Computational microbial genomics

EVENT : C3BI Seminars


Main speaker : Zamin Iqbal, from Royal Society/Wellcome Trust Sir Henry Dale Fellow, EMBL-EBI Date : 07-03-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


TBA


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Unit Seminar – Lucas Husquin & Jakob Ruess

 

EVENT : C3BI Unit Seminars


Main speaker : Lucas Husquin, from Human evolutionary genetics unit Date : 15-02-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Lucas Husquin (Human evolutionary genetics unit) : “Dissecting the impact of population variation in DNA methylation on transcriptional responses to immune activation”

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Jakob Ruess (InBio : Experimental and computational methods for modeling cellular processes) : “Virtual reality for bacteria”


Unit Seminar – Frédéric Lemoine & Lyam Baudry

 

EVENT : C3BI Unit Seminars


Main speaker : Frederic Lemoine from Bioinformatique evolutive Unit Date : 18-01-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Frederic Lemoine (Bioinformatique Evolutive) : “Renewing Felsenstein’s Phylogenetic Bootstrap in the Era of Big Data”

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Lyam Baudry (Spatial Regulation of Genomes) : “Metagenome binning using chromosome conformation capture (3C) data”


Seminars – Statistical design and analysis of reproducible quantitative mass spectrometry-based experiments

EVENT : C3BI Seminars

Statistical design and analysis of reproducible quantitative mass spectrometry-based experiments


Main speaker : Olga Vitek, from Northeastern University, Boston, MA, USA
Date : 07/12/2017 at 02:00 pm
Location : Salle Retrovirus-LWOFF, Institut Pasteur, Paris


Statistical methodology is key for quantitative proteomics, as it helps reduce bias and inefficiencies,  distinguish the systematic variation from random artifacts, and maximize the reproducibility of the results. This talk will overview the statistical methodology implemented in MSstats, an open-source R package for statistical relative quantification of proteins and peptides. MSstats supports experiments with complex designs, such as comparisons of multiple groups or time course comparisons. It handles quantitative shotgun DDA (data-dependent acquisition) experiments, targeted SRM (selected reaction monitoring), and SWATH/DIA (data independent acquisition) experiments. It can be used in conjunction with label-free experimental workflows, or with workflows that utilize stable isotope reference proteins or peptides. MSstats contains functionalities for data processing, model-based statistical analysis (including testing proteins and peptides for differential abundance, or estimating protein abundance on a relative scale), and model-based calculation of a sample size for a future experiment. It also contains functionalities for systems suitability and statistical process control, and for quantification of figures of merit (such as limit of detection) of mass spectrometric assays. MSstats is available stand-alone or via graphical user interface as an external tool in the software framework Skyline. It can be interfaced with numerous spectral processing tools, such as MaxQuant.


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Unit Seminar – Marie Lopez & Bradley Worley

EVENT : C3BI Seminars

Marie Lopez: “The demographic history and mutational load of African hunter-gatherers and farmers”

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 Bradley Worley :”Statistical model-based methods of sampling in nuclear magnetic resonance” 


Main speaker : Marie Lopez & Bradley Worley, from C3BI Department Date : 26/10/2017 at 02:00 pm Location : Retrovirus room – LWOFF (22) ,Institut Pasteur, Paris


Marie Lopez from the Unit Génétique évolutive humaine and Bradley Worley from the INBIO Unit (Méthodes expérimentales et computationnelles pour la modélisation des processus cellulaires) will present their current research.


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Seminars – Perturbing the interactome: multi-omics and personalized methods for network medicine

EVENT : C3BI Seminars

Perturbing the interactome: multi-omics and personalized methods for network medicine


Main speaker : Marc Santolini, from Network Science Institute at Northeastern University, Boston Date : 19/10/2017 at 03:00 pm Location : Module 1-2-3 – SOCIAL BUILDING (06) ,Institut Pasteur, Paris


