ArticleNucleic acids research2014
SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A systematic study of HIF1A cofactors in hypoxic cancer cells.Scientific reports · 2022Article
- Psychiatric risk gene transcription factor 4 preferentially regulates cortical interneuron neurogenesis during early brain development.Journal of biomedical research · 2022Article
- A systematic study of motif pairs that may facilitate enhancer-promoter interactions.Journal of integrative bioinformatics · 2022Article
- A holistic miRNA-mRNA module discovery.Non-coding RNA research · 2021Article
- Shared distal regulatory regions may contribute to the coordinated expression of human ribosomal protein genes.Genomics · 2020Article
- Prognostic cancer gene signatures share common regulatory motifs.Scientific reports · 2017Article
- Article
- PETModule: a motif module based approach for enhancer target gene prediction.Scientific reports · 2016Article
- Integrative analyses shed new light on human ribosomal protein gene regulation.Scientific reports · 2016Article
- Integrative omics reveals MYCN as a global suppressor of cellular signalling and enables network-based therapeutic target discovery in neuroblastoma.Oncotarget · 2015Article
- MicroRNA modules prefer to bind weak and unconventional target sites.Bioinformatics (Oxford, England) · 2015Article
- Comprehensive discovery of DNA motifs in 349 human cells and tissues reveals new features of motifs.Nucleic acids research · 2015Article
- PreDREM: a database of predicted DNA regulatory motifs from 349 human cell and tissue samples.Database : the journal of biological databases and curation · 2015Article
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3 authors.
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Abstract
The identification of transcription factor binding motifs is important for the study of gene transcriptional regulation. The chromatin immunoprecipitation (ChIP), followed by massive parallel sequencing (ChIP-seq) experiments, provides an unprecedented opportunity to discover binding motifs. Computational methods have been developed to identify motifs from ChIP-seq data, while at the same time encountering several problems. For example, existing methods are often not scalable to the large number of sequences obtained from ChIP-seq peak regions. Some methods heavily rely on well-annotated motifs even though the number of known motifs is limited. To simplify the problem, de novo motif discovery methods often neglect underrepresented motifs in ChIP-seq peak regions. To address these issues, we developed a novel approach called SIOMICS to de novo discover motifs from ChIP-seq data. Tested on 13 ChIP-seq data sets, SIOMICS identified motifs of many known and new cofactors. Tested on 13 simulated random data sets, SIOMICS discovered no motif in any data set. Compared with two recently developed methods for motif discovery, SIOMICS shows advantages in terms of speed, the number of known cofactor motifs predicted in experimental data sets and the number of false motifs predicted in random data sets. The SIOMICS software is freely available at http://eecs.ucf.edu/∼xiaoman/SIOMICS/SIOMICS.html.
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