ArticleNucleic acids research2010
De novo motif identification improves the accuracy of predicting transcription factor binding sites in ChIP-Seq data analysis.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 71 citations in OpenAlex.
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- Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants.Journal of translational medicine · 2018Article
- Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling.Nucleic acids research · 2017Article
- Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells.Frontiers in genetics · 2016Review
- Chimeric EWSR1-FLI1 regulates the Ewing sarcoma susceptibility gene EGR2 via a GGAA microsatellite.Nature genetics · 2015Article
- RNA:DNA hybrids in the human genome have distinctive nucleotide characteristics, chromatin composition, and transcriptional relationships.Epigenetics & chromatin · 2015Article
- Decoding ChIP-seq with a double-binding signal refines binding peaks to single-nucleotides and predicts cooperative interaction.Genome research · 2014Article
- Advanced methods for the analysis of chromatin-associated proteins.Physiological genomics · 2014Review
- Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data.BMC genomics · 2014Article
- An improved systematic approach to predicting transcription factor target genes using support vector machine.PloS one · 2014Article
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- Identification of transcription factor binding sites from ChIP-seq data at high resolution.Bioinformatics (Oxford, England) · 2013Article
- Systems biology of Ewing sarcoma: a network model of EWS-FLI1 effect on proliferation and apoptosis.Nucleic acids research · 2013Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dramatic progress in the development of next-generation sequencing technologies has enabled accurate genome-wide characterization of the binding sites of DNA-associated proteins. This technique, baptized as ChIP-Seq, uses a combination of chromatin immunoprecipitation and massively parallel DNA sequencing. Other published tools that predict binding sites from ChIP-Seq data use only positional information of mapped reads. In contrast, our algorithm MICSA (Motif Identification for ChIP-Seq Analysis) combines this source of positional information with information on motif occurrences to better predict binding sites of transcription factors (TFs). We proved the greater accuracy of MICSA with respect to several other tools by running them on datasets for the TFs NRSF, GABP, STAT1 and CTCF. We also applied MICSA on a dataset for the oncogenic TF EWS-FLI1. We discovered >2000 binding sites and two functionally different binding motifs. We observed that EWS-FLI1 can activate gene transcription when (i) its binding site is located in close proximity to the gene transcription start site (up to approximately 150 kb), and (ii) it contains a microsatellite sequence. Furthermore, we observed that sites without microsatellites can also induce regulation of gene expression--positively as often as negatively--and at much larger distances (up to approximately 1 Mb).
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