PWMScan: a fast tool for scanning entire genomes with a position-specific weight matrix.
Ontology highlight
ABSTRACT: Summary
Transcription factors regulate gene expression by binding to specific short DNA sequences of 5-20 bp to regulate the rate of transcription of genetic information from DNA to messenger RNA. We present PWMScan, a fast web-based tool to scan server-resident genomes for matches to a user-supplied PWM or transcription factor binding site model from a public database.Availability and implementation
The web server and source code are available at http://ccg.vital-it.ch/pwmscan and https://sourceforge.net/projects/pwmscan, respectively.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Ambrosini G
PROVIDER: S-EPMC6041753 | biostudies-literature | 2018 Jul
REPOSITORIES: biostudies-literature
Publications
PWMScan: a fast tool for scanning entire genomes with a position-specific weight matrix.
Ambrosini Giovanna G Groux Romain R Bucher Philipp P
Bioinformatics (Oxford, England) 20180701 14
<h4>Summary</h4>Transcription factors regulate gene expression by binding to specific short DNA sequences of 5-20 bp to regulate the rate of transcription of genetic information from DNA to messenger RNA. We present PWMScan, a fast web-based tool to scan server-resident genomes for matches to a user-supplied PWM or transcription factor binding site model from a public database.<h4>Availability and implementation</h4>The web server and source code are available at http://ccg.vital-it.ch/pwmscan a ...[more]
Similar Datasets
Project description:BACKGROUND:Identifying transcription factor binding sites (TFBS) in silico is key in understanding gene regulation. TFBS are string patterns that exhibit some variability, commonly modelled as "position weight matrices" (PWMs). Though convenient, the PWM has significant limitations, in particular the assumed independence of positions within the binding motif; and predictions based on PWMs are usually not very specific to known functional sites. Analysis here on binding sites in yeast suggests that correlation of dinucleotides is not limited to near-neighbours, but can extend over considerable gaps. METHODOLOGY/PRINCIPAL FINDINGS:I describe a straightforward generalization of the PWM model, that considers frequencies of dinucleotides instead of individual nucleotides. Unlike previous efforts, this method considers all dinucleotides within an extended binding region, and does not make an attempt to determine a priori the significance of particular dinucleotide correlations. I describe how to use a "dinucleotide weight matrix" (DWM) to predict binding sites, dealing in particular with the complication that its entries are not independent probabilities. Benchmarks show, for many factors, a dramatic improvement over PWMs in precision of predicting known targets. In most cases, significant further improvement arises by extending the commonly defined "core motifs" by about 10 bp on either side. Though this flanking sequence shows no strong motif at the nucleotide level, the predictive power of the dinucleotide model suggests that the "signature" in DNA sequence of protein-binding affinity extends beyond the core protein-DNA contact region. CONCLUSION/SIGNIFICANCE:While computationally more demanding and slower than PWM-based approaches, this dinucleotide method is straightforward, both conceptually and in implementation, and can serve as a basis for future improvements.
| S-EPMC2842295 | biostudies-literature
Project description:Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors.Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers.Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors.
| S-EPMC3208542 | biostudies-literature
Project description:UnlabelledDespite considerable efforts to date, DNA motif prediction in whole genome remains a challenge for researchers. Currently the genome wide motif prediction tools required either direct pattern sequence (for single motif) or weight matrix (for multiple motifs). Although there are known motif pattern databases and tools for genome level prediction but no tool for weight matrix construction. Considering this, we developed a D-MATRIX tool which predicts the different types of weight matrix based on user defined aligned motif sequence set and motif width. For retrieval of known motif sequences user can access the commonly used databases such as TFD, RegulonDB, DBTBS, Transfac. D-MATRIX program uses a simple statistical approach for weight matrix construction, which can be converted into different file formats according to user requirement. It provides the possibility to identify the conserved motifs in the co-regulated genes or whole genome. As example, we successfully constructed the weight matrix of LexA transcription factor binding site with the help of known sos-box cis-regulatory elements in Deinococcus radiodurans genome. The algorithm is implemented in C-Sharp and wrapped in ASP.Net to maintain a user friendly web interface. D-MATRIX tool is accessible through the CIMAP domain network.Availabilityhttp://203.190.147.116/dmatrix/
| S-EPMC2737498 | biostudies-literature
Project description:BACKGROUND:The high degree of sequence heterogeneity found in Hepatitis C virus (HCV) isolates, makes robust nucleic acid-based assays difficult to generate. Polymerase chain reaction based techniques, require efficient and specific sequence recognition. Generation of robust primers capable of recognizing a wide range of isolates is a difficult task. RESULTS:A position weight matrix (PWM) and a consensus sequence were built for each region of HCV and subsequently assembled into a whole genome consensus sequence and PWM. For each of the 10 regions, the number of occurrences of each base at a given position was compiled. These counts were converted to frequencies that were used to calculate log odds scores. Using over 100 complete and 14,000 partial HCV genomes from GenBank, a consensus HCV genome sequence was generated along with a PWM reflecting heterogeneity at each position. The PWM was used to identify the most conserved regions for primer design. CONCLUSIONS:This approach allows rapid identification of conserved regions for robust primer design and is broadly applicable to sets of genomes with all levels of genetic heterogeneity.
