Exploring a Drosophila Transcription Factor Interaction Network to Identify Cis-Regulatory ModulesShow others and affiliations
2020 (English)In: Journal of Computational Biology, ISSN 1066-5277, E-ISSN 1557-8666, Vol. 27, no 8, p. 1313-1328Article in journal (Refereed) Published
Abstract [en]
Multiple transcription factors (TFs) bind to specific sites in the genome and interact among themselves to form the cis-regulatory modules (CRMs). They are essential in modulating the expression of genes, and it is important to study this interplay to understand gene regulation. In the present study, we integrated experimentally identified TF binding sites collected from published studies with computationally predicted TF binding sites to identifyDrosophilaCRMs. Along with the detection of the previously known CRMs, this approach identified novel protein combinations. We determined high-occupancy target sites, where a large number of TFs bind. Investigating these sites revealed that Giant, Dichaete, and Knirp are highly enriched in these locations. A common TAG team motif was observed at these sites, which might play a role in recruiting other TFs. While comparing the binding sites at distal and proximal promoters, we found that certain regulatory TFs, such as Zelda, were highly enriched in enhancers. Our study has shown that, from the information available concerning the TF binding sites, the real CRMs could be predicted accurately and efficiently. Although we only may claim co-occurrence of these proteins in this study, it may actually point to their interaction (as known interaction proteins typically co-occur together). Such an integrative approach can, therefore, help us to provide a better understanding of the interplay among the factors, even though further experimental verification is required.
Place, publisher, year, edition, pages
Mary Ann Liebert, 2020. Vol. 27, no 8, p. 1313-1328
Keywords [en]
cis-regulatory modules, Drosophila transcription factors, high-occupancy target sites, transcription factors association
National Category
Biochemistry Molecular Biology
Identifiers
URN: urn:nbn:se:umu:diva-174467DOI: 10.1089/cmb.2018.0160ISI: 000556715100013PubMedID: 31855461Scopus ID: 2-s2.0-85089301180OAI: oai:DiVA.org:umu-174467DiVA, id: diva2:1461409
Conference
36th International Conference on Machine Learning (ICML), JUN 10-15, 2019, Long Beach, CA
2020-08-262020-08-262025-02-20Bibliographically approved