Results 1 - 10
of
89
M (2010) Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol 28: 817–825
"... A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation, and the onset of disease. While some modifications have been linked with activity levels of different functional elements, their combinatorial ..."
Abstract
-
Cited by 172 (12 self)
- Add to MetaCart
A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation, and the onset of disease. While some modifications have been linked with activity levels of different functional elements, their combinatorial patterns remain unresolved, and their potential for systematic de novo genome annotation remains untapped. In this paper, we systematically discover and characterize recurrent spatially-coherent and biologically-meaningful chromatin mark combinations, or chromatin states, in human T-cells. We describe 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific functional, experimental, conservation, annotation, and sequence-motif enrichments, revealing their distinct candidate biological roles. Overall, our work provides a complementary functional annotation of the human genome revealing the genome-wide locations of diverse classes of epigenetic functions, including previously-unsuspected chromatin states enriched in transcription end sites, distinct repeat families, and disease-SNP-associated states. While the primary DNA sequence of the human genome is ultimately responsible for the
S (2009) Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution. PLoS Comput Biol 5: e1000299. doi: 10.1371/journal.pcbi.1000299 PMID
"... Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs) and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models o ..."
Abstract
-
Cited by 23 (3 self)
- Add to MetaCart
(Show Context)
Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs) and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs) and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i) the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the
Finding regulatory DNA motifs using alignment-free
, 2009
"... evolutionary conservation information ..."
(Show Context)
Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks
- Genome Res
, 2012
"... integrative inference of transcriptional networks byDrosophila melanogasterPredictive regulatory models in ..."
Abstract
-
Cited by 20 (3 self)
- Add to MetaCart
(Show Context)
integrative inference of transcriptional networks byDrosophila melanogasterPredictive regulatory models in
On the source-identification method
- Journal of the Acoustical Society of America
, 1998
"... matrices-value-based regulatory motif discovery using positional weightP ..."
Abstract
-
Cited by 18 (0 self)
- Add to MetaCart
(Show Context)
matrices-value-based regulatory motif discovery using positional weightP
interview by Author
- IIT Developmemnt
, 2009
"... NOT THE PUBLISHED VERSION; this is the author’s final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page. ..."
Abstract
-
Cited by 16 (0 self)
- Add to MetaCart
NOT THE PUBLISHED VERSION; this is the author’s final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.
Task Modelling
- of CIGRE HVDC Benchmark System Using PSCAD/EMTDC and PSB/SIMULINK. IEEE Trans. Power Delivery. 2006
, 1997
"... transcriptional regulatory networks using network evolutionary ..."
Abstract
-
Cited by 14 (4 self)
- Add to MetaCart
(Show Context)
transcriptional regulatory networks using network evolutionary
M: Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. 2012, Submitted. doi:10.1186/gb-2012-13-9-r48 Cite this article as: Yip et al.: Classification of human genomic regions based on experimentally determined
- Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research
"... experiments ..."
Detecting gene clusters under evolutionary constraint in a large number of genomes. Bioinformatics 2009;25:571–7
"... Motivation: Spatial clusters of genes conserved across multiple genomes provide important clues to gene functions and evolution of genome organization. Existing methods of identifying these clusters often made restrictive assumptions, such as exact conservation of gene order, and relied on heuristic ..."
Abstract
-
Cited by 7 (0 self)
- Add to MetaCart
(Show Context)
Motivation: Spatial clusters of genes conserved across multiple genomes provide important clues to gene functions and evolution of genome organization. Existing methods of identifying these clusters often made restrictive assumptions, such as exact conservation of gene order, and relied on heuristic algorithms. Results: We developed a very efcient algorithm based on a gene teams model that allows genes in the clusters to appear in different orders. This allows us to detect conserved gene clusters under exible evolutionary constraints in a large number of genomes. Our statistical evaluation incorporates the evolutionary relationship among genomes, a key aspect that has been missing in most previous studies. We conducted a large scale analysis of 133 bacterial genomes. Our results conrm that our approach is an effective way of uncovering functionally related genes. The comparison with known operons and the analysis of the structural properties of our predicted clusters suggest that operons are an important source of constraint, but there are also other forces that determine evolution of gene order and arrangement. Using our method, we predicted functions of many poorly characterized genes in bacterial. The combined algorithmic and statistical methods we present here provide a rigorous framework for systematically studying evolutionary constraints of genomic contexts. Availability: The software, data and the full results of this paper are available online at
Functional Characterization of Transcription Factor Motifs Using Cross-species Comparison across Large Evolutionary Distances
"... We address the problem of finding statistically significant associations between cis-regulatory motifs and functional gene sets, in order to understand the biological roles of transcription factors. We develop a computational framework for this task, whose features include a new statistical score fo ..."
Abstract
-
Cited by 6 (0 self)
- Add to MetaCart
(Show Context)
We address the problem of finding statistically significant associations between cis-regulatory motifs and functional gene sets, in order to understand the biological roles of transcription factors. We develop a computational framework for this task, whose features include a new statistical score for motif scanning, the use of different scores for predicting targets of different motifs, and new ways to deal with redundancies among significant motif–function associations. This framework is applied to the recently sequenced genome of the jewel wasp, Nasonia vitripennis, making use of the existing knowledge of motifs and gene annotations in another insect genome, that of the fruitfly. The framework uses cross-species comparison to improve the specificity of its predictions, and does so without relying upon non-coding sequence alignment. It is therefore well suited for comparative genomics across large evolutionary divergences, where existing alignment-based methods are not applicable. We also apply the framework to find motifs associated with socially regulated gene sets in the honeybee,