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At least 19 recordsLinked to original sources
PubMed2003

On the power of profiles for transcription factor binding site detection.

Transcription factor binding site (TFBS) detection plays an important role in computational biology, with applications in gene finding and gene regulation. The sites are often modeled by gapless profiles, also known as position-weight matrices. Past research has focused on the significance of profile scores (the ability to avoid false positives), but this alone is not enough: The profile must also possess the power to detect the true positive signals. Several completed genomes are now available, and the search for TFBSs is moving to a large scale; so discriminating signal from noise becomes even more challenging. Since TFBS profiles are usually estimated from only a few experimentally confirmed instances, careful regularization is an important issue. We present a novel method that is well suited for this situation. We further develop measures that help in judging profile quality, based on both sensitivity and selectivity of a profile. It is shown that these quality measures can be efficiently computed, and we propose statistically well-founded methods to choose score thresholds. Our findings are applied to the TRANSFAC database of transcription factor binding sites. The results are disturbing: If we insist on a significance level of 5% in sequences of length 500, only 19% of the profiles detect a true signal instance with 95% success probability under varying background sequence compositions.

Journal Article↗
PubMed2024

TFinder: A Python Web Tool for Predicting Transcription Factor Binding Sites.

Transcription is a key cell process that consists of synthesizing several copies of RNA from a gene DNA sequence. This process is highly regulated and closely linked to the ability of transcription factors to bind specifically to DNA. TFinder is an easy-to-use Python web portal allowing the identification of Individual Motifs (IM) such as Transcription Factor Binding Sites (TFBS). Using the NCBI API, TFinder extracts either promoter or gene terminal regulatory regions, through a simple query of NCBI gene name or ID. It enables simultaneous analysis across five different species for an unlimited number of genes. TFinder searches for Individual Motifs in different formats, including IUPAC codes and JASPAR entries. Moreover, TFinder also allows de novo generations of a Position Weight Matrix (PWM) and the use of already established PWM. Finally, the data are provided in a tabular and a graph format showing the relevance and the P-value of the Individual Motifs found as well as their location relative to the Transcription Start Site (TSS) or the terminal region of the gene. The results are then sent by email to users facilitating the subsequent data analysis and sharing. TFinder is written in Python and freely available on GitHub under the MIT license: https://github.com/Jumitti/TFinder. It can be accessed as a web application implemented in Streamlit at https://tfinder-ipmc.streamlit.app. Resources are available on Streamlit "Resources" tab. TFINDER strength is that it relies on an all-in-one intuitive tool allowing users inexperienced with bioinformatics tools to retrieve gene regulatory regions sequences in multiple species and to search for individual motifs in a huge number of genes.

Transcription Factors↗
PubMed2006

Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities.

Transcription factors (TFs) interact with specific DNA regulatory sequences to control gene expression throughout myriad cellular processes. However, the DNA binding specificities of only a small fraction of TFs are sufficiently characterized to predict the sequences that they can and cannot bind. We present a maximally compact, synthetic DNA sequence design for protein binding microarray (PBM) experiments that represents all possible DNA sequence variants of a given length k (that is, all 'k-mers') on a single, universal microarray. We constructed such all k-mer microarrays covering all 10-base pair (bp) binding sites by converting high-density single-stranded oligonucleotide arrays to double-stranded (ds) DNA arrays. Using these microarrays we comprehensively determined the binding specificities over a full range of affinities for five TFs of different structural classes from yeast, worm, mouse and human. The unbiased coverage of all k-mers permits high-throughput interrogation of binding site preferences, including nucleotide interdependencies, at unprecedented resolution.

Animals↗
PubMed2004

Similarity of position frequency matrices for transcription factor binding sites.

