1 2 3 45
 

Conference program

The program of the CIBB 2017 International Conference is not available yet and will be provided soon.

Invited talks

Here you can find the list of the invited speakers of the CIBB 2017 International Conference. It also provides an abstract of their respective keynotes and a short bio.

Keynote 1: Identification of distinct neuroblastoma identity types through the analysis of transcriptomics and epigenetics data

Speaker: Valentina Boeva

Valentina Boeva is a group leader at the Institut Cochin/Inserm/CNRS, Paris. The research objective of her team is to understand the link between genetic and epigenetic changes in cancer and decipher the role of epigenetic modifications. Valentina and her group designed and implemented bioinformatics tools in two main areas: (i) Analysis of copy number alterations and structural variants in cancer genomes: FREEC/Control-FREEC, SVDetect, SV-Bay, ONCOCNV; and (ii) Analysis of ChIP-seq data: MICSA, HMCan, HMCan-diff and Nebula (link). The team also develops computational techniques for integration of high-throughput data applied to cancer research.


Abstract. Neuroblastoma is a tumor of the peripheral sympathetic nervous system, derived from multipotent neural crest cells (NCC). Neuroblastoma genomes are characterized by relatively few recurrent driver mutations. The most frequently mutated neuroblastoma-related gene, ALK, is found to be altered in less than 15% of cases at diagnosis. This scarcity of identified driver mutations in neuroblastoma leaves a possibility of the “driving” role of non-genetic, i.e. epigenetic changes in oncogenic processes of this pediatric cancer. Indeed, latest studies have demonstrated that tumorigenesis of many cancers is associated with considerable epigenetic modifications: changes in the chromatin states and DNA methylation. Changes in chromatin states in cancer include, in particular, formation of de novo enhancers and super-enhancers (i.e. long active chromatin regions encompassing tens of Kb and comprising a dozen of enhancer elements) and enhancer hijacking.

We analyzed the enhancer and super-enhancer landscape of neuroblastoma, integrated it with gene expression data and detected transcription factors that constitute the core neuroblastoma regulatory circuitries and drive expression of genes determining cell identity in neuroblastoma and affecting cell phenotype. For this study, we used 20 neuroblastoma cell lines and two patient-derived mouse xenografts. We demonstrated that integrative analysis of ChIP-seq profiles for the acetylation of lysine 27 of the histone H3 and gene expression data can point to the key transcriptional regulators of neuroblastoma. We also defined two neuroblastoma identity subtypes. To analyze ChIP-seq data we used a method we specifically develop to process ChIP-seq profiles generated from cancer cells: HMCan [Ashoor et al, 2013]. HMCan takes into account copy number alterations ubiquitously present in cancer cells; it also corrects for the library size and GC-content bias. To predict super-enhancer regions based on the H3K27ac profiles in cancer cell lines and xenograft samples we used our unpublished tool – LILY (boevalab.com/LILY/). LILY takes into account copy number alterations, which distort the H3K27ac signal in cancer cells, and provides accurate annotations of super-enhancers in regions of copy number loss and gain. To detect possible drivers of the super-enhancer landscape in neuroblastoma, motif discovery with the i-cisTarget method was applied to regions corresponding to valleys of H3K27ac peaks located in super-enhancers.

Keynote 2: Crosstalk between viruses and high-throughput technologies

Speaker: Manja Marz

Manja Marz got a Full Professorship for "High Throughput Sequencing Analysis" at the Friedrich Schiller University, Jena, and since 2015 she is a group leader at Leibniz Institute for Age Research – Fritz Lipmann Institute. She is also a founding and board member of FIFI (Fördervereinverein des Instituts für Informatik), and a board member of ZAJ (Aging Research Center Jena).
She is also a founding member of MSCJ (Michael Stifel Zentrum Jena for Data-Driven and Simulation Science) and a founding member of ZAJ (Aging Research Center Jena). From 2010 to 2012, she was also a group Leader of "RNA Bioinformatics" at Philipps-University, Marburg.


Abstract. In the last years we have witnessed both the emergence of new viral diseases (e.g. MERS, SARS) and the re-emergence of known diseases in new geographical areas (e.g. Zika, Dengue and Chikungunya). Virologists have traditionally concentrated on studying viruses that cause disease in humans, animals or plants. However, there has been estimated around 1031 viruses in the biosphere and only a minuscule fraction has been identified, yet. On the other hand, the power of new genome sequencing technologies, associated with new tools to handle “big data”, provide unprecedented opportunities to address fundamental questions in virology. We would like to emphasize that many of the common questions raised in virology require specific bioinformatics support and for the need to bring together the expertise of bioinformaticians and virologists.

For this mission, the European Virus Bioinformatics Center (EVBC) was founded on 8th March 2017 in Jena, Germany. The EVBC has about 100 founding members from over 50 research institutions distributed across 13 European countries. The EVBC is intended to bring together virologists and bioinformaticians across Europe and provide a platform for the implementation of interdisciplinary collaborative projects at local and international scales. I will present first attempts to tackle with virus-specific programs open questions in (co- )phylogeny, high-throughput sequencing data analysis, virus detection, virus-host interaction, and host barriers .

Keynote 3: One-dimensional and three-dimensional protein spaces and protein evolution

Speaker: Alessandra Carbone

Alessandra Carbone is Distinguished Professor of Computer Science at UPMC and she has led the Analytical Genomics team since 2003 and is the director of the Department of Computational and Quantitative Biology since 2009. Her group works on computational problems concerning the functioning and evolution of biological systems. Mathematical methods coming from statistics and combinatorics, as well as algorithmic tools are employed to study fundamental principles of the cellular functioning starting from genomic data. The projects are all aimed at understanding the basic principles of evolution and co-evolution of molecular structures in the cell. They concern sequence evolution of entire genomes as well as protein evolution. Alessandra Carbone received the Prix Joliot-Curie in 2010 from the Ministère de la Recherche et de l’Enseignement Supérieur and from the EADS Foundation, and she was distinguished in 2012 with the Grammaticakis-Neuman Prize of the Académie des Sciences for "Integrative Biology". Since 2013 she is a senior member of the Institut Universitaire de France.


Abstract. Biology entered a new era, with computational biology producing biological data that are impossible nowadays to obtain with wet experiments. Tackling biological questions with advanced engineering, new computer algorithms and novel computational approaches is a challenge that will lead to revolutionize biology and medicine through deeper, ubiquitous use of DNA information.

A fundamental question is the extraction of evolutionary information from DNA sequences. We consider protein sequences here and we shall describe how a precise mapping between the one-dimensional representation of a protein (its sequence) and its three-dimensional representation (its structure) revealed important biological information on protein-protein binding sites and on mechanical and allosteric properties of proteins. Fine combinatorial readings of the conservation and co-evolution signals between residues in sequences can be used to identify protein- protein interaction (PPI) networks and describe them at the molecular level. In particular, these analyses apply to vertebrate and viral protein families.

PPI are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments. Suitable mathematical approaches appear necessary to properly address these questions and, in the talk, we shall highlight the new computational challenges.