Wednesday, 25th April, 2012, 11:00-12:00
The identification of DNA binding sites has been a challenge since the early days of computational biology, and its importance has been increasing with the development of new experimental techniques and the ensuing flood of large-scale genomics and epigenomics data yielding approximate regions of binding. Many binding sites have a pronounced positional preference in their target regions, which makes them hard to find as this preference is typically unknown, and many of them are weak and cannot be found from target regions alone but only by comparison with carefully selected control sets. Several de-novo motif discovery programs have been developed that can either learn positional preferences from target regions or differentially abundant motifs in target versus control regions, but the combination of both ideas has been neglected. Here, we introduce Dispom, a de-novo motif discovery program for learning differentially abundant motifs and their positional preferences simultaneously. Dispom outperforms existing programs based on benchmark data and succeeded in detecting a novel auxin-responsive element (ARE) substantially more auxin-specific than the canonical ARE. Since its publication, we have endowed Dispom with more complex motif models and extended it to handle weighted input data such as ChIP-seq or BS-seq data. We have been applying Dispom to in-house and publicly available data of different transcription factors and insulators in yeasts, plants, and mammals as well as to protein-binding microarrays, where it turned out to be one of the top-scoring approaches in the corresponding DREAM challenge.
Speaker: Dr. Ivo Grosse, Institute of Computer Science, Martin Luther University, Halle, Germany
Room Xipre (seminar 173.06-183.01)
Thursday, 7th April, 2011, Thu, Apr 7, 2011 11:00 AM - Thu, Apr 7, 2011 12:00 PM
Speaker: Sonja Hänzelmann - Functional Genomics. Biomedical Informatics, UPF
Room 473.10 PRBB
Monday, 7th June, 2010, 12:00
Neo-Darwinian evolutionary theory is based on exquisite selection of phenotypes caused by small genetic variations, which is the basis of quantitative trait contribution to phenotype and disease. Epigenetics is the study of nonsequence-based changes, such as DNA methylation, heritable during cell division. Previous attempts to incorporate epigenetics into evolutionary thinking have focused on Lamarckian inheritance, that is, environmentally directed epigenetic changes. Here, we propose a new non-Lamarckian theory for a role of epigenetics in evolution. We suggest that genetic variants that do not change the mean phenotype could change the variability of phenotype; and this could be mediated epigenetically. This inherited stochastic variation model would provide a mechanism to explain an epigenetic role of developmental biology in selectable phenotypic variation, as well as the largely unexplained heritable genetic variation underlying common complex disease. We provide two experimental results as proof of principle. The first result is direct evidence for stochastic epigenetic variation, identifying highly variably DNA-methylated regions in mouse and human liver and mouse brain, associated with development and morphogenesis. The second is a heritable genetic mechanism for variable methylation, namely the loss or gain of CpG dinucleotides over evolutionary time. Finally, we model genetically inherited stochastic variation in evolution, showing that it provides a powerful mechanism for evolutionary adaptation in changing environments that can be mediated epigenetically. These data suggest that genetically inherited propensity to phenotypic variability, even with no change in the mean phenotype, substantially increases fitness while increasing the disease susceptibility of a population with a changing environment.
Speaker: Rafael Irizarry, Johns Hopkins University, Baltimore (USA)
Room 473.10 (Aula)
Thursday, 12th March, 2009, 11:00
Speaker: Ionas Erb - Comparative Genomics, CRG
Room oom 473.10-PRBB
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