| S-EPMC130017 | biostudies-literature
Project description: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.
| S-EPMC3166302 | biostudies-literature
Project description:The annotation of newly sequenced bacterial genomes begins with running several automatic analysis methods, with major emphasis on the identification of protein-coding genes. DNA sequences are heterogeneous in local nucleotide composition and this leads sometimes to sequences being annotated as authentic genes when they are not protein-coding genes or are true but uncharacterized protein-coding genes. This first annotation step is generally followed by an expert manual annotation of the predicted genes. The genomic data (sequence and annotations) organized in an appropriate databank file format is subsequently submitted to an entry point of the International Nucleotide Sequence Database. These procedures are inevitably subject to mistakes, and this can lead to unintentional syntactic annotation errors being stored in public databanks. Here, we present a new web program, MICheck (MIcrobial genome Checker), that enables rapid verification of sets of annotated genes and frameshifts in previously published bacterial genomes. The web interface allows one easily to investigate the MICheck results, i.e. inaccurate or missed gene annotations: a graphical representation is drawn, in which the genomic context of a unique coding DNA sequence annotation or a predicted frameshift is given, using information on the coding potential (curves) and annotation of the neighbouring genes. We illustrate some capabilities of the MICheck site through the analysis of 20 bacterial genomes, 9 of which were selected for their 'Reviewed' status in the National Center for Biotechnology Information (NCBI) Reference Sequence Project (RefSeq). In the context of the numerous re-annotation projects for microbial genomes, this tool can be seen as a preliminary step before the functional re-annotation step to check quickly for missing or wrongly annotated genes. The MICheck website is accessible at the following address: http://www.genoscope.cns.fr/agc/tools/micheck.
| S-EPMC1160258 | biostudies-literature
Project description:BACKGROUND: The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species. RESULTS: In this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli). CONCLUSIONS: Our pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.
| S-EPMC3549806 | biostudies-literature
Project description:BACKGROUND:Statistics in ranked lists is useful in analysing molecular biology measurement data, such as differential expression, resulting in ranked lists of genes, or ChIP-Seq, which yields ranked lists of genomic sequences. State of the art methods study fixed motifs in ranked lists of sequences. More flexible models such as position weight matrix (PWM) motifs are more challenging in this context, partially because it is not clear how to avoid the use of arbitrary thresholds. RESULTS:To assess the enrichment of a PWM motif in a ranked list we use a second ranking on the same set of elements induced by the PWM. Possible orders of one ranked list relative to another can be modelled as permutations. Due to sample space complexity, it is difficult to accurately characterize tail distributions in the group of permutations. In this paper we develop tight upper bounds on tail distributions of the size of the intersection of the top parts of two uniformly and independently drawn permutations. We further demonstrate advantages of this approach using our software implementation, mmHG-Finder, which is publicly available, to study PWM motifs in several datasets. In addition to validating known motifs, we found GC-rich strings to be enriched amongst the promoter sequences of long non-coding RNAs that are specifically expressed in thyroid and prostate tissue samples and observed a statistical association with tissue specific CpG hypo-methylation. CONCLUSIONS:We develop tight bounds that can be calculated in polynomial time. We demonstrate utility of mutual enrichment in motif search and assess performance for synthetic and biological datasets. We suggest that thyroid and prostate-specific long non-coding RNAs are regulated by transcription factors that bind GC-rich sequences, such as EGR1, SP1 and E2F3. We further suggest that this regulation is associated with DNA hypo-methylation.
