Notable clustering of transcription-factor-binding motifs in human pericentric regions and its biological significance.
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ABSTRACT:
SUBMITTER: Iwasaki Y
PROVIDER: S-EPMC3761090 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundClassically, models of DNA-transcription factor binding sites (TFBSs) have been based on relatively few known instances and have treated them as sites of fixed length using position weight matrices (PWMs). Various extensions to this model have been proposed, most of which take account of dependencies between the bases in the binding sites. However, some transcription factors are known to exhibit some flexibility and bind to DNA in more than one possible physical configuration. In some cases this variation is known to affect the function of binding sites. With the increasing volume of ChIP-seq data available it is now possible to investigate models that incorporate this flexibility. Previous work on variable length models has been constrained by: a focus on specific zinc finger proteins in yeast using restrictive models; a reliance on hand-crafted models for just one transcription factor at a time; and a lack of evaluation on realistically sized data sets.ResultsWe re-analysed binding sites from the TRANSFAC database and found motivating examples where our new variable length model provides a better fit. We analysed several ChIP-seq data sets with a novel motif search algorithm and compared the results to one of the best standard PWM finders and a recently developed alternative method for finding motifs of variable structure. All the methods performed comparably in held-out cross validation tests. Known motifs of variable structure were recovered for p53, Stat5a and Stat5b. In addition our method recovered a novel generalised version of an existing PWM for Sp1 that allows for variable length binding. This motif improved classification performance.ConclusionsWe have presented a new gapped PWM model for variable length DNA binding sites that is not too restrictive nor over-parameterised. Our comparison with existing tools shows that on average it does not have better predictive accuracy than existing methods. However, it does provide more interpretable models of motifs of variable structure that are suitable for follow-up structural studies. To our knowledge, we are the first to apply variable length motif models to eukaryotic ChIP-seq data sets and consequently the first to show their value in this domain. The results include a novel motif for the ubiquitous transcription factor Sp1.
| S-EPMC2824720 | biostudies-literature
Project description:A number of previous studies have predicted transcription factor binding sites (TFBSs) by exploiting the position of genomic landmarks like the transcriptional start site (TSS). The studies' methods are generally too computationally intensive for genome-scale investigation, so the full potential of 'positional regulomics' to discover TFBSs and determine their function remains unknown. Because databases often annotate the genomic landmarks in DNA sequences, the methodical exploitation of positional regulomics has become increasingly urgent. Accordingly, we examined a set of 7914 human putative promoter regions (PPRs) with a known TSS. Our methods identified 1226 eight-letter DNA words with significant positional preferences with respect to the TSS, of which only 608 of the 1226 words matched known TFBSs. Many groups of genes whose PPRs contained a common word displayed similar expression profiles and related biological functions, however. Most interestingly, our results included 78 words, each of which clustered significantly in two or three different positions relative to the TSS. Often, the gene groups corresponding to different positional clusters of the same word corresponded to diverse functions, e.g. activation or repression in different tissues. Thus, different clusters of the same word likely reflect the phenomenon of 'positional regulation', i.e. a word's regulatory function can vary with its position relative to a genomic landmark, a conclusion inaccessible to methods based purely on sequence. Further integrative analysis of words co-occurring in PPRs also yielded 24 different groups of genes, likely identifying cis-regulatory modules de novo. Whereas comparative genomics requires precise sequence alignments, positional regulomics exploits genomic landmarks to provide a 'poor man's alignment'. By exploiting the phenomenon of positional regulation, it uses position to differentiate the biological functions of subsets of TFBSs sharing a common sequence motif.
| S-EPMC2377430 | biostudies-literature
Project description:Binding of transcription factors to DNA is one of the keystones of gene regulation. The existence of statistical dependencies between binding site positions is widely accepted, while their relevance for computational predictions has been debated. Building probabilistic models of binding sites that may capture dependencies is still challenging, since the most successful motif discovery approaches require numerical optimization techniques, which are not suited for selecting dependency structures. To overcome this issue, we propose sparse local inhomogeneous mixture (Slim) models that combine putative dependency structures in a weighted manner allowing for numerical optimization of dependency structure and model parameters simultaneously. We find that Slim models yield a substantially better prediction performance than previous models on genomic context protein binding microarray data sets and on ChIP-seq data sets. To elucidate the reasons for the improved performance, we develop dependency logos, which allow for visual inspection of dependency structures within binding sites. We find that the dependency structures discovered by Slim models are highly diverse and highly transcription factor-specific, which emphasizes the need for flexible dependency models. The observed dependency structures range from broad heterogeneities to sparse dependencies between neighboring and non-neighboring binding site positions.
