Browse
Submit Data
Databases
API
Help

Dataset Information

37 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: 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.

SUBMITTER: Elo LL

PROVIDER: S-EPMC3245948 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

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

Elo Laura L LL Kallio Aleksi A Laajala Teemu D TD Hawkins R David RD Korpelainen Eija E Aittokallio Tero T

Nucleic acids research 20111018 1

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 g ...[more]

PMID: 22009681

Similar Datasets

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments.

Project description:BackgroundChromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications.ResultsThe present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments.ConclusionsIn general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.

| S-EPMC2804666 | biostudies-literature

On the detection and refinement of transcription factor binding sites using ChIP-Seq data.

Project description:Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic 'greedy' search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.

| S-EPMC2853110 | biostudies-literature

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

Project description:BackgroundChromatin immunoprecipitation combined with the next-generation DNA sequencing technologies (ChIP-seq) becomes a key approach for detecting genome-wide sets of genomic sites bound by proteins, such as transcription factors (TFs). Several methods and open-source tools have been developed to analyze ChIP-seq data. However, most of them are designed for detecting TF binding regions instead of accurately locating transcription factor binding sites (TFBSs). It is still challenging to pinpoint TFBSs directly from ChIP-seq data, especially in regions with closely spaced binding events.ResultsWith the aim to pinpoint TFBSs at a high resolution, we propose a novel method named SeqSite, implementing a two-step strategy: detecting tag-enriched regions first and pinpointing binding sites in the detected regions. The second step is done by modeling the tag density profile, locating TFBSs on each strand with a least-squares model fitting strategy, and merging the detections from the two strands. Experiments on simulation data show that SeqSite can locate most of the binding sites more than 40-bp from each other. Applications on three human TF ChIP-seq datasets demonstrate the advantage of SeqSite for its higher resolution in pinpointing binding sites compared with existing methods.ConclusionsWe have developed a computational tool named SeqSite, which can pinpoint both closely spaced and isolated binding sites, and consequently improves the resolution of TFBS detection from ChIP-seq data.

| S-EPMC3287483 | biostudies-literature

A ChIP-Seq benchmark shows that sequence conservation mainly improves detection of strong transcription factor binding sites.

Project description:BackgroundTranscription factors are important controllers of gene expression and mapping transcription factor binding sites (TFBS) is key to inferring transcription factor regulatory networks. Several methods for predicting TFBS exist, but there are no standard genome-wide datasets on which to assess the performance of these prediction methods. Also, it is believed that information about sequence conservation across different genomes can generally improve accuracy of motif-based predictors, but it is not clear under what circumstances use of conservation is most beneficial.ResultsHere we use published ChIP-seq data and an improved peak detection method to create comprehensive benchmark datasets for prediction methods which use known descriptors or binding motifs to detect TFBS in genomic sequences. We use this benchmark to assess the performance of five different prediction methods and find that the methods that use information about sequence conservation generally perform better than simpler motif-scanning methods. The difference is greater on high-affinity peaks and when using short and information-poor motifs. However, if the motifs are specific and information-rich, we find that simple motif-scanning methods can perform better than conservation-based methods.ConclusionsOur benchmark provides a comprehensive test that can be used to rank the relative performance of transcription factor binding site prediction methods. Moreover, our results show that, contrary to previous reports, sequence conservation is better suited for predicting strong than weak transcription factor binding sites.

| S-EPMC3077367 | 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.

| S-EPMC2917543 | biostudies-literature

Identification of transcription factor binding sites from ChIP-seq data at high resolution.

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.

| S-EPMC3799470 | biostudies-literature

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

Project description:ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available.

| S-EPMC3946423 | biostudies-literature

Optimized ChIP-seq method facilitates transcription factor profiling in human tumors.

Project description:Chromatin immunoprecipitation (ChIP)-seq analyses of transcription factors in clinical specimens are challenging due to the technical limitations and low quantities of starting material, often resulting in low enrichments and poor signal-to-noise ratio. Here, we present an optimized protocol for transcription factor ChIP-seq analyses in human tissue, yielding an ?100% success rate for all transcription factors analyzed. As proof of concept and to illustrate general applicability of the approach, human tissue from the breast, prostate, and endometrial cancers were analyzed. In addition to standard formaldehyde fixation, disuccinimidyl glutarate was included in the procedure, greatly increasing data quality. To illustrate the sensitivity of the optimized protocol, we provide high-quality ChIP-seq data for three independent factors (AR, FOXA1, and H3K27ac) from a single core needle prostate cancer biopsy specimen. In summary, double-cross-linking strongly improved transcription factor ChIP-seq quality on human tumor samples, further facilitating and enhancing translational research on limited amounts of tissue.

| S-EPMC6311467 | biostudies-literature

ABC: a tool to identify SNVs causing allele-specific transcription factor binding from ChIP-Seq experiments.

Project description:MotivationDetection of allelic imbalances in ChIP-Seq reads is a powerful approach to identify functional non-coding single nucleotide variants (SNVs), either polymorphisms or mutations, which modulate the affinity of transcription factors for chromatin. We present ABC, a computational tool that identifies allele-specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs. ABC controls for potential false positives resulting from biases introduced by the use of short sequencing reads in ChIP-Seq and can efficiently process a large number of heterozygous SNVs.ResultsABC successfully identifies previously characterized functional SNVs, such as the rs4784227 breast cancer risk associated SNP that modulates the affinity of FOXA1 for the chromatin.Availability and implementationThe code is open-source under an Artistic-2.0 license and versioned on GitHub (https://github.com/mlupien/ABC/). ABC is written in PERL and can be run on any platform with both PERL (≥5.18.1) and R (≥3.1.1) installed. The script requires the PERL Statistics::R module.Contactmlupien@uhnres.utoronto.caSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4668780 | biostudies-literature

Identifying differential transcription factor binding in ChIP-seq.

Project description:ChIP seq is a widely used assay to measure genome-wide protein binding. The decrease in costs associated with sequencing has led to a rise in the number of studies that investigate protein binding across treatment conditions or cell lines. In addition to the identification of binding sites, new studies evaluate the variation in protein binding between conditions. A number of approaches to study differential transcription factor binding have recently been developed. Several of these methods build upon established methods from RNA-seq to quantify differences in read counts. We compare how these new approaches perform on different data sets from the ENCODE project to illustrate the impact of data processing pipelines under different study designs. The performance of normalization methods for differential ChIP-seq depends strongly on the variation in total amount of protein bound between conditions, with total read count outperforming effective library size, or variants thereof, when a large variation in binding was studied. Use of input subtraction to correct for non-specific binding showed a relatively modest impact on the number of differential peaks found and the fold change accuracy to biological validation, however a larger impact might be expected for samples with more extreme copy number variations between them. Still, it did identify a small subset of novel differential regions while excluding some differential peaks in regions with high background signal. These results highlight proper scaling for between-sample data normalization as critical for differential transcription factor binding analysis and suggest bioinformaticians need to know about the variation in level of total protein binding between conditions to select the best analysis method. At the same time, validation using fold-change estimates from qRT-PCR suggests there is still room for further method improvement.

| S-EPMC4413818 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data