| S-EPMC4021615 | biostudies-literature
Project description:BackgroundThe Neogastropoda is a highly diversified group of predatory marine snails (Gastropoda: Caenogastropoda). Traditionally, its monophyly has been widely accepted based on several morphological synapomorphies mostly related with the digestive system. However, recent molecular phylogenetic studies challenged the monophyly of Neogastropoda due to the inclusion of representatives of other caenogastropod lineages (e.g. Littorinimorpha) within the group. Neogastropoda has been classified into up to six superfamilies including Buccinoidea, Muricoidea, Olivoidea, Pseudolivoidea, Conoidea, and Cancellarioidea. Phylogenetic relationships among neogastropod superfamilies remain unresolved.ResultsThe complete mitochondrial (mt) genomes of seven Neogastropoda (Bolinus brandaris, Cancellaria cancellata, Conus borgesi, Cymbium olla, Fusiturris similis, Nassarius reticulatus, and Terebra dimidiata) and of the tonnoidean Cymatium parthenopeum (Littorinimorpha), a putative sister group to Neogastropoda, were sequenced. In addition, the partial sequence of the mitochondrial genome of the calyptraeoidean Calyptraea chinensis (Littorinimorpha) was also determined. All sequenced neogastropod mt genomes shared a highly conserved gene order with only two instances of tRNA gene translocation. Phylogenetic relationships of Neogastropoda were inferred based on the 13 mt protein coding genes (both at the amino acid and nucleotide level) of all available caenogastropod mitochondrial genomes. Maximum likelihood (ML) and Bayesian inference (BI) phylogenetic analyses failed to recover the monophyly of Neogastropoda due to the inclusion of the tonnoidean Cymatium parthenopeum within the group. At the superfamily level, all phylogenetic analyses questioned the taxonomic validity of Muricoidea, whereas the monophyly of Conoidea was supported by most phylogenetic analyses, albeit weakly. All analyzed families were recovered as monophyletic except Turridae due to the inclusion of Terebridae. Further phylogenetic analyses based on either a four mt gene data set including two additional Littorinimorpha or combining mt and nuclear sequence data also rejected the monophyly of Neogastropoda but rendered rather unresolved topologies. The phylogenetic performance of each mt gene was evaluated under ML. The total number of resolved internal branches of the reference (whole-mt genome) topology was not recovered in any of the individual gene phylogenetic analysis. The cox2 gene recovered the highest number of congruent internal branches with the reference topology, whereas the combined tRNA genes, cox1, and atp8 showed the lowest phylogenetic performance.ConclusionPhylogenetic analyses based on complete mt genome data resolved a higher number of internal branches of the caenogastropod tree than individual mt genes. All performed phylogenetic analyses agreed in rejecting the monophyly of the Neogastropoda due to the inclusion of Littorinimorpha lineages within the group. This result challenges morphological evidence, and prompts for further re-evaluation of neogastropod morphological synapomorphies. The important increase in number of analyzed positions with respect to previous studies was not enough to achieve conclusive results regarding phylogenetic relationships within Neogastropoda. In this regard, sequencing of complete mtDNAs from all closely related caenogastropod lineages is needed. Nevertheless, the rapid radiation at the origin of Neogastropoda may not allow full resolution of this phylogeny based only on mt data, and in parallel more nuclear sequence data will also need to be incorporated into the phylogenetic analyses.
| S-EPMC2741453 | biostudies-literature
Project description:BackgroundThe identifying of binding sites for transcription factors is a key component of gene regulatory network analysis. This is often done using position-weight matrices (PWMs). Because of the importance of in silico mapping of tentative binding sites, we previously developed an approach for PWM optimization that substantially improves the accuracy of such mapping.ResultsThe present work implements the optimization algorithm applied to the existing PWM for GATA-3 transcription factor and builds a new di-nucleotide PWM. The existing available PWM is based on experimental data adopted from Jaspar. The optimized PWM substantially improves the sensitivity and specificity of the TF mapping compared to the conventional applications. The refined PWM also facilitates in silico identification of novel binding sites that are supported by experimental data. We also describe uncommon positioning of binding motifs for several T-cell lineage specific factors in human promoters.ConclusionOur proposed di-nucleotide PWM approach outperforms the conventional mono-nucleotide PWM approach with respect to GATA-3. Therefore our new di-nucleotide PWM provides new insight into plausible transcriptional regulatory interactions in human promoters.
| S-EPMC3481455 | biostudies-literature