| S-EPMC4605289 | biostudies-literature
Project description:Purpose: 224 GSM Samples form GSE32970 and GSE29692 was reanalyzed to find the binding sites of 542 TFs. Methods: 1. iFORM (incorporating Find Occurrence of Regulatory Motifs) is a tool we designed to scan through sequences in search of binding sites that match given motifs. 2. We mapped motif-binding protein information found in TRANSFAC, JASPAR, and UniPROBE databases to 542 coding genes, using GeneCards (Rebhan et al., 1997) and UniProt Knowledgebase (Magrane and Consortium, 2011). The position-specific weight matrices of the 542 TFs, corresponding to 796 motif models, were collected from these databases. 3.We used a uniform set of open chromatin elements (DNaseI hypersensitive sites, DHSs) in 133 ENCODE cell types, based on DNase-seq data produced by the "Open Chromatin" (Duke/UNC/UT-A)(GSE32970) and University of Washington (UW) ENCODE groups (GSE29692)from the project inception in 2007 through the ENCODE January 2011 data freeze. Processing pipeline: Combined all replicates for a given cell type; Subsampled the result at a level of 30 million tags; Ran results through the Stam Lab hotspot pipeline. Hotspots were identified using a relaxed threshold. NarrowPeaks were generated by first thresholding hotspots at FDR 1%, and then locating local maxima of the tag density within the hotspots. The 133 narrowPeaks are base sequences for the given motifs scan. Result: We used our iFORM with a custom library of all 796 motifs to scan for motif instances in 133 ENCODE DHSs cell types separately. Result in 133 files correspond to 133 cell types, each file contains binding sites of 542 TFs in a cell type.
2014-10-12 | GSE53962 | GEO
Project description:To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.
| S-EPMC3509107 | biostudies-literature
Project description:Position-specific scoring matrices (PSSMs) are routinely used to predict transcription factor (TF)-binding sites in genome sequences. However, their reliability to predict novel binding sites can be far from optimum, due to the use of a small number of training sites or the inappropriate choice of parameters when building the matrix or when scanning sequences with it. Measures of matrix quality such as E-value and information content rely on theoretical models, and may fail in the context of full genome sequences. We propose a method, implemented in the program 'matrix-quality', that combines theoretical and empirical score distributions to assess reliability of PSSMs for predicting TF-binding sites. We applied 'matrix-quality' to estimate the predictive capacity of matrices for bacterial, yeast and mouse TFs. The evaluation of matrices from RegulonDB revealed some poorly predictive motifs, and allowed us to quantify the improvements obtained by applying multi-genome motif discovery. Interestingly, the method reveals differences between global and specific regulators. It also highlights the enrichment of binding sites in sequence sets obtained from high-throughput ChIP-chip (bacterial and yeast TFs), and ChIP-seq and experiments (mouse TFs). The method presented here has many applications, including: selecting reliable motifs before scanning sequences; improving motif collections in TFs databases; evaluating motifs discovered using high-throughput data sets.
| S-EPMC3035439 | biostudies-literature
Project description:We study the limits imposed by transcription factor specificity on the maximum number of binding motifs that can coexist in a gene regulatory network, using the SwissRegulon Fantom5 collection of 684 human transcription factor binding sites as a model. We describe transcription factor specificity using regular expressions and find that most human transcription factor binding site motifs are separated in sequence space by one to three motif-discriminating positions. We apply theorems based on the pigeonhole principle to calculate the maximum number of transcription factors that can coexist given this degree of specificity, which is in the order of ten thousand and would fully utilize the space of DNA subsequences. Taking into account an expanded DNA alphabet with modified bases can further raise this limit by several orders of magnitude, at a lower level of sequence space usage. Our results may guide the design of transcription factors at both the molecular and system scale.
