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Seminars, events & talks

Thursday, 2nd March, 2017, 12:00

Functional Genomics

Peeking at incomplete penetrance with linkage analysis

Large-scale genetic profiling and clinical sequencing are revealing an increasing number of carriers of disease-causing mutations who do not develop the disease phenotype. This characteristic is clinically reported as a genetic disorder of reduced or incomplete penetrance. Several mechanisms have been proposed to explain incomplete penetrance, such as the molecular context of mutations, patient characteristics, such as age or sex, as well as specific environmental conditions that delay or trigger the disease onset. The phenomenon of incomplete penetrance constitutes a major challenge in the field of genetic diagnosis and counseling because phenotypes no longer unambiguously exhibit underlying genotypes. Nevertheless, its existence also provides new opportunities to learn how genotypes shape phenotypes. In this talk I will discuss our efforts using linkage analysis, to find a genetic modifier that explains the incomplete penetrance of a specific genetic disorder.

Speaker: Pau Puigdevall, Functional Genomics, GRIB, UPF

Room Aula room 473.10 (4th floor)

Thursday, 12th May, 2016, 12:00

Functional Genomics

A population-level analysis of mutations affecting 5'ss splicing

Wild-type processing of RNA transcripts by the splicing machinery is a fundamental step in the gene expression pathway. Mutations affecting this step can produce aberrant splicing with deleterious effects that ultimately lead to disease. Mutation databases curating the scientific literature store an increasing number of single nucleotide variants (SNVs) inducing aberrant splicing involved in disease. Yet, this increase is far below the current growth rate of genetically profiled diseased individuals. This results in many SNVs of unknown effect. In this context, understanding the deleterious effects of SNVs on 5'ss splicing becomes extremely important to attempt a sensible prioritization of such SNVs. Population-level allele frequencies constitute a valuable resource to gather understanding of mutation processes. We have used the last release of the 1000 Genomes and the ExAC catalogs of human variation to characterize mutations that affect 5'ss splicing and attempt a sensible prioritization of such SNVs.

Speaker: Pau Puigdaval - Functional Genomics Group, GRIB (UPF-IMIM)

Room Aula room 473.10 (4th floor)

Thursday, 3rd December, 2015, 12:00

Functional Genomics

"Umbilical cord gene expression reveals the molecular architecture of the fetal inflammatory response in extremely preterm newborns"

The fetal inflammatory response (FIR) in placental membranes to an intrauterine infection often precedes premature birth raising neonatal mortality and morbidity. However, the precise molecular events behind FIR still remain largely unknown, and little has been investigated at gene expression level. We collected publicly available microarray expression data profiling umbilical cord (UC) tissue derived from the cohort of Extremely Low Gestational Age Newborns (ELGANs) and interrogate them for differentially expressed (DE) genes between FIR and non-FIR affected ELGANs. We found a broad and complex FIR UC gene expression signature, changing up to 19% (3,896/20,155) of all human genes at 1% false discovery rate (FDR). Significant changes of a minimum 50% magnitude (1,097/3,896) affect the upregulation of many inflammatory pathways and molecules, such as cytokines, toll-like receptors, and calgranulins. Remarkably, they also include the downregulation of neurodevelopmental pathways and genes, such as fragile-X mental retardation 1 (FMR1), contactin 1 (CNTN1) and adenomatous polyposis coli (APC). The FIR expression signature in UC tissue contains molecular clues about signaling pathways that trigger FIR, and it is consistent with an acute inflammatory response by fetal innate and adaptive immune systems, which participate in the pathogenesis of neonatal brain damage.

Speaker: Robert Castelo - Functional Genomics Group, GRIB (IMIM-UPF)

Room Aula room 473.10 (4th floor)

Thursday, 21th May, 2015, 11:00h

Functional Genomics

Mapping eQTL networks with mixed graphical Markov models and the genetic control of gene expression in yeast network hub genes

Speaker: Robert Castelo - Functional Genomics Group - GRIB

Room 473.10

Wednesday, 8th October, 2014, 12:00

Functional Genomics

Clues to molecular mechanisms from the concerted action of genes

Speaker: Robert Castelo - Head of Functional Genomics group (GRIB)

Room Ramon y Cajal

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.

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

Wednesday, 25th April, 2012, 11:00-12:00

Functional Genomics

De-Novo Discovery of Differentially Abundant DNA Binding Sites Including Their Positional Preference

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

Functional Genomics

GSVA: Gene Set Variation Analysis

Speaker: Sonja Hänzelmann - Functional Genomics. Biomedical Informatics, UPF

Room 473.10 PRBB

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