MOTIVATION: Transcription-factor binding sites (TFBS) in promoter sequences of higher eukaryotes are commonly modeled using position frequency matrices (PFM). The ability to compare PFMs representing binding sites is especially important for de novo sequence motif discovery, where it is desirable to compare putative matrices to one another and to known matrices. RESULTS: We describe a PFM similarity quantification method based on product multinomial distributions, demonstrate its ability to identify PFM similarity and show that it has a better false positive to false negative ratio compared to existing methods. We grouped TFBS frequency matrices from two libraries into matrix families and identified the matrices that are common and unique to these libraries. We identified similarities and differences between the skeletal-muscle-specific and non-muscle-specific frequency matrices for the binding sites of Mef-2, Myf, Sp-1, SRF and TEF of Wasserman and Fickett. We further identified known frequency matrices and matrix families that were strongly similar to the matrices given by Wasserman and Fickett. We provide methodology and tools to compare and query libraries of frequency matrices for TFBSs. AVAILABILITY: Software is available to use over the Web at http://rulai.cshl.edu/MatCompare SUPPLEMENTARY INFORMATION: Database and clustering statistics, matrix families and representatives are available at http://rulai.cshl.edu/MatCompare/Supplementary.

Algorithms↗
PubMed2005

Scoring functions for transcription factor binding site prediction.

BACKGROUND: Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations. RESULTS: Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome. Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study. CONCLUSION: LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.

Algorithms↗
PubMed2003

A biophysical approach to transcription factor binding site discovery.

Identification of transcription factor binding sites within regulatory segments of genomic DNA is an important step toward understanding of the regulatory circuits that control expression of genes. Here, we describe a novel bioinformatics method that bases classification of potential binding sites explicitly on the estimate of sequence-specific binding energy of a given transcription factor. The method also estimates the chemical potential of the factor that defines the threshold of binding. In contrast with the widely used information-theoretic weight matrix method, the new approach correctly describes saturation in the transcription factor/DNA binding probability. This results in a significant improvement in the number of expected false positives, particularly in the ubiquitous case of low-specificity factors. In the strong binding limit, the algorithm is related to the "support vector machine" approach to pattern recognition. The new method is used to identify likely genomic binding sites for the E. coli transcription factors collected in the DPInteract database. In addition, for CRP (a global regulatory factor), the likely regulatory modality (i.e., repressor or activator) of predicted binding sites is determined.

Algorithms↗
PubMed2004

Genome-wide prediction and analysis of function-specific transcription factor binding sites.

DNA-binding transcription factors play a central role in transcription regulation, and the annotation of transcription-factor binding sites in upstream regions of human genes is essential for building a genome-wide regulatory network. We describe methodology to accurately predict the transcription-factor binding sites in the proximal-promoter region of function-specific genes. In order to increase the accuracy of transcription factor binding-site prediction, we rely on recent genome sequence data, known transcription factor binding-site matrices, and Gene Ontology biological-function-based gene classification. Using TRANSFAC position-frequency matrices, we detected individual and cooperating transcription-factor binding sites in proximal promoters of ENSEMBL annotated human genes. We used the over representation of detected binding sites in the proximal promoters as compared to the second exons to control specificity. We confirmed the majority of transcription-factor binding sites predicted in proximal promoters of immune-response genes with evidence from existing literature. We validated the predicted cooperation between transcription factors NF-kappa B and IRF in the regulation of gene expression with microarray transcript profiling data and literature-derived protein-protein interaction network. We also identified over-represented individual and pairs of transcription-factor binding sites in the proximal promoters of each Gene Ontology biological-process gene group. Our tools and analysis provide a new resource for deciphering transcription regulation in different biological paradigms.

Computational Biology↗
PubMed2002

Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors.

We can determine the effects of many possible sequence variations in transcription factor binding sites using microarray binding experiments. Analysis of wild-type and mutant Zif268 (Egr1) zinc fingers bound to microarrays containing all possible central 3 bp triplet binding sites indicates that the nucleotides of transcription factor binding sites cannot be treated independently. This indicates that the current practice of characterizing transcription factor binding sites by mutating individual positions of binding sites one base pair at a time does not provide a true picture of the sequence specificity. Similarly, current bioinformatic practices using either just a consensus sequence, or even mononucleotide frequency weight matrices to provide more complete descriptions of transcription factor binding sites, are not accurate in depicting the true binding site specificities, since these methods rely upon the assumption that the nucleotides of binding sites exert independent effects on binding affinity. Our results stress the importance of complete reference tables of all possible binding sites for comparing protein binding preferences for various DNA sequences. We also show results suggesting that microarray binding data using particular subsets of all possible binding sites can be used to extrapolate the relative binding affinities of all possible full-length binding sites, given a known binding site for use as a starting sequence for site preference refinement.

