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Seminars, events & talks

Thursday, 22th May, 2014, 11:00

Evolutionary Genomics

Exploring hibernation in mammals through transcriptomics: the case of the hibernating lemur

Hibernation is a complex physiological response some mammalian species employ to evade energetic demands. Hibernators conserve energy by essentially “shutting down” physiological processes; metabolic rate is severely depressed, body temperature plummets to ambient levels, and brain activity is greatly diminished (reviewed in Carey et al., 2003). In recent years the study of the molecular processes involved in mammalian hibernation has shifted from investigating a few carefully selected candidate genes to large-scale analysis of differential gene expression. The availability of high-throughput data provides an unprecedented opportunity to ask whether phylogenetically distant species show similar mechanisms of genetic control, and how these relate to particular genes and pathways involved in the hibernation phenotype.

In this talk, we are going to present our ongoing research in this field, comparing the genetic controls of hibernation in several mammalian species and presenting some results about the genetic regulation in the only primate known to naturally exhibit this behavior: the dwarf lemur (genus Cheirogaleus), endemic to Madagascar.

Carey, H. V, Andrews, M. T., & Martin, S. L. Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiological reviews 2003; Vol: 83, 4 pages, doi:10.1152/physrev.00008.2003

Speaker: José Luis Villanueva - Evolutionary Genomics Group

Tuesday, 13th May, 2014, 11.00-12.00

Functional Genomics

Regression models for categorical responses: an application to the interaction of different chronic comorbidities in HIV positive patients

Multimorbidity is defined as the co-occurrence of two or more chronic medical conditions in one person. Multimorbidity is turning into a major medical issue for both individuals and health care providers. It is well-known that multimorbidity correlates with age and, furthermore, that HIV-infected patients experience an increased prevalence of noninfectious comorbidities, compared with the general population. It has also been hypothesized that such increased prevalence is the result of premature aging of HIV-infected patients.

Guaraldi et al. (2011) investigated the effect of HIV-infection on the prevalence of five noninfectious chronic medical conditions from an Italian dataset obtained from a cross-sectional retrospective case-control study. More specifically, Guaraldi et al. (2011) analysed the effect of HIV-infection by means of logistic regressions on univariate outcomes. However, the analysis of single responses is not sufficient becasue multimorbidity is characterised by complex interactions of co-existing diseases and to gain relevant insight on the role of HIV-infection on multimorbitiy it is necessary to use a multivariate approach aimed to investigate the effect of HIV on the interaction of different chronic conditions.

In regression models for categorical responses a linear model is typically related to the response variables via a transformation of probabilities called a link function.  We present an approach that is based on the connection between two different links: the log-mean linear link and the Moebius link. In our framework, the interpretation of the effect of covariates on the interaction of responses is straightforward.

This is joint work with Monia Lupparelli, University of Bologna.

Speaker: Alberto Roverato, Professor of Statistics at the University of Bologna (Italy)

Room Xipre (seminar 173.06-183.01), PRBB.

Tuesday, 13th May, 2014, 10.00-11.00

Functional Genomics

Traceable regressions: general properties and special cases


Traceable regressions are those graphical Markov models that are best suited to capture generating processes in longitudinal studies, either without or with interventions. Sequences of responses may contain several component variables and these components may be continuous or discrete.

In this lecture, I discuss properties of  corresponding distributions that are needed to read off the graph all implied independences, as well as the additional properties that permit similar conclusions about dependences. Some data analyses are shown and some results are discussed for star graphs, a very special type of graph.

Speaker: Nanny Wermuth, Chalmers University of Technology, Gothenburg and Johannes Gutenberg-University, Mainz

Room Xipre (seminar 173.06-183.01), PRBB.

Thursday, 8th May, 2014, 11:00

Computational RNA Biology

Understanding alternative splicing by genome-wide, quantitative profiling

Both chromatin state and binding of splicing factors to regulatory sequence elements of pre-mRNA have been shown to affect alternative splicing outcomes.  Yet the precise interplay between these two determinants is not known.  The availability of a relatively large number of relevant, publicly available, high-throughput ChIP-seq, CLIP-seq, and RNA-seq datasets make it possible to study this in depth on a genome-wide scale.  Read profiling is a commonly used method to increase the signal strength of high-throughput data by combining reads from a set of similar loci rather than examining each locus individually, thus increasing the signal and statistical power.  However, profiles often convey only qualitative information,  In this talk I will present a method we have developed to calculate exact P-values for comparison of a profile with a proper control.  The method allows for single-nucleotide resolution in principle, and can be used on most types of high-throughput sequenceing data.  I will also show how we have applied the method thus far to study the relationship between chromatin and splicing factors.

