Volume 14, Issue 1 - Special Issue: Computational miRNomics / Guest Editor: Jens Allmer

Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

MicroRNAs (miRNAs) are small RNA molecules which are known to take part in post-transcriptional regulation of gene expression. Here, VANESA, an existing platform for reconstructing, visualizing, and analysis of large biological networks, has been further expanded to include all experimentally validated human miRNAs available within miRBase, TarBase and miRTarBase. This is done by integrating a custom hybrid miRNA database to DAWIS-M.D., VANESA’s main data source, enabling the visualization and analysis of miRNAs within large biological pathways such as those found within the Kyoto Encyclopedia of Genes and Genomes (KEGG). Interestingly, 99.15 % of human KEGG pathways either contain genes which are targeted by miRNAs or harbor them. This is mainly due to the high number of interaction partners that each miRNA could have (e.g.: hsa-miR-335-5p targets 2544 genes and 71 miRNAs target NUFIP2 ). We demonstrate the usability of our system by analyzing the measles virus KEGG pathway as a proof-of-principle model and further highlight the importance of integrating miRNAs (both experimentally validated and predicted) into biological networks for the elucidation of novel miRNA-mRNA interactions of biological importance.

The miRBase currently reports more than 25,000 microRNAs in several hundred genomes that belong to more than 1000 families of homologous sequences. Quantitative investigations of miRNA gene evolution requires the construction of data sets that are consistent in their coverage and include those genomes that are of interest in a given study. Given the size and structure of data, this can be achieved only with the help of a fully automatic pipeline that improves the available seed alignments, extends the set of available sequences by homology search, and reliably identifies true positive homology search results. Here we describe the current progress towards such a system, emphasizing the task of improving and completing the initial seed alignment.

MicroRNAs are a class of small non-coding RNAs that are involved in many important biological processes and the dysfunction of microRNA has been associated with many diseases. The seed region of a microRNA is of crucial importance to its target recognition. Mutations in microRNA seed regions may disrupt the binding of microRNAs to their original target genes and make them bind to new target genes. Here we use a knowledge-based computational method to systematically predict the functional effects of all the possible single nucleotide mutations in human microRNA seed regions. The result provides a comprehensive reference for the functional assessment of the impacts of possible natural and artificial single nucleotide mutations in microRNA seed regions.

Increase in soil salt causes osmotic and ionic stress to plants, which inhibits their growth and productivity. Rice production is also hampered by salinity and the effect of salt is most severe at the seedling and reproductive stages. Salainity tolerance is a quantitative property controlled by multiple genes coding for signaling molecules, ion transporters, metabolic enzymes and transcription regulators. MicroRNAs are key modulators of gene-expression that act at the post-transcriptional level by translation repression or transcript cleavage. They also play an important role in regulating plant’s response to salt-stress. In this work we adopted the approach of comparative and integrated data-mining to understand the miRNA-mediated regulation of salt-stress in rice. We profiled and compared the miRNA regulations using natural varieties and transgenic lines with contrasting behaviors in response to salt-stress. The information obtained from sRNAseq, RNAseq and degradome datasets was integrated to identify the salt-deregulated miRNAs, their targets and the associated metabolic pathways. The analysis revealed the modulation of many biological pathways, which are involved in salt-tolerance and play an important role in plant phenotype and physiology. The end modifications of the miRNAs were also studied in our analysis and isomiRs having a dynamic role in salt-tolerance mechanism were identified.