ReLA, a local alignment search tool for the identification of distal and proximal gene regulatory regions and their conserved transcription factor binding sites

González, Santi; Montserrat-Sentís, Bàrbara; Sánchez, Friman; Puiggròs, Montserrat; Ramírez, Alex; Torrents, David

doi:10.1093/bioinformatics/bts024

Cited by 12 publications

(11 citation statements)

References 32 publications

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“…Yet, up to 48 ProStar predicted regions are not proximate (<1.2 kb) to any 2012-annotated TSS. Intriguingly, the attempt of validating ProStar predicted regions using methods based on interspecies sequence conservation, such as ReLA (28), yielded a low success rate (9%), providing further evidences that ProStar locates putative promoters in genomic regions where phylogenetic footprinting finds no signal.…”

Section: Resultsmentioning

confidence: 99%

“…TFBS conservation was determined from the comparison of boxes among human, mouse and rat according to the UCSC TFBS conservation track using matrices obtained from TRANSFAC database (27). In addition, we used Regulatory region Local Alignment (ReLA) algorithm (28), a footprinting-based program for the detection of conserved clusters of TFBSs, to determine whether the regions predicted by ProStar would also be detectable as sequence-based only promoter signals.…”

Section: Methodsmentioning

confidence: 99%

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Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Durán

Djebali

González

et al. 2013

Nucleic Acids Research

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Although protein recognition of DNA motifs in promoter regions has been traditionally considered as a critical regulatory element in transcription, the location of promoters, and in particular transcription start sites (TSSs), still remains a challenge. Here we perform a comprehensive analysis of putative core promoter sequences relative to non-annotated predicted TSSs along the human genome, which were defined by distinct DNA physical properties implemented in our ProStar computational algorithm. A representative sampling of predicted regions was subjected to extensive experimental validation and analyses. Interestingly, the vast majority proved to be transcriptionally active despite the lack of specific sequence motifs, indicating that physical signaling is indeed able to detect promoter activity beyond conventional TSS prediction methods. Furthermore, highly active regions displayed typical chromatin features associated to promoters of housekeeping genes. Our results enable to redefine the promoter signatures and analyze the diversity, evolutionary conservation and dynamic regulation of human core promoters at large-scale. Moreover, the present study strongly supports the hypothesis of an ancient regulatory mechanism encoded by the intrinsic physical properties of the DNA that may contribute to the complexity of transcription regulation in the human genome.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Durán

Djebali

González

et al. 2013

Nucleic Acids Research

Self Cite

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“…EMMA identified CRMs in regions that were previously classified nonfunctional sites and further showed that distal CRMs were more conserved than promoter-proximal CRMs. Another approach, REgulatory region Local Alignment tool (ReLA; González et al 2012), uses a local alignment strategy to identify regulatory regions that are characterized by the presence of conserved clusters of binding sites. Such regions can represent enhancers as well as promoters.…”

Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution

Thompson

Regev

Roy

2015

Annu. Rev. Cell Dev. Biol.

166

129

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Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

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“…The search of remote gene regulatory regions for rSNPs is very difficult due to the absence of regular patterns in localizations and sizes of distal regulatory units (enhancers, silencers, and LCRs). Since at the DNA level gene regulatory regions represent clusters of TFBSs [26]; [27] computation approaches based on the search of TFBSs clusters were elaborated [28]; [29]; [30]. Despite of these approaches led to discovery of some functional enhances [29]; [31], their genome wide application is problematic because of too many false-positives [30]; [32].…”

Section: Introductionmentioning

confidence: 99%

Detection of Regulatory SNPs in Human Genome Using ChIP-seq ENCODE Data

et al. 2013

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A vast amount of SNPs derived from genome-wide association studies are represented by non-coding ones, therefore exacerbating the need for effective identification of regulatory SNPs (rSNPs) among them. However, this task remains challenging since the regulatory part of the human genome is annotated much poorly as opposed to coding regions. Here we describe an approach aggregating the whole set of ENCODE ChIP-seq data in order to search for rSNPs, and provide the experimental evidence of its efficiency. Its algorithm is based on the assumption that the enrichment of a genomic region with transcription factor binding loci (ChIP-seq peaks) indicates its regulatory function, and thereby SNPs located in this region are more likely to influence transcription regulation. To ensure that the approach preferably selects functionally meaningful SNPs, we performed enrichment analysis of several human SNP datasets associated with phenotypic manifestations. It was shown that all samples are significantly enriched with SNPs falling into the regions of multiple ChIP-seq peaks as compared with the randomly selected SNPs. For experimental verification, 40 SNPs falling into overlapping regions of at least 7 TF binding loci were selected from OMIM. The effect of SNPs on the binding of the DNA fragments containing them to the nuclear proteins from four human cell lines (HepG2, HeLaS3, HCT-116, and K562) has been tested by EMSA. A radical change in the binding pattern has been observed for 29 SNPs, besides, 6 more SNPs also demonstrated less pronounced changes. Taken together, the results demonstrate the effective way to search for potential rSNPs with the aid of ChIP-seq data provided by ENCODE project.

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ReLA, a local alignment search tool for the identification of distal and proximal gene regulatory regions and their conserved transcription factor binding sites

Cited by 12 publications

References 32 publications

Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution

Detection of Regulatory SNPs in Human Genome Using ChIP-seq ENCODE Data

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