Web Based

BACKGROUND: The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs. RESULTS: For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs.

microRNAs and microRNA-independent RNA-binding proteins are 2 classes of post-transcriptional regulators that have been shown to cooperate in gene-expression regulation. We compared the genome-wide target sets of microRNAs and RBPs identified by recent CLIP-Seq technologies, finding that RBPs have distinct target sets and favor gene interaction network hubs. To identify microRNAs and RBPs with a similar functional context, we developed simiRa, a tool that compares enriched functional categories such as pathways and GO terms.

Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression.

Validation of clinical biomarkers and response to therapy is a challenging topic in cancer research. An important source of information for virtual validation is the datasets generated from multi-center cancer research projects such as The Cancer Genome Atlas project (TCGA). These data enable investigation of genetic and epigenetic changes responsible for cancer onset and progression, response to cancer therapies, and discovery of the molecular profiles of various cancers.