Models Of Transcription Factor Binding Sites Research Articles

Identification of Plant Transcription Factor DNA-Binding Sites Using seq-DAP-seq.

The identification of genome-wide transcription factor binding sites(TFBS) is a critical step in deciphering gene and transcriptional regulatory networks. However, determining the genome-wide binding of specific TFs or TF complexes remains a technical challenge. DNA affinity purification sequencing (DAP-seq) and modifications such as sequential DAP-seq (seq-DAP-seq) are robust in vitro methods for mapping individual TF or TF complex binding sites in a genome-wide manner. DAP-seq protocols use a genomic DNA (gDNA) library from any target organism with or without amplification, allowing the determination of TF binding on naked or endogenously modified DNA, respectively. As a first step, the gDNA is fragmented to ~200bp, end-repaired, and sequencing adaptors are added. This gDNA library can be used directly or an amplification step may be performed to remove DNA modifications such as cytosine methylation. DNA libraries are then incubated with an affinity-tagged TF or TF- complex immobilized on magnetic beads. The TF or TF complex of interest is generally produced using recombinant protein expression and purified prior to DNA affinity purification. After incubation of the DNA library with the immobilized TF of interest, multiple wash steps are performed to reduce non-specific DNA binding and the TF-DNA complexes eluted. The eluted DNA is PCR-amplified and sequenced using next-generation sequencing. The resulting sequence reads are mapped to the corresponding reference genome, identifying direct potential bound regions and binding sites of the TF or TF complex of interest. Predictive TFBS models are generated from the bound regions using downstream bioinformatics analysis pipelines. Here, we present a detailed protocol outlining the steps required for seq-DAP-seq of a heterooligomeric TF complex (Fig. 1) and briefly describe the downstream bioinformatics pipeline used to develop a robust TFBS model from sequencing data generated from a DAP-seq experiment.

HOCOMOCO: data integration for building collection of reliable transcription factor binding sites models

HOCOMOCO: data integration for building collection of reliable transcription factor binding sites models

Erratum to: A systematic, large-scale comparison of transcription factor binding site models.

Erratum to: A systematic, large-scale comparison of transcription factor binding site models.

A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.

Transcription factor binding sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA motif) models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement if choosing di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models.

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data

BackgroundChIP-Seq is widely used to detect genomic segments bound by transcription factors (TF), either directly at DNA binding sites (BSs) or indirectly via other proteins. Currently, there are many software tools implementing different approaches to identify TFBSs within ChIP-Seq peaks. However, their use for the interpretation of ChIP-Seq data is usually complicated by the absence of direct experimental verification, making it difficult both to set a threshold to avoid recognition of too many false-positive BSs, and to compare the actual performance of different models.ResultsUsing ChIP-Seq data for FoxA2 binding loci in mouse adult liver and human HepG2 cells we compared FoxA binding-site predictions for four computational models of two fundamental classes: pattern matching based on existing training set of experimentally confirmed TFBSs (oPWM and SiteGA) and de novo motif discovery (ChIPMunk and diChIPMunk). To properly select prediction thresholds for the models, we experimentally evaluated affinity of 64 predicted FoxA BSs using EMSA that allows safely distinguishing sequences able to bind TF. As a result we identified thousands of reliable FoxA BSs within ChIP-Seq loci from mouse liver and human HepG2 cells. It was found that the performance of conventional position weight matrix (PWM) models was inferior with the highest false positive rate. On the contrary, the best recognition efficiency was achieved by the combination of SiteGA & diChIPMunk/ChIPMunk models, properly identifying FoxA BSs in up to 90% of loci for both mouse and human ChIP-Seq datasets.ConclusionsThe experimental study of TF binding to oligonucleotides corresponding to predicted sites increases the reliability of computational methods for TFBS-recognition in ChIP-Seq data analysis. Regarding ChIP-Seq data interpretation, basic PWMs have inferior TFBS recognition quality compared to the more sophisticated SiteGA and de novo motif discovery methods. A combination of models from different principles allowed identification of proper TFBSs.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-80) contains supplementary material, which is available to authorized users.

