ArticleNucleic acids research2012
Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Interplay between NRF1, E2F4 and MYC transcription factors regulating common target genes contributes to cancer development and progression.Cellular oncology (Dordrecht, Netherlands) · 2018Review
- coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers.PLoS computational biology · 2018Article
- STAT3-Mediated Transcriptional Regulation of Osteopontin in STAT3 Loss-of-Function Related Hyper IgE Syndrome.Frontiers in immunology · 2018Article
- Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.BMC bioinformatics · 2017Article
- Article
- Modeling co-occupancy of transcription factors using chromatin features.Nucleic acids research · 2016Article
- Large-scale investigation of human TF-miRNA relations based on coexpression profiles.BioMed research international · 2014Article
- Article
- Jaccard index based similarity measure to compare transcription factor binding site models.Algorithms for molecular biology : AMB · 2013Article
- The impact of trans-regulation on the evolutionary rates of metazoan proteins.Nucleic acids research · 2013Article
- Overlapping ETS and CRE Motifs ((G/C)CGGAAGTGACGTCA) preferentially bound by GABPα and CREB proteins.G3 (Bethesda, Md.) · 2012Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis-regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.
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