CANCELLED : Computational Biology in the Crossroad of Big Data, Artificial Intelligence and High Performance Computing

EVENT : C3BI Seminars


Main speaker : Alfonso Valencia, from Barcelona Super Computing Center (BSC-CNS) Date : 26-09-2019 at 02:00 pm


Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

Genomic enzymology web tools for functional assignment: Generating and analyzing Sequence Similarity Networks (SSNs) and Genome Neighborhood Networks (GNNs) with the EFI suite

EVENT : C3BI Seminars


Main speaker : Rémy Zallot, from University of Illinois Date : 20-06-2019 at 02:00 pm Location : Duclaux room down groundfloor – DUCLAUX (01), Institut Pasteur, Paris


Protein databases contain an exponentially growing number of sequences as a result of the decrease of cost and difficulty of genome sequencing. The rate of data accumulation far exceeds the rate of functional studies, producing an increase in genomic ‘dark matter’, sequences for which no precise and validated function is defined. Strategies to leverage the protein and genome databases for discovery of the functions of novel enzymes belonging to the dark matter are needed. “Genomic enzymology” is the integration of relationships among sequence-function space in protein families and the genome context of their bacterial, archaeal, and fungal members to propose function. The Enzyme Function Initiative suite of webtools (https://efi.igb.illinois.edu) include the EFI-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks (SSNs) for protein families and the EFI-Genome Neighborhood Tool (EFI-GNT) producing Genome Neighborhood Networks (GNNs) and Genome Neighborhood Diagrams (GND) for analyzing and visualizing genome context of SSNs clusters. Together, these tools facilitate the “Genomic enzymology” application to the ‘dark matter’ problem. A detailed overview of the principle of SSNs, GNNs and GNDs generation will be presented. The identification of an unexpected reaction in the Queuosine biosynthesis pathway will illustrate the approach.


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New computational tools for the analysis of microbiome dynamics

EVENT : C3BI Seminars


Main speaker : Eran Halperin, from UCLA (Computer Science Department & Departments of Human Genetics, Biomathematics & Department of Anesthesiology) Date : 25-06-2019 at 11:00 am Location : Auditorium Jaques Monod – MONOD (66) ,Institut Pasteur, Paris


High-throughput microbiome analysis has become ubiquitous over the last few years. However, the interpretation of the data is often non-trivial and highly depends on the methodology used for the analysis. I will describe a few methods for the analysis of microbiome in contexts that are typical in such analyses. First, I will describe a new method for microbial source tracking, that is, finding the sources of a microbiome sample. I will demonstrate how using the method one can reach very different conclusions (that make more biological sense) than using previous methods. Specifically, I will show examples on the dynamics of gut microbiome in babies, and on the usage of source tracking as a tool for disease diagnosis. Second, I will discuss novel approaches for the analysis of time-series micorbiome data, and here too, I will show how this new approach results in new biological insights, particularly on the dependence of microbiome in the future on the current composition of the microbiome. The talk will be self contained, and I will not assume any expert knowledge in computer science or statistics.


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Interpreting the cancer genome through physical and functional models of the cancer cell

EVENT : C3BI Seminars


Main speaker : Trey Ideker, from UC San Diego – School of Medicine Date : 21-06-2019 at 02:00 pm Location : Jules Bordet room – METCHNIKOFF (67) ,Institut Pasteur, Paris


Dr. Ideker is a Professor of Medicine at UC San Diego. He is the Director of the National Resource for Network Biology, the San Diego Center for Systems Biology, and the Cancer Cell Map Initiative. He is a pioneer in using genome-scale measurements to construct network models of cellular processes and disease.

Recently we and other laboratories have launched the Cancer Cell Map Initiative (ccmi.org) and have been building momentum. The goal of the CCMI is to produce a complete map of the gene and protein wiring diagram of a cancer cell. We and others believe this map, currently missing, will be a critical component of any future system to decode a patient’s cancer genome. I will describe efforts along several lines: 1. Coalition building. We have made notable progress in building a coalition of institutions to generate the data, as well as to develop the computational methodology required to build and use the maps. 2. Development of technology for mapping gene-gene interactions rapidly using the CRISPR system. 3. Causal network maps connecting DNA mutations (somatic and germline, coding and noncoding) to the cancer events they induce downstream. 4. Development of software and database technology to visualize and store cancer cell maps. 5. A machine learning system for integrating the above data to create multi-scale models of cancer cells. In a recent paper by Ma et al., we have shown how a hierarchical map of cell structure can be embedded with a deep neural network, so that the model is able to accurately simulate the effect of mutations in genotype on the cellular phenotype.