Jaccard index based similarity measure to compare transcription factor binding site models

BackgroundPositional weight matrix (PWM) remains the most popular for quantification of transcription factor (TF) binding. PWM supplied with a score threshold defines a set of putative transcription factor binding sites (TFBS), thus providing a TFBS model.TF binding DNA fragments obtained by different experimental methods usually give similar but not identical PWMs. This is also common for different TFs from the same structural family. Thus it is often necessary to measure the similarity between PWMs. The popular tools compare PWMs directly using matrix elements. Yet, for log-odds PWMs, negative elements do not contribute to the scores of highly scoring TFBS and thus may be different without affecting the sets of the best recognized binding sites. Moreover, the two TFBS sets recognized by a given pair of PWMs can be more or less different depending on the score thresholds.ResultsWe propose a practical approach for comparing two TFBS models, each consisting of a PWM and the respective scoring threshold. The proposed measure is a variant of the Jaccard index between two TFBS sets. The measure defines a metric space for TFBS models of all finite lengths. The algorithm can compare TFBS models constructed using substantially different approaches, like PWMs with raw positional counts and log-odds. We present the efficient software implementation: MACRO-APE (MAtrix CompaRisOn by Approximate P-value Estimation).ConclusionsMACRO-APE can be effectively used to compute the Jaccard index based similarity for two TFBS models. A two-pass scanning algorithm is presented to scan a given collection of PWMs for PWMs similar to a given query.Availability and implementationMACRO-APE is implemented in ruby 1.9; software including source code and a manual is freely available at http://autosome.ru/macroape/ and in supplementary materials.

HOCOMOCO: a comprehensive collection of human transcription factor binding sites models

Transcription factor (TF) binding site (TFBS) models are crucial for computational reconstruction of transcription regulatory networks. In existing repositories, a TF often has several models (also called binding profiles or motifs), obtained from different experimental data. Having a single TFBS model for a TF is more pragmatic for practical applications. We show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias.We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source.

Tree-Based Position Weight Matrix Approach to Model Transcription Factor Binding Site Profiles

Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. We develop a novel Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data with a pre-existing PWM. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method starting from existing PWM has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions.

OPOSSUM: integrated tools for analysis of regulatory motif over-representation

The identification of over-represented transcription factor binding sites from sets of co-expressed genes provides insights into the mechanisms of regulation for diverse biological contexts. oPOSSUM, an internet-based system for such studies of regulation, has been improved and expanded in this new release. New features include a worm-specific version for investigating binding sites conserved between Caenorhabditis elegans and C. briggsae, as well as a yeast-specific version for the analysis of co-expressed sets of Saccharomyces cerevisiae genes. The human and mouse applications feature improvements in ortholog mapping, sequence alignments and the delineation of multiple alternative promoters. oPOSSUM2, introduced for the analysis of over-represented combinations of motifs in human and mouse genes, has been integrated with the original oPOSSUM system. Analysis using user-defined background gene sets is now supported. The transcription factor binding site models have been updated to include new profiles from the JASPAR database. oPOSSUM is available at http://www.cisreg.ca/oPOSSUM/

Identifying cis-regulatory modules by combining comparative and compositional analysis of DNA

Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction. We have developed a method to predict CRMs called CisPlusFinder that identifies high density regions of perfect local ungapped sequences (PLUSs) based on multiple species conservation. By assuming that PLUSs contain core TFBS motifs that are locally overrepresented, the method attempts to capture the expected features of CRM structure and evolution. Applied to a benchmark dataset of CRMs involved in early Drosophila development, CisPlusFinder predicts more annotated CRMs than all other methods tested. Using the REDfly database, we find that some 'false positive' predictions in the benchmark dataset correspond to recently annotated CRMs. Our work demonstrates that CRM prediction methods that combine comparative genomic data with statistical properties of DNA may achieve reasonable performance when applied genome-wide in the absence of an a priori set of known TFBS motifs. The program CisPlusFinder can be downloaded at http://jakob.genetik.uni-koeln.de/bioinformatik/people/nora/nora.html. All software is licensed under the Lesser GNU Public License (LGPL).

A new generation of JASPAR, the open-access repository for transcription factor binding site profiles

JASPAR is the most complete open-access collection of transcription factor binding site (TFBS) matrices. In this new release, JASPAR grows into a meta-database of collections of TFBS models derived by diverse approaches. We present JASPAR CORE—an expanded version of the original, non-redundant collection of annotated, high-quality matrix-based transcription factor binding profiles, JASPAR FAM—a collection of familial TFBS models and JASPAR phyloFACTS—a set of matrices computationally derived from statistically overrepresented, evolutionarily conserved regulatory region motifs from mammalian genomes. JASPAR phyloFACTS serves as a non-redundant extension to JASPAR CORE, enhancing the overall breadth of JASPAR for promoter sequence analysis. The new release of JASPAR is available at .

MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes

BackgroundCis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity.ResultsWe describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method.ConclusionThe search engine, available at , allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.