Tag Archives: microRNA

Friday SNPpets

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…

Tip of the Week: RepTar, a database of miRNA target sites

microRNAs have become a rich source of research as they probably have a huge effect on gene expression and disease. The human genome may encode over 1,000 miRNAs that target over half of our genes. They might be implicated in a lot of common diseases (which not yet have been picked up in GWAS studies?). They are a fascinating area of biology that has only come of it’s on in the last decade. As such, the number of databases to catalog miRNAs is large. Today’s tip is on a new one, RepTar, which is reported in the upcoming NAR database issue. The niche RepTar is attempting to fill is to get predictions of miRNAs more comprehensive by including new research in the algorithm. This new research suggests there are more possible target sites than previously thought. As mentioned in the article,

Recently, the miRNA binding options were expanded further with the identification of ‘centered sites’, functional miRNA target sites that lack both perfect seed pairing and 3′-compensatory pairing and instead exhibit pairing with the target along 11–12 contiguous pairs at the center of the miRNA (4). While some algorithms relaxed the evolutionary conservation criterion (5–11) and/or offer also predictions of 3′-compensatory sites [e.g. (6,12,13)], few databases offer predictions of the whole repertoire of miRNA targeting patterns. Furthermore to date, no database lists genome-wide prediction of cellular targets of viral miRNAs. These miRNAs lack significant evolutionary conservation and their targets are not necessarily expected to be evolutionarily conserved. In addition, the few identified viral miRNA targets have shown both conventional seed binding and 3′-compensatory binding [e.g. (3,14)].

Here we present a database of genome-wide miRNA target predictions for mouse and human genes, based on the predictions of our novel target prediction algorithm, RepTar

I’ll leave the predictive value up to miRNA researchers, but I thought I’d introduce the site.

While I’m at it, allow me to list a few other miRNA sites from labs and institutes as far flung as China, Italy, Israel, Canada and the U.S.. Perhaps someday I’ll do a comparison.

CircuitsDB, which Jennifer did a great tip of the week tutorial on.

miRBase, which we have a full-length tutorial on.
microRNA.org
HMDD
miRDB
tarBase
miRecords:
PicTar, they have an annotation track for UCSC Genome Browser
miRNA2Disease
PuTmiR (in relation to transcription factors)
microRNAdb:

two lists to catch some others: http://mirnablog.com/microrna-target-prediction-tools/ and  http://www.ncrna.org/KnowledgeBase/link-database/mirna_target_database

Elefant, N., Berger, A., Shein, H., Hofree, M., Margalit, H., & Altuvia, Y. (2010). RepTar: a database of predicted cellular targets of host and viral miRNAs Nucleic Acids Research DOI: 10.1093/nar/gkq1233

Tip of the Week: CircuitsDB for TF/miRNA/gene Regulation Networks


In this week’s tip I’d like to introduce you to CircuitsDB, which describes itself as:

“…a database where transcriptional and post-transcriptional (miRNA mediated) network information is fused together in order to propose and recognize non trivial regulatory combinations. “

I found out about the database from the BioMed Central article “CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse“, which I cite below. I had already been thinking about miRNAs because I am slated to update our miRBase tutorial in the near future and have been reading/catching up on the latest in the field. The CircuitsDB paper by Olivier Friard et al does a really nice job of quickly and clearly laying out the background of the project – how transcription factors have long been studied for their transcriptional regulation of protein-coding genes involved in any manor of pathways, including those of disease. It goes on to describe that the study of microRNAs, or miRNAs, is a newer field studying the post-translational regulatory effects of miRNAs on protein-coding genes and their functions. Current efforts are moving to integrate the two areas of research to create more complete, and admittedly more complex, regulatory views of protein-coding genes and the affects on disease and other pathways.

The developers of CircuitsDB also very clearly describe how they have mined, analyzed and connected data from several top databases – many of which we have tutorials on, such as OMIM, miRBase, Ensembl and others – in order to create feed-forward regulatory loops, or FFLs, of TFs, affected miRNAs and ultimately affected protein-encoding genes. The image at the right is from their original paper: “Genome-wide survey of microRNA–transcription factor feed-forward regulatory circuits in human” (cited below), which reported the development of the computational framework for the mixed miRNA/TF Feed-Forward regulatory circuits that are freely available through the  CircuitsDB web interface. This original paper is available for free, with registration to RSC Publishing, and provides a detailed description of their original development, as well as access to several supplemental files.

Essentially networks linking transcription factors and affected genes, miRNAs and affected genes, and transcription factors and miRNAs were painstakingly connected through an ab-initio oligo analysis. Support was then gained for the connections by analyzing enriched GO terms, disease connections, and previously-known connections found in other specialized resources. The CircuitsDB interface offers multiple tools. The main tool (FFL) is what I show in this tip & is the tool that searches for the networks diagrammed above. The MYC FFL is an impressive “curated database of miRNA mediated Feed Forward Loops involving MYC as Master Regulator”, and includes information on the direction of regulation, loop participants, evidence levels and more. The Transcriptional network tool allows a user to search with either a miRNA & find its regulating TF, or search with a TF & find regulated genes or miRNAs. The Post-transcriptional network tool is similar, but allows searches for a miRNA or gene to find regulated genes or regulating miRNA, respectively. So check out the tip & then check out CircuitsDB – enjoy!

References:
Friard, O., Re, A., Taverna, D., De Bortoli, M., & Corá, D. (2010). CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse BMC Bioinformatics, 11 (1) DOI: 10.1186/1471-2105-11-435

Re, A., Corá, D., Taverna, D., & Caselle, M. (2009). Genome-wide survey of microRNA–transcription factor feed-forward regulatory circuits in human Molecular BioSystems, 5 (8) DOI: 10.1039/B900177H