DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association

microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in www.microrna.gr/microT-v4

DIANA-microT web server: elucidating microRNA functions through target prediction

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into account hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findrComment: New result and method sections added. 38 pages, 4 figures, 1 table. Supplementary: 20 pages, 10 figures, 2 table

Down-Regulation of miR-92 in Breast Epithelial Cells and in Normal but Not Tumour Fibroblasts Contributes to Breast Carcinogenesis

Background MicroRNA (miR) expression is commonly dysregulated in many cancers, including breast. MiR–92 is one of six miRs encoded by the miR-17-92 cluster, one of the best-characterised oncogenic miR clusters. We examined expression of miR–92 in the breast epithelium and stroma during breast cancer progression. We also investigated the role of miR–92 in fibroblasts in vitro and showed that down-regulation in normal fibroblasts enhances the invasion of breast cancer epithelial cells. Methodology/Principal Findings We used laser microdissection (LMD) to isolate epithelial cells from matched normal, DCIS and invasive tissue from 9 breast cancer patients and analysed miR–92 expression by qRT-PCR. Expression of ERβ1, a direct miR–92 target, was concurrently analysed for each case by immunohistochemistry. LMD was also used to isolate matched normal (NFs) and cancer-associated fibroblasts (CAFs) from 14 further cases. Effects of miR–92 inhibition in fibroblasts on epithelial cell invasion in vitro was examined using a Matrigel™ assay. miR– 92 levels decreased in microdissected epithelial cells during breast cancer progression with highest levels in normal breast epithelium, decreasing in DCIS (p<0.01) and being lowest in invasive breast tissue (p<0.01). This was accompanied by a shift in cell localisation of ERβ1 from nuclear expression in normal breast epithelium to increased cytoplasmic expression during progression to DCIS (p = 0.0078) and invasive breast cancer (p = 0.031). ERβ1 immunoreactivity was also seen in stromal fibroblasts in tissues. Where miR–92 expression was low in microdissected NFs this increased in matched CAFs; a trend also seen in cultured primary fibroblasts. Down-regulation of miR–92 levels in NFs but not CAFs enhanced invasion of both MCF–7 and MDA-MB–231 breast cancer epithelial cells. Conclusions miR–92 is gradually lost in breast epithelial cells during cancer progression correlating with a shift in ERβ1 immunoreactivity from nuclei to the cytoplasm. Our data support a functional role in fibroblasts where modification of miR–92 expression can influence the invasive capacity of breast cancer epithelial cells. However in silico analysis suggests that ERβ1 may not be the most important miR–92 target in breast cancer

Approximate regional sequence matching for genomic databases

Recent advances in computational biology have raised sequence matching requirements that result in new types of sequence database problems. In this work, we introduce an important class of such problems, the Approximate Regional Sequence Matching (ARSM) problem. Given a data and a pattern sequence, an ARSM result is an approximate occurrence of a region of the pattern in the data sequence under two conditions. First, the region must contain a predetermined area of the pattern sequence, termed core. Second, the allowable deviation between the region of the pattern and its occurrence in the data sequence depends on the length of the regio

Publishing diachronic life science linked data

The Linked Data paradigm involves practices to publish, share, and connect data on the Web. Thus, it is a compelling approach for the dissemination and re-use of scientific data, realizing the vision of the so-called Linked Science. However, by just converting legacy scientific data as Linked Data, we do not fully meet the requirements of data re-use. Scientific data is evolving data. To ensure re-use and allow exploitation and validation of scientific results, several challenges related to scientific data dynamics should be tackled. In this paper, we deal with the publication of diachronic life science linked data. We propose a change model based on RDF to capture versioned entities. Based on this model we convert legacy data from biological databases as diachronic linked data. Our linked data server can assist biologists to explore bio- logical entities and their evolution by either using SPARQL queries or navigating among entity versions. All services are publicly available at http://diana.imis.athena-innovation.gr/lod