ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input

A survey of DNA motif finding algorithms

Background: Unraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.Results: Earlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.Conclusion: Despite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.Peer reviewedComputer Scienc

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC

On the use of algorithms to discover motifs in DNA sequences

Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging for specialists nowadays due to the difficulties they find to deeply understand gene regulatory mechanisms, especially when analyzing binding sites in DNA. These sites or specific nucleotide sequences are known to be responsible for transcription processes. Thus, this work aims at providing an updated overview on strategies developed to discover meaningful motifs in DNA-related sequences, and, in particular, their attempts to find out relevant binding sites. From all existing approaches, this work is focused on dictionary, ensemble, and artificial intelligence-based algorithms since they represent the classical and the leading ones, respectively.Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00Junta de Andalucia P07-TIC- 02611

Cis-regulatory module detection using constraint programming

We propose a method for finding CRMs in a set of co-regulated genes. Each CRM consists of a set of binding sites of transcription factors. We wish to find CRMs involving the same transcription factors in multiple sequences. Finding such a combination of transcription factors is inherently a combinatorial problem. We solve this problem by combining the principles of itemset mining and constraint programming. The constraints involve the putative binding sites of transcription factors, the number of sequences in which they co-occur and the proximity of the binding sites. Genomic background sequences are used to assess the significance of the modules. We experimentally validate our approach and compare it with state-of-the-art techniques

The Effect of Orthology and Coregulation on Detecting Regulatory Motifs

Background: Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. Methodology: We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. Results and Conclusions: Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/,kmarchal/Supplementary_Storms_Valerie_PlosONE

Multi-scale genetic network inference based on time series gene expression profiles

This work integrates multi-scale clustering and short-time correlation to estimate genetic networks with different time resolutions and detail levels. Gene expression data are noisy and large scale. Clustering is widely used to group genes with similar pattern. The cluster centers can be used to infer the genetic networks among these clusters. This work introduces the Multi-scale Fuzzy K-means clustering algorithm to uncover groups of coregulated genes and capture the networks in different levels of detail.;Time series expression profiles provide dynamic information for inferring gene regulatory relationships. Large scale network inference, identifying the transient interactions and feedback loops as well as differentiating direct and indirect interactions are among the major challenges of genetic network inference. Time correlation can estimate the time delay and edge direction. Partial correlation and directed-separation theory help differentiate direct and indirect interactions and identify feedback loops. This work introduces the constraint-based time-correlation (CBTC) network inference algorithm that combines these methods with time correlation estimation to more fully characterize genetic networks. Gene expression regulation can happen in specific time periods and conditions instead of across the whole expression profile. Short-time correlation can capture transient interactions.;The network discovery algorithm was mainly validated using yeast cell cycle data. The algorithm successfully identified the yeast cell cycle development stages, cell cycle and negative feedback loops, and indicated how the networks dynamically changes over time. The inferred networks reflect most interactions previously identified by genome-wide location analysis and match the extant literature. At detailed network level, the inferred networks provide more detailed information about genes (or clusters) and the interactions among them. Interesting genes, clusters and interactions were identified, which match the literature and the gene ontology information and provide hypotheses for further studies

Combining comparative genomics with de novo motif discovery to identify human transcription factor DNA-binding motifs

BACKGROUND: As more and more genomes are sequenced, comparative genomics approaches provide a methodology for identifying conserved regulatory elements that may be involved in gene regulations. RESULTS: We developed a novel method to combine comparative genomics with de novo motif discovery to identify human transcription factor binding motifs that are overrepresented and conserved in the upstream regions of a set of co-regulated genes. The method is validated by analyzing a well-characterized muscle specific gene set, and the results showed that our approach performed better than the existing programs in terms of sensitivity and prediction rate. CONCLUSION: The newly developed method can be used to extract regulatory signals in co-regulated genes, which can be derived from the microarray clustering analysis