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Seminars, events & talks

Wednesday, 21th June, 2017, 12:00 - 13:00

DisGeNET discovery platform 5.0: Illuminating the study of human diseases

In the last few decades, our knowledge about the genetic underpinnings of human diseases has grown at an unprecedented pace. Data resulting from GWAS studies, experiments in animal models, and from exome sequencing pipelines are freely available, but scattered across several repositories. To enable translation of this wealth of knowledge into better disease biomarkers and drug therapies, this information should be made readily available to translational researchers and clinicians. DisGeNET (http://www.disgenet.org/) is a platform that fulfills this need. It contains one of the largest available collections of genes and variants associated to human diseases. This is achieved by the integration of data from several specialized resources on gene and variant-disease associations with information extracted from automatically mining Medline abstracts. Here, we present DisGeNET 5.0, containing more than 500,000 gene-disease associations, and over 135,000 variant-disease associations. The associations are annotated using community-based standards, including a variety of disease vocabularies and are enriched and expanded by linking them to other key resources covering the chemical and omics spaces. DisGeNET 5.0 also includes several new improvements, besides more disease associations: a) a new curated source: the PsyGeNET database, b) an improvement of the text-mining pipeline to extract gene-disease and variant-disease associations from publications, c) non-coding variants associated to disease, d) disease-phenotype associations, and e) gene-phenotype associations from the Human Phenotype Ontology. With its fifth release, DisGeNET is an established and mature resource, which is increasingly used in the investigation of the genetic basis of human diseases and to support drug discovery projects.

Speaker: Janet Piñero, Integrative Biomedical Informatics, GRIB (IMIM/UPF)

Room Aula 473.10 (PRBB, 4th floor)

Tuesday, 6th June, 2017

ELIXIR Innovation and SME Forum

The ELIXIR Innovation and SME Forum: Genomics, bioinformatics and health - Public-private partnerships in open data" is organized by ELIXIR Spain and the ELIXIR Hub in collaboration with Innovative Medicines Initiative (IMI), BIOCAT, Bioinformatics Barcelona (BIB), Barcelona Biomedical Research Park (PRBB) and the GRIB (IMIM-UPF). It will be held on 6-7 June 2017 at PRBB Auditorium and  is particularly relevant for large and small companies active in the genomics and health domains.​. The aim of this Innovation and SME forum is to showcase to companies the free data resources and services that are available through ELIXIR Spain and ELIXIR Europe more generally. It will feature talks from ELIXIR partners on some of the key databases and resources and present examples of innovative companies that are already using public data in their businesses. The event is free and open to all companies large and small, in the Barcelona region and further afield. 

Room PRBB Auditorium

Tuesday, 6th June, 2017, 14:00 - 15.30

Integrative analysis of cancer transcriptomes

Speaker: Eduardo Eyras, Head of Computational RNA Biology, GRIB (IMIM/UPF)

Room Charles Darwin, PRBB Innner square

Wednesday, 31th May, 2017, 12:00 - 13:00

Measuring ribosome profiling at isoform level: a step towards unveiling alternative splicing funcional impact

The alternative processing of transcribed genomic loci through alternative transcript initiation, splicing and polyadenylation, is an intermediate step in mRNA maturation, between transcription and translation. These mechanisms produce different mature mRNA transcripts and determine the transcript repertoire of cells. There is evidence showing that the differential production of transcript isoforms, especially through the mechanism of alternative splicing, is crucial in multiple biological processes such as cell differentiation, acquisition of tissue-specific functions, DNA repair, as well as in multiple pathologies, including cancer. This has been exhaustively shown at RNA level but it remains elusive at protein level. Sequencing of ribosome-protected RNA fragments, or ribosome profiling, provides detailed information on the transcripts being translated in the cell. In this work, we have analysed the quantification of individual transcript coding sequences (CDSs) from ribosome profiling using both RNA-seq and Ribo-seq data from human and murine gliomas. Moreover, we investigated to what extend differential transcript usage and differential splicing impacts protein production. We describe for the first time evidence for differential translation of coding regions associated to differential transcript usage and alternative splicing, and in particular for the differential translation of microexons, which have a crucial role in neural tissue.

