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Seminars, events & talks

Wednesday, 7th June, 2017, 14:00-15.30

Computational RNA Biology

Integrative analysis of cancer transcriptomes

Speaker: Eduardo Eyras, Head of Computational RNA Biology lab, GRIB.

Room Charles Darwin Room, PRBB Building.

Thursday, 1st June, 2017, 12:00 - 13:00

Computational RNA Biology

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

Room Aula room 473.10 (4th floor)

Thursday, 9th March, 2017, 12:00

Computational RNA Biology

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

Room Aula room 473.10 (PRBB, 4th floor)

Thursday, 15th December, 2016, 12:00

Computational RNA Biology

"Intratumoral evolution of breast cancer in response to therapy"

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, 28th April, 2016, 12:00

Computational RNA Biology

Characterization of DNA sequence variants that affect pre-mRNA processing in multiple cancer types

In our lab we study alterations in splicing in multiple cancer types. These splicing alterations occur through somatic mutations in cis in introns and exons or through other mechanisms in trans. We recently published the largest analysis to date of the splicing alterations in cancer (Sebestyen et al. 2016) that includes an exhaustive analysis of the mutations, copy number variations and expression changes in RNA binding proteins and how these impact alternative splicing in multiple cancer. Our previous work lead us to some open ended question about mutations that affect splicing in cis. Therefore, currently we are developing a method for identifying and characterizing significantly mutated regions (SMRs) inside genes from whole genome sequencing (WGS) data and their impact on RNA processing in multiple tumors. I will be presenting some preliminary results about this project.

Speaker: Babita Singh - Computational Genomics group of GRIB (IMIM-UPF)

Room Aula room 473.10 (4th floor)

Thursday, 8th October, 2015, 12:00

Computational RNA Biology

Exploiting processing patterns to study small non-coding RNAs

Small non-coding RNAs play a vital role in several cellular processes. Their pattern of processing and its traces in short RNA-Seq reads data is a powerful and unbiased resource that can be used to study those important molecules. Here we present a collection of software tools based on the analysis of processing patterns for the discovery, annotation and characterization of small non-coding RNAs.

Speaker: AMADIS PAGÉS - Computational Genomics, GRIB

Room Marie Curie Room (Ground floor)

Thursday, 16th April, 2015, 11:00h

Computational RNA Biology

RNA processing alterations as drivers and prognostic markers of cancer


Alterations in RNA processing are emerging as important signatures to understand tumor formation and to develop new therapeutic strategies. However, it is not yet known the extent to which these alterations can be considered drivers or whether specific patterns of RNA processing can be predictive of prognosis. We describe our efforts to determine the functional impact and relevance in cancer of RNA processing alterations measured in 11 cancer types. We describe multiple alterations in RNA regulatory proteins and their target genes, and investigate RNA alterations that are predictive of tumor stage and survival. These novel signatures expand the catalogue of candidate actionable alterations in tumors and potentially complement current strategies in precision cancer medicine.

Speaker: EDUARDO EYRAS Computational Genomics Group - GRIB

Room Aula room (4th floor)

Thursday, 20th November, 2014, 11:00-12:00

Computational RNA Biology

Recent Developments in RNA structure prediction and RNA design

Speaker: Ivan Dotu; Biology Department, Boston College.

Room Aula room (470.03 – 4th floor)

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

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

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