Mini Workshop: Bioinformatics
2016-04-28: Mini workshop: Bioinformatics. University of Copenhagen. From 13.15- 15.00, in Auditorium A1-05.01, Dyrlægevej 100, Frederiksberg Campus. Speakers: Larry Ruzzo and Rolf Backofen
13.15-14.00: "Eliminating the Cost of Sex: Asexual Clonal Lineages Amidst Sexual Eukaryotic Microbes"
Computer Sciences & Engineering, University of Washington, USA
Abstract: Sexual reproduction roots the eukaryotic tree of life, and yet asexual reproduction is scattered across it. Asexual lineages are presumed to be evolutionary dead ends due to the loss of long-term adaptive benefits associated with genetic recombination. Are they common? How do they arise? Why? Do they affect global ecology? We present quantitative evidence that the unicellular eukaryotic marine diatom Thalassiosira pseudonana maintains both sexual and obligate asexual lineages in the wild. Whole genome comparisons identified two lineages that display characteristics expected of sexually reproducing strains in Hardy-Weinberg equilibrium. In contrast, a third lineage displays genomic signatures for the functional loss of sexual reproduction followed, most surprisingly, by rapid global colonization. This life history strategy leverages near-term benefits gained from asexual invasions, while simultaneously hedging on the long-term benefits of enhanced genetic diversity.
Joint work with T Chiang, J Koester, C Berthiaume, N Hiranuma, M Parker, V Iverson, R Morales, A Sarwate & E Armbrust
14.00-14.15 Coffee Break
14.15-15.00: "How to determine binding motifs for RNA-binding proteins"
Department of Computer Science, University of Freiburg, Germany
Abstract: It is becoming increasingly clear that a RNA-binding proteins are key elements in regulating the cell's transcriptome. Thus, unraveling the interaction network of the RNA-binding proteins by determining their binding sites is becoming an increasingly important topic. There are several high-throughput methods available to detect binding sites such as CLIP-seq. Since not all possible binding sites are covered due to differential expression in tissues and developmental states, the main problem is to come up with good motif descriptions to find missing binding sites and to evaluate the binding strength. Our new approach GraphProt uses an advanced machine learning approach based on our graph kernel, and is able to use both structural profiles as well as detailed 2D-structures, and predicts missing binding sites with an high accuracy.