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Abstract is not available.[1]

MicroRNAs (miRNAs) are small non-coding RNA molecules that are ~22-nt-long sequences capable of suppressing protein synthesis. Previous research has suggested that miRNAs regulate 30% or more of the human protein-coding genes. The aim of this work is to consider various analyzing scenarios in the identification of miRNA-target interactions, as well as to provide an integrated system that will aid in facilitating investigation on the influence of miRNA targets by alternative splicing and the biological function of miRNAs in biological pathways.

Three-prime untranslated regions (3'UTRs) are widely recognized as important post-transcriptional regulatory regions of mRNAs. RNA-binding proteins and small non-coding RNAs such as microRNAs (miRNAs) bind to functional elements within 3'UTRs to influence mRNA stability, translation and localization. These interactions play many important roles in development, metabolism and disease. However, even in the most well-annotated metazoan genomes, 3'UTRs and their functional elements are not well defined.

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.

Plant microRNAs (miRNAs) and trans-acting small interfering RNAs (tasiRNAs) play important roles in a variety of biological processes. Bioinformatics prediction and small RNA (sRNA) cloning are the most important approaches for identification of miRNAs and tasiRNAs and their targets. However, these approaches are not readily accessible to every researcher. Here we present SoMART, a web server for miRNA/tasiRNA analysis resources and tools, which is designed for researchers who are interested in identifying miRNAs or tasiRNAs that potentially regulate genes of interest.

An integrated gene network for Caenorhabditis elegans using data from multiple genome-wide screens encompasses most protein-coding genes and can accurately predict their phenotypes.[1]