Animals↗
PubMed2002

rVista for comparative sequence-based discovery of functional transcription factor binding sites.

Identifying transcriptional regulatory elements represents a significant challenge in annotating the genomes of higher vertebrates. We have developed a computational tool, rVista, for high-throughput discovery of cis-regulatory elements that combines clustering of predicted transcription factor binding sites (TFBSs) and the analysis of interspecies sequence conservation to maximize the identification of functional sites. To assess the ability of rVista to discover true positive TFBSs while minimizing the prediction of false positives, we analyzed the distribution of several TFBSs across 1 Mb of the well-annotated cytokine gene cluster (Hs5q31; Mm11). Because a large number of AP-1, NFAT, and GATA-3 sites have been experimentally identified in this interval, we focused our analysis on the distribution of all binding sites specific for these transcription factors. The exploitation of the orthologous human-mouse dataset resulted in the elimination of > 95% of the approximately 58,000 binding sites predicted on analysis of the human sequence alone, whereas it identified 88% of the experimentally verified binding sites in this region.

Animals↗
PubMed2005

Identification of conserved structural features at sequentially degenerate locations in transcription factor binding sites.

UNLABELLED: Many locations within transcription factor binding sites are not sequentially conserved and appear to be degenerate. We hypothesize that some of these positions contain essential structural codes that are recognized by the transcription factors that bind to them. The structural codes can be defined by base-pair step parameters that describe the relative displacement and orientation of two adjacent base pairs in a nucleic acid structure. We have developed a method, Identification of Conserved Structural Features (ICSF), which uses base-pair step parameters obtained from a collection of high-resolution DNA crystal structures to discover structural conservation that exists in the sequentially degenerate areas within a binding site and produce profiles of the structural features along the entire site. We have focused our study on the transcription factor binding sites in the JASPAR database and have found that one-third (P-value > or = 0.05) of the binding sites contain sequentially degenerate locations with highly conserved structural features as described by the base-pair step parameters. These results will help us to gain a better understanding of the process by which transcription factors recognize their binding sites and possibly lead to an improvement in our ability to find these sites in genomic sequences. AVAILABILITY: ICSF is freely available to academic users at http://zlab.bu.edu/ICSF . SUPPLEMENTARY INFORMATION: http://zlab.bu.edu/ICSF .

Algorithms↗
PubMed2005

Measuring similarities between transcription factor binding sites.

BACKGROUND: Collections of transcription factor binding profiles (Transfac, Jaspar) are essential to identify regulatory elements in DNA sequences. Subsets of highly similar profiles complicate large scale analysis of transcription factor binding sites. RESULTS: We propose to identify and group similar profiles using two independent similarity measures: chi2 distances between position frequency matrices (PFMs) and correlation coefficients between position weight matrices (PWMs) scores. CONCLUSION: We show that these measures complement each other and allow to associate Jaspar and Transfac matrices. Clusters of highly similar matrices are identified and can be used to optimise the search for regulatory elements. Moreover, the application of the measures is illustrated by assigning E-box matrices of a SELEX experiment and of experimentally characterised binding sites of circadian clock genes to the Myc-Max cluster.

ARNTL Transcription Factors↗
PubMed2002

AliBaba2: context specific identification of transcription factor binding sites.