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Evolution of information in HIV-1 protease

EVENT : C3BI Seminars


Main speaker : Chris Adami, from Michigan State University Date : 06-06-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Highly-active anti-retroviral therapy has been extremely effective at maintaining low levels of viral load in HIV-infected individuals, but emerging drug resistance is threatening those gains. When therapy is interrupted even briefly, HIV can evolve resistance to one or multiple drugs. Understanding how to stop viral evolution is an important goal of current research. I use HIV-1 protease sequences from public databases to study the dynamics of evolution over a span of nearly ten years, to compare patterns of adaptation in populations that are drug-naive to those that have taken one or multiple protease inhibitors. Using information theory, I show that the amount of information stored in protease sequences of patients that are on drug therapy has been increasing over time, suggesting that they are adapting to the drugs. In comparison, there is no increase in information in the sequences of patients that are drug naive. However, for the virus the increase in information comes at a price: because most of the information is stored in correlations between residues, the sequences are evolving into a more rugged area of the fitness landscape, which could make further evolution more difficult. While the data up to 2006 do not suggest a slowing down of evolution, such a trend may exist in data from later years not analyzed here.


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A population genetic interpretation of complex trait architecture in humans

EVENT : C3BI Seminars


Main speaker : Guy Sella, from Department of Biological Sciences, Columbia University Date : 02-05-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architectures are shaped by basic population genetic processes—notably, by mutation, natural selection, and genetic drift. Because many complex traits are subject to stabilizing selection and genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model at steady state, to find that the distribution of variances contributed by loci identified in GWASs should be well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. This prediction fits the findings of GWASs for height and body mass index (BMI) well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose during the Out-of-Africa bottleneck at sites with selection coefficients around s = 0.001.


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Insights into early human migrations with modern and ancient genomic data

EVENT : C3BI Seminars


Main speaker : Anna-Sapfo Malaspinas, from Department of computational biology, Université de Lausanne
Date : 14-03-2019 at 02:00 pm
Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Anna-Sapfo Malaspinas is assistant professor in the Department of computational biology of Université de Lausanne since 2017.
Her work aims to characterize evolutionary processes (genetic drift, natural selection, migration and mutation), using genomics data from both modern and ancient samples. Her group develops analytical and computational methods to analyse and interpret time-sampled data and applies those methods to novel ancient DNA datasets. Her work allows quantification and timing of adaptive and migration events, in particular in the context of human colonization of the world.


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Using Systems Approaches to Understand the Mechanism of Disease

EVENT : C3BI Seminars


Main speaker : Nevan Krogan, from Quantitative Biosciences Institute , UC San Francisco, USA Date : 11-04-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


There is a wide gap between the generation of large-scale biological data sets and more-detailed, structural and mechanistic studies. However, recent work that explicitly combine data from systems and structural biological approaches is having a profound effect on our ability to predict how mutations and small molecules affect atomic-level mechanisms, disrupt systems-level networks and ultimately lead to changes in organismal fitness. Our group aims to create a stronger bridge between these areas primarily using three types of data: genetic interactions, protein-protein interactions and post-translational modifications.  Protein structural information helps to prioritize and functionally understand these large-scale datasets; conversely global, unbiasedly collected datasets helps inform the more mechanistic studies. Our efforts in this respect have been focused on three disease areas: cancer, infectious diseases and neuropsychiatric disorders. Our work has found remarkable similarities between these and other disease areas which are leading to novel therapeutic strategies.


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Human gut resistome

EVENT : C3BI Seminars


Main speaker : Amine Ghozlane, from HUB, C3BI Pasteur Date : 04-04-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens via mobile genetic elements. Yet, this assumption is poorly supported by empirical evidence due to the distant homologies between known ARDs (mostly from culturable bacteria) and ARDs from the intestinal microbiota. Consequently, an accurate census of intestinal ARDs (that is, the intestinal resistome) has not yet been fully determined. For this purpose, we developed and validated an annotation method (called pairwise comparative modelling) on the basis of a three-dimensional structure (homology comparative modelling), leading to the prediction of 6,095 ARDs in a catalogue of 3.9 million proteins from the human intestinal microbiota. We found that the majority of predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino acid identity 29.8%) and found little evidence supporting their transfer between species. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n = 663) into six resistotypes that were connected to the previously described enterotypes. Finally, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results indicate that the majority of intestinal microbiota ARDs can be considered intrinsic to the dominant commensal microbiota and that these genes are rarely shared with bacterial pathogens.

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