microPIR: An Integrated Database of MicroRNA Target Sites within Human Promoter Sequences

Background: microRNAs are generally understood to regulate gene expression through binding to target sequences within 39-UTRs of mRNAs. Therefore, computational prediction of target sites is usually restricted to these gene regions. Recent experimental studies though have suggested that microRNAs may alternatively modulate gene expression by interacting with promoters. A database of potential microRNA target sites in promoters would stimulate research in this field leading to more understanding of complex microRNA regulatory mechanism. Methodology: We developed a database hosting predicted microRNA target sites located within human promoter sequences and their associated genomic features, called microPIR (microRNA-Promoter Interaction Resource). microRNA seed sequences were used to identify perfect complementary matching sequences in the human promoters and the potential target sites were predicted using the RNAhybrid program..15 million target sites were identified which are located within 5000 bp upstream of all human genes, on both sense and antisense strands. The experimentally confirmed argonaute (AGO) binding sites and EST expression data including the sequence conservation across vertebrate species of each predicted target are presented for researchers to appraise the quality of predicted target sites. The microPIR database integrates various annotated genomic sequence databases, e.g. repetitive elements, transcription factor binding sites, CpG islands, and SNPs, offering users the facility to extensively explore relationships among target sites and other genomi

System overview of microPIR database.

<p>This is a three-tier system overview of the microPIR database displaying the data sources and web interface features.</p

Search input and report output from the advanced search module.

<p>(A) The advanced search page is separated into three main parts. 1) A target gene or associated miRNA is put as a query. 2) The choices of binding site parameter settings can be adjusted to fit the user's needs. 3) The overlap of target site with specified annotated sequence is allowed as additional search criteria. (B) The list of resulting target sites obtained from search page is displayed. The information of associated miRNA is presented with the direction, chromosomal location, strand, length, upstream location, MFE, and conservation score of each target site including the number of bases with available score data. The number of different annotated sequences overlapping with each predicted target site is also shown. The hypertext link-outs to original sources of gene/miRNA associated information are provided. More target detail which includes a link to primer design is provided on the detail page of each target.</p

Genome browser displaying the resulting target sites with other genomic features.

<p>The position of target site is presented in an integrated view along with other supporting information and genomic annotations on a local genome broswer. The region-level view shows the distribution of putative target sites located within a specified genomic region. Users can highlight (yellow color) to zoom in the interested location for the detail-level view. The target site is displayed in gold box with green line. The conservation score of each nucleotide position (blue color on the bottom) is displayed in the range from 0 to 1. AGO binding-site cluster is represented as distribution of read numbers along the cluster (green color). In this particular case, the presence of miRNA (red color) on the same locus as its binding site represents the <i>cis</i>-regulatory role of miRNA.</p

iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD).</p> <p>Results</p> <p>In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles.</p> <p>Conclusion</p> <p>iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.</p