Currently, prediction of transcription factor binding sites is widely done using matrices collected from literature. This leads to several problems. We cannot actively control the conservation of the matrices, we cannot systematically use all binding sites available, we do not know which sites were used and which were discarded in matrix construction, we cannot compare and evaluate matrices easily, we cannot detect redundancy and we cannot control sensitivity and specificity. So we are lacking control during the identification process. In this paper a method to overcome these problems is proposed. It is assumed that each binding site has an unknown context which determines its sequence. This leads to the idea of constructing specific matrices for each sequence we are analysing. To do so we have to regard identification of binding sites as a general process, starting at a dataset of known binding sites and ending with the identification of a potential new binding site. In this paper such a process is presented. Besides overcoming the mentioned problems, the implementation also reaches a significantly higher accuracy than current approaches. Evaluations are done analysing all binding sites of TRANSFAC 3.5 public. The resulting tool AliBaba2 is available at http://wwwiti.cs.uni-magdeburg.de/grabe/alibaba2.

Algorithms↗
PubMed2004

MSCAN: identification of functional clusters of transcription factor binding sites.

Identification of functional transcription factor binding sites in genomic sequences is notoriously difficult. The critical problem is the low specificity of predictions, which directly reflects the low target specificity of DNA binding proteins. To overcome the noise produced in predictions of individual binding sites, a new generation of algorithms achieves better predictive specificity by focusing on locally dense clusters of binding sites. MSCAN is a leading method for binding site cluster detection that determines the significance of observed sites while correcting for local compositional bias of sequences. The algorithm is highly flexible, applying any set of input binding models to the analysis of a user-specified sequence. From the user's perspective, a key feature of the system is that no reference data sets of regulatory sequences from co-regulated genes are required to train the algorithm. The output from MSCAN consists of an ordered list of sequence segments that contain potential regulatory modules. We have chosen the features in MSCAN such that sequence and matrix retrieval is highly facilitated, resulting in a web server that is intuitive to use. MSCAN is available at http://mscan.cgb.ki.se/cgi-bin/MSCAN.

Algorithms↗
PubMedDate not supplied

Conformational and physicochemical DNA features specific for transcription factor binding sites.

MOTIVATION: A reliable recognition of transcription factor binding sites is essential for analysis of regulatory genomic sequences. The experimental data make evident an important role of DNA conformational features for site functioning. However, Internet-available tools for revealing conformational and physicochemical DNA features significant for the site functioning and subsequent use of these features for site recognition have not been developed up to now. RESULTS: We suggest an approach for revealing significant conformational and physicochemical properties of functional sites implemented in the database B-DNA-VIDEO. This database is designed to study the sets of various transcription factor binding sites, providing evidence that transcription factor binding sites are characterized by specific sets of significant conformational and physicochemical DNA properties. For a fixed site, by using the B-DNA features selected for this site recognition, the C-program recognizing this site may be generated, control tested and stored in the database B-DNA-VIDEO. Each B-DNA-VIDEO entry links to the Web-applet recognizing the site, whose significant B-DNA features are stored in this entry as the 'site recognition programs'. The pairwise linked entry-applet pairs are compiled within the B-DNA-VIDEO system, which is simultaneously the database and the program tools package applicable immediately for recognizing the sites stored in the database. Indeed, this is the novelty. Hence, B-DNA-VIDEO is the Web resource of both 'searching for static data' and 'active computation' type, that is why it was called an 'activated database'. AVAILABILITY: B-DNA-VIDEO is available at http://wwwmgs.bionet.nsc.ru/systems/BDNAVideo/ and the mirror site at http://www.cbil.upenn.edu/mgs/systems/c onsfreq/.

Base Sequence↗
PubMed2002

Computational identification of transcription factor binding sites via a transcription-factor-centric clustering (TFCC) algorithm.

While microarray-based expression profiling has facilitated the use of computational methods to find potential cis-regulatory promoter elements, few current in silico approaches explicitly link regulatory motifs with the transcription factors that bind them. We have thus developed a TF-centric clustering (TFCC) algorithm that may provide such missing information through incorporation of biological knowledge about TFs. TFCC is a semi-supervised clustering algorithm which relies on the assumption that the expression profiles of some TFs may be related to those of the genes under their control. We examined this premise and found the vicinities of TFs in expression space are often enriched with the genes they regulate. So, instead of clustering genes based on the mutual similarity of their expression profiles to each other, we used TFs as seeds to group together genes whose expression patterns correlate with that of a particular TF. Then a Gibbs sampling algorithm was applied to search for shared cis-regulatory elements in promoters of clustered genes. Our working hypothesis was that if a TF-centric cluster indeed contains many targets of the seeding TF, at least one of the discovered motifs would be the site bound by the very same TF. We tested the TFCC approach on eight cell cycle and sporulation regulating TFs whose binding sites have been previously characterized in Saccharomyces cerevisiae, and correctly identified binding site motifs for half of them. In addition, we also made de novo predictions for some unknown TF binding sites.