Speaker: Marina Reixachs, Computational RNA Biology, GRIB (IMIM/UPF)

Room Aula 473.10 (PRBB, 4th floor)

Thursday, 20th April, 2017, 13:00 - 14.00

Big data in chemical safety assessment, challenges and opportunities

BIGCHEM BCN 2017: school about Computational Chemistry and Pharmacology.  Traditional methods for chemical safety assessment based in animal testing are being replaced by alternatives approaches, more acceptable from an ethical point of view. This situation gives the opportunity to exploit the vast amount of data already obtained from in vivo studies and other sources, even if the application of this concept in practice is being more difficult than expected.

Speaker: Manuel Pastor, Head of PharmacoInformatics , GRIB (UPF)

Room Charles Darwin, PRBB Innner square

Wednesday, 19th April, 2017, 13:00 - 14.00

Large-Scale Predictive Drug Safety

BIGCHEM BCN 2017: school about Computational Chemistry and Pharmacology. The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety that pave the way towards gaining a better understanding of the mechanisms leading to adverse outcomes.

Speaker: Jordi Mestres, Head of the Systems Pharmacology, GRIB (IMIM/UPF)

Room Charles Darwin, PRBB Innner square

Thursday, 9th March, 2017, 11:00-12:00

Structural prediction of protein-protein interactions for the upcoming challenges in biomedicine

Speaker: Juan Fernández Recio, Research Director, BSC

Room Ramón y Cajal, PRBB Innner square

Wednesday, 8th March, 2017, 12:00 - 13.00

Characterization of RNA processing alterations in small cell lung cancer

Small cell lung cancer (SCLC) accounts for 15% of all lung cancers. Previous studies have shown high frequency of mutations in TP53 and RB1, and amplification of MYC. However, no targeted therapies have been approved for use in treatment of SCLC, contrary to other lung cancer types like adenocarcinoma. Accordingly, chemotherapy remains the only treatment, which is initially effective but is inexorably followed by rapid relapse in the majority of the patients. Understanding the molecular mechanisms underneath this disease is thus necessary for improving treatment. We have analyzed RNA-seq from 73 RNA-seq SCLC patient samples from and characterized the transcriptomic changes between tumor and normal tissues. We have validated these changes on other 2 cohorts of 31 and 19 RNA-seq SCLC patient samples. In order to identify those changes specific of SCLC, and to account for the fact that SCLC tumors have different cell type of origin than other lung tumors, we performed comparisons against more than 1000 non-small cell lung samples from The Cancer Genome Atlas and against neuroendocrine lung carcinoid tumors. Additionally, using 71 WGS SCLC samples, we looked for somatic mutations disrupting intronic and exonic splicing regulatory motifs that could be responsible for these changes in the transcriptome. This is the largest analysis performed to date of RNA processing alterations and associated mutations in SCLC, which could lead to the uncovering of novel targets of therapy.

Speaker: Juan Luís Trincado, Computational RNA Biology, GRIB (IMIM/UPF)

Room Aula 473.10 (PRBB, 4th floor)

Wednesday, 1st March, 2017, 12:00 - 13.00

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 473.10 (PRBB, 4th floor)

Wednesday, 22th February, 2017, 12:00 - 13.00

Identifying temporal patterns in patient disease trajectories: a population-based study

The widespread use of electronic health record (EHR) datasets has facilitated the massive collection of patient health information, thereby enabling researchers to conduct large-scale studies of comorbidities. The term comorbidity can be defined as the co-occurrence of two or more diseases within the same individual. The factor of time has, typically, not been taken into account in most of the relevant works. However, by incorporating the time dimension into a comorbidity study more complex disease patterns and their temporal characteristics can be revealed. In this work, a large-scale temporal comorbidity study is performed on a local (Catalonian) health database. The disease-history vectors of individual patients are compared between each other in order to extract common disease trajectories (i.e. shared by at least 2 patients). By using statistical-significance tests on the common disease trajectories of length=2, significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, a novel unsupervised clustering algorithm, based on the Dynamic Time Warping (DTW) technique, is applied on all extracted common disease trajectories (length>=2), in order to group them according to the temporal patterns that they share. It will be shown that DTW can successfully cluster the disease-trajectory signals under investigation, which consist of various time scales and durations, although they do not exhibit any obvious temporal alignment. In this manner, important key clusters can be identified with trajectories that share the same time-dependent characteristics. A time-dependent comorbidity analysis is expected to facilitate the early diagnosis of a disease and prevent any adverse outcomes, by permitting the prediction of the disease progression along time.

Speaker: Alexia Giannoula, Integrative Biomedical Informatics, GRIB (IMIM/UPF)

Room Aula 473.10 (PRBB, 4th floor)

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