| S-EPMC8797260 | biostudies-literature
Project description:Deciphering the interplay between chromatin accessibility and transcription factor (TF) binding is fundamental to understanding transcriptional regulation, control of cellular states, and the establishment of new phenotypes. Recent genome-wide chromatin accessibility profiling studies have provided catalogs of putative open regions, where TFs can recognize their motifs and regulate gene expression programs. Here, we present motif enrichment in differential elements of accessibility (MEDEA), a computational tool that analyzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible regions and lineage-specific motifs associated with TF binding therein. To benchmark MEDEA, we used a panel of reference cell lines profiled by ENCODE and curated by the ENCODE Project Consortium for the ENCODE-DREAM Challenge. By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints, we show that MEDEA improves the detection of motifs associated with known lineage specifiers. We then applied MEDEA to 610 ENCODE DNase-seq data sets, where it revealed significant motifs even when absolute enrichment was low and where it identified novel regulators, such as NRF1 in kidney development. Finally, we show that MEDEA performs well on both bulk and single-cell ATAC-seq data. MEDEA is publicly available as part of our Glossary-GENRE suite for motif enrichment analysis.
| S-EPMC7263192 | biostudies-literature
Project description:Transcription factors (TFs) regulate the expression of gene expression. The binding specificities of many TFs have been deciphered and summarized as position-weight matrices, also called TF motifs. Despite the availability of hundreds of known TF motifs in databases, it remains non-trivial to quickly query and visualize the enrichment of known TF motifs in genomic regions of interest. Towards this goal, we developed TFmotifView, a web server that allows to study the distribution of known TF motifs in genomic regions. Based on input genomic regions and selected TF motifs, TFmotifView performs an overlap of the genomic regions with TF motif occurrences identified using a dynamic P-value threshold. TFmotifView generates three different outputs: (i) an enrichment table and scatterplot calculating the significance of TF motif occurrences in genomic regions compared to control regions, (ii) a genomic view of the organisation of TF motifs in each genomic region and (iii) a metaplot summarizing the position of TF motifs relative to the center of the regions. TFmotifView will contribute to the integration of TF motif information with a wide range of genomic datasets towards the goal to better understand the regulation of gene expression by transcription factors. TFmotifView is freely available at http://bardet.u-strasbg.fr/tfmotifview/.
| S-EPMC7319436 | biostudies-literature
Project description:BackgroundNeuroblastoma, a cancer derived from precursor cells of the sympathetic nervous system, is a major cause of childhood cancer related deaths. The single most important prognostic indicator of poor clinical outcome in this disease is genomic amplification of MYCN, a member of a family of oncogenic transcription factors.MethodologyWe applied MYCN chromatin immunoprecipitation to microarrays (ChIP-chip) using MYCN amplified/non-amplified cell lines as well as a conditional knockdown cell line to determine the distribution of MYCN binding sites within all annotated promoter regions.ConclusionAssessment of E-box usage within consistently positive MYCN binding sites revealed a predominance for the CATGTG motif (p<0.0016), with significant enrichment of additional motifs CATTTG, CATCTG, CAACTG in the MYCN amplified state. For cell lines over-expressing MYCN, gene ontology analysis revealed enrichment for the binding of MYCN at promoter regions of numerous molecular functional groups including DNA helicases and mRNA transcriptional regulation. In order to evaluate MYCN binding with respect to other genomic features, we determined the methylation status of all annotated CpG islands and promoter sequences using methylated DNA immunoprecipitation (MeDIP). The integration of MYCN ChIP-chip and MeDIP data revealed a highly significant positive correlation between MYCN binding and DNA hypermethylation. This association was also detected in regions of hemizygous loss, indicating that the observed association occurs on the same homologue. In summary, these findings suggest that MYCN binding occurs more commonly at CATGTG as opposed to the classic CACGTG E-box motif, and that disease associated over expression of MYCN leads to aberrant binding to additional weaker affinity E-box motifs in neuroblastoma. The co-localization of MYCN binding and DNA hypermethylation further supports the dual role of MYCN, namely that of a classical transcription factor affecting the activity of individual genes, and that of a mediator of global chromatin structure.
| S-EPMC2781550 | biostudies-literature