Dataset Information

COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.

ABSTRACT: In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.

SUBMITTER: Ha N

PROVIDER: S-EPMC3525548 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.

Ha Nati N Polychronidou Maria M Lohmann Ingrid I

PloS one 20121218 12

In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficie ...[more]

PMID: 23272209

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Project description:BackgroundThe use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures.ResultsWe develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 10(5) gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.ConclusionsThe presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.

Project description:Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

Project description:BACKGROUND: Eukaryotic promoters are regions containing various sequence motifs necessary to control gene transcription. Much evidence has emerged showing that structural and/or contextual changes in regulatory elements can critically affect cis-regulatory activity. As sequence motifs can be key factors in maintaining complex promoter architectures, one effective approach to further understand the evolution of promoter regions in vertebrates is to compare the abundance and distribution patterns of sequence motifs in these regions between divergent species. When compared with mammals, the chicken (Gallus gallus) has a very different genome composition and sufficient genomic information to make it a good model for the exploration of promoter structure and evolution. RESULTS: More than 10% of chicken genes contained short tandem repeat (STR) in the region 2 kb upstream of promoters, but the total number of STRs observed in chicken is approximately half of that detected in human promoters. In terms of the STR motif frequencies, chicken promoter regions were more similar to other avian and mammalian promoters than these were to the entire chicken genome. Unlike other STRs, nearly half of the trinucleotide repeats found in promoters partly or entirely overlapped with CpG islands, indicating potential association with nucleosome positions. Moreover, the chicken promoters are abundant with sequence motifs such as poly-A, poly-G and G-quadruplexes, especially in the core region, that are otherwise rare in the genome. Most of sequence motifs showed strong functional enrichment for particular gene ontology (GO) categories, indicating roles in regulation of transcription and gene expression, as well as immune response and cognition. CONCLUSIONS: Chicken promoter regions share some, but not all, of the structural features observed in mammalian promoters. The findings presented here provide empirical evidence suggesting that the frequencies and locations of STR motifs have been conserved through promoter evolution in a lineage-specific manner. Correlation analysis between GO categories and sequence motifs suggests motif-specific constraints acting on gene function.

Dataset Information

COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.

Publications

COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.

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