Events

Mini Workshop: RNA Transcriptomics

2016-09-19: University of Copenhagen, 10.00-11.45,  at Festauditoriet, A1-01.01, Frederiksberg Campus, Bülowsvej 17, 1870 Frederiksberg C, Denmark. Speakers: Kay Niesselt, University of Tübingen and Daniel Gautheret, Université Paris Sud.

Joint by RTH and Danish RNA Society

Programme:

10.00-10-45:
"Comparative transcription start site prediction from dRNA-seq data" by Kay Niesselt, University of Tübingen

Abstract:
RNA deep-sequencing technologies (RNA-seq) reveal detailed insights into the transcriptome structures of eukaryotic and prokaryotic organisms. This includes the genome-wide detection of transcription start sites (TSS).
Manual annotation of TSS from RNA-seq data is time-intensive, can be biased by the annotating person, and becomes infeasible when comparing different species.  We have developed TSSpredator, a tool for the automated de novo detection and classification of TSS from dRNA-seq data.TSSpredator performs a genome-wide comparative prediction of TSS in bacteria, for example between samples from different growth conditions or mutants. For the comparison of different organisms we integrated the
SuperGenome approach which generates a common coordinate system for the compared genomes, allowing for the comparative annotation of TSS across several strains or species. Our latest version now also includes the possibility to predict TSS in bacteria with several chromosomes, or genomes with an unfinished assembly. Furthermore, since TSSpredator is based on a parametric model we have added the possibility to compute a probability value using the TSSAR model.

11.00-11.45:
"RNA isoform detection, why you're doing it wrong" by Daniel Gautheret, Université Paris Sud.

Abstract:
Genes producing multiple alternative transcripts is a universal phenomenon, which is best studied using the RNA-seq technology. There are over 50 computer programs for identifying and quantifying transcript isoforms from RNA-seq data, however they produce widely divergent results. I will present the strengths and limitations of leading algorithms and show why they fail anyway to capture the full diversity of transcripts. I'll present cases where transcript differences are due to events other than alternative promoter/splicing/polyadenylation sought by most programs and show how alternative approaches might capture them.