinformatics/17.7.608 pmid:11448879 fatcat:fsjtxfw7k5gttorq6u5qqhoya4

Functional bioinformatics of microarray data: from expression to regulation

Y. Moreau, F. De Smet, G. Thijs, K. Marchal, B. De Moor
2002 Proceedings of the IEEE  
As a result similar patterns of expression can correspond to similar binding site patterns. Here we integrate clustering of coexpressed genes with the discovery of binding motifs.  ...  We overview the different techniques for motif finding, in particular the technique of Gibbs sampling, and we present several extensions of this technique in our Motif Sampler.  ...  Rouzé and S.  ... 
doi:10.1109/jproc.2002.804681 fatcat:nwo7ktqb6nam7mwgcjyaludtrm

Rhodopseudomonas palustris Regulons Detected by Cross-Species Analysis of Alphaproteobacterial Genomes

S. Conlan, C. Lawrence, L. A. McCue
2005 Applied and Environmental Microbiology  
In the first step, 4,963 putative transcription factor binding sites, upstream of 2,044 genes and operons, were identified using cross-species Gibbs sampling.  ...  With this goal in mind, we predicted regulatory signals genomewide, using a two-step phylogenetic-footprinting and clustering process that we had developed previously.  ...  the Gibbs sampler, Michael Palumbo for assistance with the BMC software, and Caroline Harwood for comments on the manuscript.  ... 
doi:10.1128/aem.71.11.7442-7452.2005 pmid:16269786 pmcid:PMC1287613 fatcat:wwwouhkwvbditopg2inwmspgoe

Parametric bootstrapping for biological sequence motifs

Patrick K. O'Neill, Ivan Erill
2016 BMC Bioinformatics  
As a proof of concept, we employ these sampling methods to analyze a broad collection of prokaryotic and eukaryotic transcription factor binding site motifs.  ...  As a second application, we apply maximum entropy sampling to the motif p-value problem and use it to give elementary derivations of two new estimators.  ...  Acknowledgements The authors wish to thank Sefa Kılıç for assistance with data collection and motif structure detection, Rory Donovan for advice on a preliminary study that led to the present work, and  ... 
doi:10.1186/s12859-016-1246-8 pmid:27716039 pmcid:PMC5052923 fatcat:onb5q7gc5nc2jg7axrhdpja4k4

A model for sequential evolution of ligands by exponential enrichment (SELEX) data

Juli Atherton, Nathan Boley, Ben Brown, Nobuo Ogawa, Stuart M. Davidson, Michael B. Eisen, Mark D. Biggin, Peter Bickel
2012 Annals of Applied Statistics  
We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target.  ...  Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment.  ...  Thanks to John Atherton, Stephanie Atherton and Alex Glazer for helpful discussions regarding physical chemistry. SUPPLEMENTARY MATERIAL Code for SELEX model (DOI: 10.1214/12-AOAS537SUPP; .pdf).  ... 
doi:10.1214/12-aoas537 fatcat:tnr7lhwqtnedrdxvuemwlv6eki

BayesMD: Flexible Biological Modeling for Motif Discovery

Man-Hung Eric Tang, Anders Krogh, Ole Winther
2008 Journal of Computational Biology  
It is trained on transcription factor (TF) databases in order to extract the typical properties of TF binding sites.  ...  Lastly, we use a prior over the position of binding sites.  ...  ACKNOWLEDGMENTS Thanks to Albin Sandelin for his valuable comments on the manuscript and Thomas Down for sharing his training and assessment datasets.  ... 
doi:10.1089/cmb.2007.0176 pmid:19040368 fatcat:yv6ppbvkkzhc3pomh5zszuq6mu

A computational genomics approach to identify cis-regulatory modules from chromatin immunoprecipitation microarray data--A case study using E2F1

V. X. Jin, A. Rabinovich, S. L. Squazzo, R. Green, P. J. Farnham
2006 Genome Research  
from transcription factor binding sites, a comparative genomics approach, and statistical learning methods to identify transcriptional regulatory modules.  ...  We began with E2F1 binding site information obtained from ChIP-chip analyses of ENCODE regions, from both HeLa and MCF7 cells.  ...  We thank the members of the Farnham laboratory for helpful discussion and Celina Mojica for excellent technical assistance. Finally, we thank the ENCODE Project Consortium for discussion and support.  ... 
doi:10.1101/gr.5520206 pmid:17053090 pmcid:PMC1665642 fatcat:njznlbbgqfdwrebuqzsfiuphxq

Approximate Bayesian computation of transcriptional pausing mechanisms [article]

Jordan Douglas, Richard L Kingston, Alexei J Drummond
2019 bioRxiv   pre-print
This is consistent with a model where the relative Gibbs energies between the pre and posttranslocated positions, and the rate of translocation between the two, is the primary factor behind invoking transcriptional  ...  Under this Bayesian framework, models and their parameters were assessed by their ability to predict the locations of pause sites in the E. coli genome.  ...  S(31, 2) · · · k bind K D ↑↓ k bind [U T P ] S N (31, 1) ↓ · · · Assume that the transcription bubble is described by h = 11, β 1 = 2, and β 2 = 0. 344 The baseline Gibbs energy barrier ∆G ‡ τ = 21 k  ... 
doi:10.1101/748210 fatcat:ivzyz5rcj5ghlla7hlakoii4ya

In Silico Identification of Regulatory Elements in Promoters [chapter]

Vikrant Nain, Shakti Sahi, Polumetla Ananda
2011 Computational Biology and Applied Bioinformatics  
motifs with a sensitivity of >90% for known transcription factor binding sites.  ...  Predictions are based on the integration of binding site prediction generated with high-quality transcription factor models and cross-species comparison filtering (phylogenetic footprinting).  ...  It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information  ... 
doi:10.5772/22230 fatcat:wcruf4x7bvcsbaszaknsiejuxy

Recent Advances in the Computational Discovery of Transcription Factor Binding Sites

Tung Nguyen, Ioannis Androulakis
2009 Algorithms  
Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.  ...  We start with a brief review of the basic approaches for binding site representation and promoter identification, then discuss the techniques to locate physical TFBSs, identify functional binding sites  ...  Acknowledgements The authors acknowledge financial support from the National Institutes of Health (R01GM082974), the National Science Foundation (NSF-BES 0519563) and the EPA (GAD R 832721-010).  ... 
doi:10.3390/a2010582 fatcat:z3ijlhwgofbzjirnupglbtrf2e
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