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Biomedical subjects

Stijn van Dongen

Publications and source records attributed to Stijn van Dongen.

3 recordsLinked to original sources

RNA editing of human microRNAs.

BACKGROUND: MicroRNAs (miRNAs) are short RNAs of around 22 nucleotides that regulate gene expression. The primary transcripts of miRNAs contain double-stranded RNA and are therefore potential substrates for adenosine to inosine (A-to-I) RNA editing. RESULTS: We have conducted a survey of RNA editing of miRNAs from ten human tissues by sequence comparison of PCR products derived from matched genomic DNA and total cDNA from the same individual. Six out of 99 (6%) miRNA transcripts from which data were obtained were subject to A-to-I editing in at least one tissue. Four out of seven edited adenosines were in the mature miRNA and were predicted to change the target sites in 3' untranslated regions. For a further six miRNAs, we identified A-to-I editing of transcripts derived from the opposite strand of the genome to the annotated miRNA. These miRNAs may have been annotated to the wrong genomic strand. CONCLUSION: Our results indicate that RNA editing increases the diversity of miRNAs and their targets, and hence may modulate miRNA function.

Adenosine↗

miRBase: microRNA sequences, targets and gene nomenclature.

The miRBase database aims to provide integrated interfaces to comprehensive microRNA sequence data, annotation and predicted gene targets. miRBase takes over functionality from the microRNA Registry and fulfils three main roles: the miRBase Registry acts as an independent arbiter of microRNA gene nomenclature, assigning names prior to publication of novel miRNA sequences. miRBase Sequences is the primary online repository for miRNA sequence data and annotation. miRBase Targets is a comprehensive new database of predicted miRNA target genes. miRBase is available at http://microrna.sanger.ac.uk/.

Animals↗

GeneMCL in microarray analysis.

Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5,730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms.

Algorithms↗