Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations

Kel, Alexander; Konovalova, Tatiana; Waleev, T.; Cheremushkin, Evgeny; Kel-Margoulis, Olga V.; Wingender, Edgar

doi:10.1093/bioinformatics/btl041

Cited by 48 publications

(57 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the six-cluster level, GATA binding sites are enriched in C6-1, present on 62% of promoters in that cluster. Further examination of enrichment of TFBS for GATA in conjunction with binding sites for coregulators, using predictive tools for composite binding site analysis (24,31), may help elucidate the role of this complex family of regulators in erythroid development. This type of composite TFBS analysis also may be necessary to determine whether the segregation of the adult and fetal globin genes into different clusters at the six-cluster level (adult in C6-2, fetal in C6-6) is due to factors critical to developmental-specific regulation of globin expression.…”

Section: Some Critical Regulators Not Identified Using Trnamentioning

confidence: 99%

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

Keller¹,

Addya²,

Vadigepalli

et al. 2006

Physiological Genomics

View full text Add to dashboard Cite

We used an in vitro erythroid differentiation system to study the developing red blood cell transcriptome derived from adult CD34ϩ hematopoietic progenitor cells. mRNA expression profiling was used to characterize developing erythroid cells at six time points during differentiation (days 1, 3, 5, 7, 9, and 11). Eleven thousand seven hundred sixty-three genes (20,963 Affymetrix probe sets) were expressed on day 1, and 1,504 genes, represented by 1,953 probe sets, were differentially expressed (DE) with 537 upregulated and 969 downregulated. A subset of the DE genes was validated using realtime RT-PCR. The DE probe sets were subjected to a cluster metric and could be divided into two, three, four, five, or six clusters of genes with different expression patterns in each cluster. Genes in these clusters were examined for shared transcription factor binding sites (TFBS) in their promoters by comparing enrichment of each TFBS relative to a reference set using transcriptional regulatory network analysis. The sets of TFBS enriched in genes up-and downregulated during erythropoiesis were distinct. This analysis identified transcriptional regulators critical to erythroid development, factors recently found to play a role, as well as a new list of potential candidates, including Evi-1, a potential silencer of genes upregulated during erythropoiesis. Thus this transcriptional regulatory network analysis has yielded a focused set of factors and their target genes whose role in differentiation of the hematopoietic stem cell into distinct blood cell lineages can be elucidated.

show abstract

Section: Some Critical Regulators Not Identified Using Trnamentioning

confidence: 99%

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

Keller¹,

Addya²,

Vadigepalli

et al. 2006

Physiological Genomics

View full text Add to dashboard Cite

show abstract

“…If the identity of TFs active in the cell type of interest and their motifs is known, the predictive power of the methods increases for that cell type [144][145][146][147][148][149][150]. In a complementary approach, the loci of genes with a similar function can be searched for common TF binding sites [151][152][153][154]. In such approaches, TFs specific to that function can also be learned.…”

Section: Human Tsssmentioning

confidence: 99%

“…Drosophila developmental genes; human muscle data HexDiff [168] The frequency of all hexamers is computed for known CRMs and a set of control sequences; hexamers which have a higher frequency in CRMs are chosen and used to build a linear model which can be used to scan and score new DNA windows Drosophila developmental data CMA [153] Upstream regions of co-regulated genes are modelled as a combination of one of more composite modules, each of which contains one TF binding site or a pair (from a library of PWMs) constrained by a spacer length distribution and orientation Human T-cell data; yeast cell-cycle data EI [152], DiRE [178] A linear model based on motifs from a library of TFs is learned, which selects combinations of motifs in conserved regions of loci of coexpressed genes, while simultaneously learning regions most likely to be CRMs…”

Section: Drosophila Developmental Datamentioning

confidence: 99%

Identifying regulatory elements in eukaryotic genomes

Narlikar

Ovcharenko²

2009

Briefings in Functional Genomics and Proteomics

View full text Add to dashboard Cite

Proper development and functioning of an organism depends on precise spatial and temporal expression of all its genes. These coordinated expression-patterns are maintained primarily through the process of transcriptional regulation. Transcriptional regulation is mediated by proteins binding to regulatory elements on the DNA in a combinatorial manner, where particular combinations of transcription factor binding sites establish specific regulatory codes. In this review, we survey experimental and computational approaches geared towards the identification of proximal and distal gene regulatory elements in the genomes of complex eukaryotes. Available approaches that decipher the genetic structure and function of regulatory elements by exploiting various sources of information like gene expression data, chromatin structure, DNA-binding specificities of transcription factors, cooperativity of transcription factors, etc. are highlighted. We also discuss the relevance of regulatory elements in the context of human health through examples of mutations in some of these regions having serious implications in misregulation of genes and being strongly associated with human disorders.

show abstract

“…Tools allowing the integration of microarray data with promoter structure information have been developed [104][105][106][107][108][109]. The software developed by Kel [104,106] is commercially available (Explain software, www.biobase.de) and uses a genetic algorithm to predict relevant promoters in a set of given transcripts obtained from microarray analysis, taking advantage of the promoter element matrix database TRANSFAC [110][111][112].…”

Section: Promoter Analysismentioning

confidence: 99%

“…The software developed by Kel [104,106] is commercially available (Explain software, www.biobase.de) and uses a genetic algorithm to predict relevant promoters in a set of given transcripts obtained from microarray analysis, taking advantage of the promoter element matrix database TRANSFAC [110][111][112]. Werner software [107,108] is also a commercial tool where promoter elements are identified using MatInspector [113].…”

Section: Promoter Analysismentioning

confidence: 99%

Microarray data analysis and mining approaches

Cordero

Botta²,

Calogero³

2008

Briefings in Functional Genomics and Proteomics

View full text Add to dashboard Cite

Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology. A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions.

show abstract

Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations

Abstract: The CMA program is freely available for non-commercial users. URL http://www.gene-regulation.com/pub/programs.html#CMAnalyst. It is also a part of the commercial system ExPlain (www.biobase.de) designed for causal analysis of gene expression data..

Cited by 48 publications

References 23 publications

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

Identifying regulatory elements in eukaryotic genomes

Microarray data analysis and mining approaches

Contact Info

Product

Resources

About