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De novo motif identification improves the accuracy of predicting transcription factor binding sites in ChIP-Seq data analysis.

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).

SUBMITTER: Boeva V

PROVIDER: S-EPMC2887977 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

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GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments.

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Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite.

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| S-EPMC3287483 | biostudies-literature

Optimized detection of transcription factor-binding sites in ChIP-seq experiments.

Project description:We developed a computational procedure for optimizing the binding site detections in a given ChIP-seq experiment by maximizing their reproducibility under bootstrap sampling. We demonstrate how the procedure can improve the detection accuracies beyond those obtained with the default settings of popular peak calling software, or inform the user whether the peak detection results are compromised, circumventing the need for arbitrary re-iterative peak calling under varying parameter settings. The generic, open-source implementation is easily extendable to accommodate additional features and to promote its widespread application in future ChIP-seq studies. The peakROTS R-package and user guide are freely available at http://www.nic.funet.fi/pub/sci/molbio/peakROTS.

| S-EPMC3245948 | biostudies-literature

Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data.

Project description:Molecular interactions between protein complexes and DNA mediate essential gene-regulatory functions. Uncovering such interactions by chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) has recently become the focus of intense interest. We here introduce quantitative enrichment of sequence tags (QuEST), a powerful statistical framework based on the kernel density estimation approach, which uses ChIP-Seq data to determine positions where protein complexes contact DNA. Using QuEST, we discovered several thousand binding sites for the human transcription factors SRF, GABP and NRSF at an average resolution of about 20 base pairs. MEME motif-discovery tool-based analyses of the QuEST-identified sequences revealed DNA binding by cofactors of SRF, providing evidence that cofactor binding specificity can be obtained from ChIP-Seq data. By combining QuEST analyses with Gene Ontology (GO) annotations and expression data, we illustrate how general functions of transcription factors can be inferred.

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Project description:MotivationChromatin immunoprecipitation coupled to next-generation sequencing (ChIP-seq) is widely used to study the in vivo binding sites of transcription factors (TFs) and their regulatory targets. Recent improvements to ChIP-seq, such as increased resolution, promise deeper insights into transcriptional regulation, yet require novel computational tools to fully leverage their advantages.ResultsTo this aim, we have developed peakzilla, which can identify closely spaced TF binding sites at high resolution (i.e. resolves individual binding sites even if spaced closely), as we demonstrate using semisynthetic datasets, performing ChIP-seq for the TF Twist in Drosophila embryos with different experimental fragment sizes, and analyzing ChIP-exo datasets. We show that the increased resolution reached by peakzilla is highly relevant, as closely spaced Twist binding sites are strongly enriched in transcriptional enhancers, suggesting a signature to discriminate functional from abundant non-functional or neutral TF binding. Peakzilla is easy to use, as it estimates all the necessary parameters from the data and is freely available.Availability and implementationThe peakzilla program is available from https://github.com/steinmann/peakzilla or http://www.starklab.org/data/peakzilla/.Contactstark@starklab.org.Supplementary informationSupplementary data are available at Bioinformatics online.

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PolyaPeak: detecting transcription factor binding sites from ChIP-seq using peak shape information.

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