Algorithms↗
PubMed2026

How negative sampling shapes the performance of transcription factor binding site prediction models.

MOTIVATION: Transcription factors (TFs) are key players in gene regulation and development, where they activate and repress gene expression through DNA binding. Predicting transcription factor binding sites (TFBSs) has long been an active area of research, with many deep learning methods developed to tackle this problem. These models are often trained on TF ChIP-seq data, which is generally seen as only providing positive samples. The choice of datasets and negative sampling techniques is a critical yet often overlooked aspect of this work. RESULTS: In this study, we investigate the impact of different negative sampling techniques on TFBS prediction performance. We create high-quality test datasets based on ChIP-seq and ATAC-seq data, where true negatives can be identified as positions that are accessible but not bound by the TF in question. We then train models using various negative sampling techniques, including genomic sampling, shuffling, dinucleotide shuffling, neighborhood sampling, and cell line specific sampling, simulating cases where matching ATAC-seq data is not available. Our results show that, generally, metrics calculated on training datasets give inflated performance scores. Of the tested techniques, genomic sampling of negatives based on similarity to the positives performed by far the best, although still not reaching the performance of baseline models trained on high-quality datasets. Models trained on dinucleotide shuffled negatives performed poorly, despite being a common practice in the field. Our findings highlight the importance of carefully selecting negative sampling techniques for TFBS prediction, as they can significantly impact model performance and the interpretation of results. AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/NatanTourne/TFBS-negatives (DOI: 10.5281/zenodo.18007567).

Binding Sites↗
PubMed2006

A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites.

Given a set of known binding sites for a specific transcription factor, it is possible to build a model of the transcription factor binding site, usually called a motif model, and use this model to search for other sites that bind the same transcription factor. Typically, this search is performed using a position-specific scoring matrix (PSSM), also known as a position weight matrix. In this paper we analyze a set of eukaryotic transcription factor binding sites and show that there is extensive clustering of similar k-mers in eukaryotic motifs, owing to both functional and evolutionary constraints. The apparent limitations of probabilistic models in representing complex nucleotide dependencies lead us to a graph-based representation of motifs. When deciding whether a candidate k-mer is part of a motif or not, we base our decision not on how well the k-mer conforms to a model of the motif as a whole, but how similar it is to specific, known k-mers in the motif. We elucidate the reasons why we expect graph-based methods to perform well on motif data. Our MotifScan algorithm shows greatly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs.

Algorithms↗
PubMed2004

POBO, transcription factor binding site verification with bootstrapping.

Transcription factors can either activate or repress target genes by binding onto short nucleotide sequence motifs in the promoter regions of these genes. Here, we present POBO, a promoter bootstrapping program, for gene expression data. POBO can be used to detect, compare and verify predetermined transcription factor binding site motifs in the promoters of one or two clusters of co-regulated genes. The program calculates the frequencies of the motif in the input promoter sets. A bootstrap analysis detects significantly over- or underrepresented motifs. The output of the program presents bootstrapped results in picture and text formats. The program was tested with published data from transgenic WRKY70 microarray experiments. Intriguingly, motifs recognized by the WRKY transcription factors of plant defense pathways are similarly enriched in both up- and downregulated clusters. POBO analysis suggests slightly modified hypothetical motifs that discriminate between up- and downregulated clusters. In conclusion, POBO allows easy, fast and accurate verification of putative regulatory motifs. The statistical tests implemented in POBO can be useful in eliminating false positives from the results of pattern discovery programs and increasing the reliability of true positives. POBO is freely available from http://ekhidna.biocenter.helsinki.fi:9801/pobo.

Binding Sites↗