Speaker: Isaac J. Kremsky Computational Genomics group, GRIB

Room Aula

Monday, 28th April, 2014

PharmacoInformatics

GPCR Spring Conference 2014, Selent, Jana i Martí, Maria. Comité organitzador.

PRBB (Barcelona) 28-30/4/2014. 

Friday, 14th March, 2014, 11.00-12.00

Functional Genomics

What can the cloud do for you? Solving health puzzles through cloud computing.

Recent years, the use of computer-aided drug discovery techniques has been increased. These techniques, that includes, for example, virtual screening, compound selection, activity prediction and modelling, allow us to optimize our research work, reducing time and money. But its usage requires software (usually commercial and expensive) and high HPC (high performance computing) systems, which implies to invest money in computational infrastructure.  Cloud solutions for drug discovery helps us to "optimize our optimization process". Cloud systems are elastic, requires less money investment and are always ready, paying only for the resources you are really using.

Speaker: Alfons Nonell-Canals, PhD, CEO Mind the Byte, S.L.

Room Xipre Room 173.06 - PRBB

Thursday, 27th February, 2014, 11:00

Evolutionary Genomics

Are long non-coding RNAs translated?: Ribosome profiling reveals the complexity of eukaryotic proteomes

We have compiled raw sequencing data from ribosome profiling experiments performed in different species (human, mouse, zebrafish, fruit fly, yeast) and used them to assemble transcripts, quantify transcript-ribosome associations, and investigate the coding potential and strength of purifying selection of the putatively translated open reading frames in some long non-coding RNAs. We detected extensive association of lincRNAs with ribosomes, not only observed in mammals but also in the other eukaryotic groups. Surprisingly, some of the lncRNAs show significant sequence similarity to proteins only annotated in Genbank, whereas others show not such similarity but still contain putative short open reading frames. The coding potential of ribosome-associated lncRNAs ORFs, measured using different codon frequency based sequence statistics, is intermediate between intronic ORFs and experimentally validated ORFs. These ORFs are subject to weaker selective constraints than most experimentally validated proteins as inferred from single nucleotide polymorphism densities. Our results suggest that many lncRNAs have coding properties and that this class of genes most likely includes protein-coding genes that are no longer functional as well as genes encoding new, poorly-conserved, peptides.

Speaker: Jorge Ruiz Orera - Evolutionary Genomics, GRIB (IMIM - UPF)

Room Aula-4th floor

Monday, 17th February, 2014, 11:00

Computational Biophysics

Rethinking Binding and Binding Kinetics

Speaker: Jose Duca, Head of Computer-Aided Drug Discovery at Novartis

Room Marie Curie Room

Thursday, 13th February, 2014, 11:00

Computational RNA Biology

Alternative splicing patterns in the Cancer Genome Atlas datasets

Speaker: Endre Sebestyén - Computational Genomics, GRIB (IMIM-UPF)

Room Sala 473.10

Wednesday, 22th January, 2014, 12:00

Integrative Biomedical Informatics

Integrative strategies for biomedical knowledge.

The progress in biomedical research is increasingly hampered by the difficulty of managing and jointly exploiting the huge amount of information that is accumulated and that it is often fragmented into silos. Consequently, the biomedical knowledge discovery can deeply benefit from scientific, technological and organisational advances in the ways that biomedical information is integrated and jointly analysed. Examples of efforts that we are doing in this direction are: 1) the IMI eTOX project, which tries to advance in the in silico prediction of the potential in vivo toxicity of drug candidates by means of information sharing among the pharmaceutical companies and the application of multi-level modelling strategies; 2) DisGeNET, which is a gene-disease database created by integration of gene-disease associations from several resources; 3) the EU-ADR project, which has developed innovative pharmacovigilance strategies by means of joint exploitation of millions of European healthcare records followed by bioinformatics substantiation of the drug-event signals detected; and 4) the recently started IMI EMIF project, which aims to develop a common information framework of patient-level data that will link up and facilitate access to diverse medical and research data sources, opening up new avenues for research.

Speaker: Ferran Sanz, professor of Biostatistics and Biomedical Informatics at Pompeu Fabra University (UPF) and Director of the IMIM-UPF Research Programme on Biomedical Informatics (GRIB).

Room PRBB Auditorium



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