Identifying Cell Type-Specific Transcription Factors by Integrating ChIP-seq and eQTL Data-Application to Monocyte Gene Regulation
| Autor: | Stephen A. Ramsey, Mudra Choudhury |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
genetic processes Genomics Computational biology eQTL Bioinformatics Proteomics 03 medical and health sciences Interaction network Genetics SIN3A Medicine natural sciences lcsh:QH301-705.5 Molecular Biology Transcription factor transcription factor Ecology Evolution Behavior and Systematics Regulation of gene expression business.industry fungi Methodology Computer Science Applications Chromatin ChIP-seq 030104 developmental biology lcsh:Biology (General) Expression quantitative trait loci business |
| Zdroj: | Gene Regulation and Systems Biology, Vol 2016, Iss 10, Pp 105-110 (2016) Gene Regulation and Systems Biology, Vol 10 (2016) Gene Regulation and Systems Biology |
| ISSN: | 1177-6250 |
| DOI: | 10.4137/grsb.s40768 |
| Popis: | We describe a novel computational approach to identify transcription factors (TFs) that are candidate regulators in a human cell type of interest. Our approach involves integrating cell type-specific expression quantitative trait locus (eQTL) data and TF data from chromatin immunoprecipitation-to-tag-sequencing (ChIP-seq) experiments in cell lines. To test the method, we used eQTL data from human monocytes in order to screen for TFs. Using a list of known monocyte-regulating TFs, we tested the hypothesis that the binding sites of cell type-specific TF regulators would be concentrated in the vicinity of monocyte eQTLs. For each of 397 ChIP-seq data sets, we obtained an enrichment ratio for the number of ChIP-seq peaks that are located within monocyte eQTLs. We ranked ChIP-seq data sets according to their statistical significances for eQTL overlap, and from this ranking, we observed that monocyte-regulating TFs are more highly ranked than would be expected by chance. We identified 27 TFs that had significant monocyte enrichment scores and mapped them into a protein interaction network. Our analysis uncovered two novel candidate monocyte-regulating TFs, BCLAF1 and SIN3A. Our approach is an efficient method to identify candidate TFs that can be used for any cell/tissue type for which eQTL data are available. |
| Databáze: | OpenAIRE |
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