Friday, 10th March, 2017, 11:00-12:00
Speaker: Juan Fernández Recio, Research Director, BSC.
Room Ramón y Cajal Room, PRBB Building
Thursday, 9th March, 2017, 12:00
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
Room Aula room 473.10 (PRBB, 4th floor)
Thursday, 2nd March, 2017, 12:00
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, 23th February, 2017, 12:00
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
Room Aula room 473.10 (4th floor)
Thursday, 2nd February, 2017, 12:00
In de novo gene emergence, a segment of non-coding DNA undergoes a series of changes which enables transcription of the segment, potentially leading to a new protein with a novel function. What makes de novo genes different from other genes? Due to their unique origins, young de novo genes have no homology with other genes and may not initially be under the same selective constraints. While dozens of de novo genes have been observed in many species, the mechanisms driving their appearance are not yet well understood. To study this phenomena, we have performed deep RNA-seq and ribosome profiling (RP) on 11 species of yeast from the phylum of Ascomycota in both rich media and oxidative stress conditions. These data have been used to classify the conservation of genes at different depths in the yeast phylogeny. Hundreds of genes in each species were novel (non-annotated), and many were identified as putative de novo genes; these can then be tested for signals of translation using our RP data. We show that putative de novo genes have different properties when compared to phylogenetically conserved genes. Understanding the mechanisms behind de novo gene emergence in a 'simple' eukaryote like S. cerevisiae may help to explain some of the unique adaptations seen in more complex organisms.
Speaker: Will Blevins (Evolutionary Genomics group of GRIB)
Room Aula room 473.10 (4th floor PRBB)
Friday, 20th January, 2017, 13.00 - 14.00
Speaker: Giorgio Colombo, Instituto di Chimica del Riconoscimento Molecolare, CNR, Italia,
Room Xipre Room (Seminar 173.06-183.01), PRBB Building
Thursday, 19th January, 2017, 12:00
In this lecture I will review the basics behind what makes proteins the most basic elements on which selection acts, how they mediate evolvability of organisms and why it seems so unlikely that a protein emergence de novo, i.e. by creation of a new ORF from previously untranscribed DNA. Since such emergence has, however, been observed we -- and many other groups around the world -- are desperately trying to resolve this mysterious puzzle which puts two fundamental schools of thought -- biophysics and genetics -- at odds.
Speaker: Erich Bornberg-Bauer Molecular Evolution and Bioinformatics. Institute for Evolution and Biodiversity. Universität Münster. Germany.
Room Aula room 473.10 (4th floor)
Thursday, 15th December, 2016, 12:00
Population heterogeneity within tumors is essential to the development of drug resistance. However, precise quantification of cellularity levels of subpopulations, and in particular how they evolve in response to treatment, has been challenging. Here we describe the high precision characterization of subclonal evolution within triple-negative breast cancer patient-derived xenografts (PDXs) generated from three patients in response to multiple chemotherapies, covering >100 total samples and allowing for extensive intratumoral comparisons. Computational mutation and copy number analysis from post-treatment sequencing indicated sample-specific differences in tumor populations both in response to treatment and due to genetic drift. I will describe the evolutionary behaviors we have observed, which include selective sweeps, spatial diffusion, and symbiosis.
Speaker: Jeffrey Chuang, Ph.D, The Jackson Laboratory for Genomic Medicine; University of Connecticut Health Center Dept. of Genetics and Genome Sciences; Host: Eduardo Eyras
Room Aula room 473.10 (4th floor)
Thursday, 15th September, 2016, 12.30 - 13.30
Drug design lags far behind other engineering disciplines in lacking predictive, quantitative models that allow small-molecule therapeutics to be designed, rather than fortuitously discovered. While many challenges exist to building these models, our laboratory uses cycles of computational predictions coupled to experimental measurements to rapidly generate data that can be used to improve rigorous, quantitative approaches to small molecule design based on alchemical free energy calculations. In this talk, we describe how this process can be done cheaply in an automated manner by inverting the drug discovery problem, and describe our first few steps toward this goal in the design of selective kinase inhibitors.
Speaker: John Chodera, Assistant Faculty Member, Computational Biology Memorial Sloan-Kettering Cancer Center, NYC, USA
Room Xipre Room (173.06-183.01), PRBB Building
Sunday, 4th September, 2016, 13:30 - 17:00 (half day tutorial)
Recent technological breakthroughs have produced an unprecedented increase in the amount of data on the genetic determinants of diseases. To unveil the molecular mechanisms that underlie diseases and to support drug discovery projects, it is necessary to place these data in the context of the current biomedical knowledge. Despite the large volume of information available, its analysis and interpretation are hindered because it is annotated using different criteria and vocabularies and fragmented across different resources. Furthermore, a large fraction of data on diseases is only available as free text in biomedical publications. To overcome these difficulties we have developed DisGeNET (Piñero et al, 2015), a discovery platform that contains information on human diseases and their genes. In this tutorial we will provide an overview of the main features of DisGeNET, and then introduce the suite of tools that the platform offers to support translational research. The tutorial includes a hands-on session organized around case studies that will illustrate how to use these tools. Materials will be made available via the DisGeNET website. Target audience: the tutorial is aimed at a variety of audiences: bioinformaticians, systems biology users, biologists, and healthcare practitioners.
Speaker: Laura I. Furlong and Janet Piñero - IBI group of GRIB (IMIM-UPF)
Room 15th European Conference on Computational Biology - The Hague, Netherlands - World Forum Convention Center