{"title":"Cis regulatory module discovery in immune cell development","authors":"S. R. Ganakammal, M. Kaplan, N. Perumal","doi":"10.1145/1722024.1722039","DOIUrl":null,"url":null,"abstract":"Transcriptional regulatory mechanisms are mediated by a set of transcription factors (TFs), which bind to a specific region (motifs or transcription factor binding sites, TFBS), on the target gene(s) leading to gene expression. Eukaryotic regulatory motifs, referred to as cis regulatory modules (CRMs), tend to co-occur near the regulated gene's transcription start site and provide the building blocks to transcriptional regulatory networks that model the relevant TF-TFBS interactions. Here, we study IL-12 stimulated transcriptional regulators in STAT4 mediated T helper 1 (Th1) cell development by focusing on the identification of TFBS and CRMs using a set of Stat4 ChIP-on-chip target genes. A region containing 2000 bases of Mus musculus sequences with the Stat4 binding site, derived from the ChIP-on-chip data, has been characterized for enrichment of other motifs and, thus CRMs. We find two such motifs, (NF-κB and PPARγ/RXR) being enriched in the Stat4 binding sequences compared to neighboring background sequences and sets of random sequences of equal size. Furthermore, these predicted CRMs were observed to be associated with biologically relevant target genes in the ChIP-on-chip data set by meaningful gene ontology annotations. These analyses will lead to a better understanding of transcriptional regulatory networks in IL-12 stimulated Stat4 mediated Th1 cell differentiation.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722039","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"In Silico Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1722024.1722039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Transcriptional regulatory mechanisms are mediated by a set of transcription factors (TFs), which bind to a specific region (motifs or transcription factor binding sites, TFBS), on the target gene(s) leading to gene expression. Eukaryotic regulatory motifs, referred to as cis regulatory modules (CRMs), tend to co-occur near the regulated gene's transcription start site and provide the building blocks to transcriptional regulatory networks that model the relevant TF-TFBS interactions. Here, we study IL-12 stimulated transcriptional regulators in STAT4 mediated T helper 1 (Th1) cell development by focusing on the identification of TFBS and CRMs using a set of Stat4 ChIP-on-chip target genes. A region containing 2000 bases of Mus musculus sequences with the Stat4 binding site, derived from the ChIP-on-chip data, has been characterized for enrichment of other motifs and, thus CRMs. We find two such motifs, (NF-κB and PPARγ/RXR) being enriched in the Stat4 binding sequences compared to neighboring background sequences and sets of random sequences of equal size. Furthermore, these predicted CRMs were observed to be associated with biologically relevant target genes in the ChIP-on-chip data set by meaningful gene ontology annotations. These analyses will lead to a better understanding of transcriptional regulatory networks in IL-12 stimulated Stat4 mediated Th1 cell differentiation.
In Silico BiologyComputer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍:
The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.