Computational Regulatory Genomics

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Regulation on the RNA level

Post-transcriptional regulatory mechansisms are now accepted as crucial and frequent components of regulatory pathways.

Non-coding gene identification.

Current efforts in this area focus on the identification of regulatory RNA genes from deep sequencing data, most notably in plant. Our new platform to identify precursor and mature plant miRNAs from deep sequencing data is called PIPMiR.

Identification of RNA-binding protein targets.

We are developing new methods to map target sites of RNA-binding proteins by cross-linking and immunoprecitipation, in particular the PAR-CLIP protocol. The PARalyzer method can be used to identify precise sites from deep sequencing data, and can be followed by motif finding tools to identify enriched sequence motifs or miRNA seeds. We are collaborating with several experimental groups (cf Mukherjee N et al, Mol Cell 2011).

miRNA target prediction without conservation.

It is an open question how conserved miRNA target sites are. Many target prediction algorithms rely heavily on conservation, but in some settings, e.g. for predicting viral miRNA targets, it does not make much sense to assume conservation. We are thus developing approaches to predict target sites without relying on the conservation in several genomes.

For instance, we have used sequence with expression data to predict targets for the miRNAs of the human Kaposi-sarcoma associated herpes virus (KSHV) (Gottwein et al., Nature 2007). More recently, we have evaluated how well energy-based predictors are able to predict im/perfect seed match targets without conservation, implemented as TargetThermo (Lekprasert et al, 2011). Finally, we have developed a model, microMUMMIE, that integrates direct Argonaute binding data from cross-linking and immunoprecipitation with sequence features to identify miRNA target sites. 

miRNAs and alternative 3' UTR isoforms.

We have carried out a detailed analysis of microRNA target sites and their location in alternative 3'UTRs. The supplementary data contains the detailed list of predictions from that analysis (Majoros and Ohler, Spatial preferences of microRNA targets in 3' untranslated regions, BMC Genomics 2007.) More recently, we have developed an approach to identify condition-specific alternative 3' UTRs from high-throughput sequencing data, the PA-seq protocol (Hafez et a, Bioinformatics 2013).


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