latest news & announcements

PhD Pieter-Jan Volders: Exploring lncRNAs in cancer: tools for discovery and characterization of cancer associated lncRNAs

Long non-coding RNAs (lncRNAs) form a new class of genes that outnumbers any other class of RNAs predicted in the human genome. Since most lncRNA annotation is relatively new, lncRNAs are underrepresented in the established genomic databases and on the commercially available platforms. To address this issue, I collected human lncRNA annotation from different sources and developed the public lncRNA database LNCipedia (www.lncipedia.org) in the first months of my PhD.

First BIG N2N paper published

On June 20th, the first paper bearing the affiliation "Bioinformatics Institute Ghent" was published in BMC Bioinformatics.

http://www.biomedcentral.com/1471-2105/16/197

Jan M. Ruijter, Steve Lefever, Jasper Anckaert, Jan Hellemans, Michael W. Pfaffl, Vladimir Benes, Stephen A. Bustin, Jo Vandesompele, Andreas Untergasser and on behalf of the RDML consortium (2015) RDML-Ninja and RDMLdb for standardized exchange of qPCR data. BMC Bioinformatics, 16:197

SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Background: With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively.

A Decoy-Free Approach to the Identification of Peptides

A growing number of proteogenomics and metaproteomics studies indicate potential limitations of the application of the decoy database paradigm used to separate correct peptide identifications from incorrect ones in traditional shotgun proteomics. We therefore propose a binary classifier called Nokoi that allows fast yet reliable decoy-free separation of correct from incorrect peptide-to-spectrum matches (PSMs).

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