microPIR2: a comprehensive database for human-mouse comparative study of microRNA-promoter interactions.

microRNA (miRNA)-promoter interaction resource (microPIR) is a public database containing over 15 million predicted miRNA target sites located within human promoter sequences. These predicted targets are presented along with their related genomic and experimental data, making the microPIR database the most comprehensive repository of miRNA promoter target sites. Here, we describe major updates of the microPIR database including new target predictions in the mouse genome and revised human target predictions. The updated database (microPIR2) now provides ∼80 million human and 40 million mouse predicted target sites. In addition to being a reference database, microPIR2 is a tool for comparative analysis of target sites on the promoters of human-mouse orthologous genes. In particular, this new feature was designed to identify potential miRNA-promoter interactions conserved between species that could be stronger candidates for further experimental validation. We also incorporated additional supporting information to microPIR2 such as nuclear and cytoplasmic localization of miRNAs and miRNA-disease association. Extra search features were also implemented to enable various investigations of targets of interest. Database URL: http://www4a.biotec.or.th/micropir2

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 3'-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 genomic features. Furthermore, functional information of target genes including gene ontologies, KEGG pathways, and OMIM associations are provided. The built-in genome browser of microPIR provides a comprehensive view of multidimensional genomic data. Finally, microPIR incorporates a PCR primer design module to facilitate experimental validation. CONCLUSIONS: The proposed microPIR database is a useful integrated resource of microRNA-promoter target interactions for experimental microRNA researchers and computational biologists to study the microRNA regulation through gene promoter. The database can be freely accessed from: http://www4a.biotec.or.th/micropir.

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

BACKGROUND: 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). RESULTS: 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. CONCLUSION: 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.