In this talk, I will describe several recently developed methods to study disease perturbations through the lens of network science. First I will present an investigation of the personalized gene expression responses when inducing hypertrophy and heart failure in 100+ strains of genetically distinct mice from the Hybrid Mouse Diversity Panel (HMDP). I will show that genes whose expression change significantly correlates with the severity of the disease are either up- or down-regulated across strains, and therefore missed by traditional population-wide analyses of differential gene expression. These uncovered personalised genes are enriched in human cardiac disease genes and form a dense co-regulated module strongly interacting with the cardiac hypertrophic signaling network in the human interactome, the set of molecular interactions in the cell. We validate our approach by showing that the knockdown of Hes1, predicted as a strong candidate, induces a dramatic reduction of hypertrophy by 80-90% in neonatal rat ventricular myocytes, demonstrating that individualized approaches are crucial to identify genes underlying complex diseases as well as to develop personalized therapies. Then, I will present a novel approach to identify the collective impact of miRNAs in disease. Instead of focusing on the magnitude of miRNA differential expression, here we address the secondary consequences for the interactome. We developed the Impact of Differential Expression Across Layers (IDEAL), a network-based algorithm to prioritize disease-relevant miRNAs based on the central role of their targets in the molecular interactome. This method was used in the context of asthmatic Th2 inflammation and identified five Th2-related miRNAs (mir27b, mir206, mir106b, mir203, and mir23b) whose antagonization led to a sharp reduction of the Th2 phenotype. This result offers novel approaches for therapeutic interventions. Finally, I will present evidence that one can accurately predict perturbation patterns from the topology of biological networks, even when lacking measurements on the kinetic parameters governing the dynamics of these interactions. Using 87 biochemical networks with experimentally measured kinetic parameters, we show that a knowledge of the network topology offers 65% to 80% accuracy in predicting the impact of perturbations. In other words, we can use the increasingly accurate topological models to approximate perturbation patterns, bypassing expensive kinetic constant measurement. These results open new avenues in modeling drug action, and in identifying drug targets relying on the human interactome only.


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Seminars – A leap forward in biological link prediction

EVENT : C3BI Seminars

A leap forward in biological link prediction


Main speaker : Itsvan Kovacs, from Network Science Institute at Northeastern University, Boston Date : 19/10/2017 at 02:00 pm Location : Module 1-2-3 – SOCIAL BUILDING (06) ,Institut Pasteur, Paris


Biological function emerges from the complex interplay between molecules in our cells, comprising the human interactome. While interactions between proteins play a central role in the interactome, current maps are still missing the majority of these interactions. State-of-the-art network-based link prediction tools rely on the triadic closure principle, stating that proteins are likely to interact if they share many of their interaction partners, utilizing network paths of length l=2. We show that this principle is valid only for the small fraction of self-interacting proteins, while it fails completely for the rest of the network. Supported by both evolutionary and protein structural arguments, we identify missing protein interactions based on l=3 paths. Our top predictions validate as well experimentally as known protein-protein interactions, outperforming previous methods at least 2-3 fold. Our Length 3 Association Prediction (L3AP) approach provides a fundamental biological principle with a broad potential applicability, including also protein associations (i.e. co-complex membership) information, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.


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Seminars – Emergence of de novo protein coding genes from ‘dark genomic matter’ — fact or fiction?

EVENT : C3BI Seminars

Emergence of de novo protein coding genes from ‘dark genomic matter’ — fact or fiction?


Main speaker : Erich Bornberg-Bauer, from The Westfalian Wilhelms University of Muenster, Germany
Date : 09/11/2017 at 02:00 pm
Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Proteins are the workhorses of the cell and, over billions of years, they have evolved an amazing plethora of extremely diverse and versatile structures with equally diverse functions. Therefore, their evolution echoes the evolution of all forms of life. Evolutionary emergence of new proteins and transitions between existing ones are widely believed to be rare or even impossible.
However, recent advances in comparative genomics have repeatedly called some 10%-30% of all genes without any detectable similarity to existing proteins. Even after careful scrutiny, some of those “orphan” genes contain protein coding reading frames with detectable transcription and translation. Thus some proteins seem to have emerged from previously non-coding ‘dark genomic matter’. These ‘de novo’ proteins tend to be disordered, fast evolving, weakly expressed but also rapidly assuming novel and physiologically important functions. I will review mechanisms by which ‘de novo’ proteins might be created, under which circumstances they may become fixed and why they are elusive. I will present a couple of studies which mostly focus on metazoan genomes.


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Seminars – Computer Vision & Computational Optics for Bioimage Informatics

EVENT : C3BI Seminars

Computer Vision & Computational Optics for Bioimage Informatics


Main speaker : Eugene Myers, from Director, Center for Systems Biology Dresden, Max Planck Institute for Molecular Cell Biology and Genetics Date : 21/09/2017 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Eugene Myers: Best known for contributing to the early development of the BLAST tool for sequence analysis. His 1990 paper (with Stephen Altschul and others) describing BLAST has received over 62,000+ citations making it amongst the most highly cited papers ever. Myers was also the Vice President of Informatics Research at Celera Genomics, where he was involved in the sequencing of the human genome, as well as the genomes of Drosophila and mouse. In 2012, Myers moved to Dresden to lead a new center for systems biology.
Abstract:
Our group has been actively pursuing the idea that with great microscopes and great informatics we will be able to truly digitize models of cells, tissues, and organisms through time with information about the genetic and proteomic states of each cell layered there on. The belief is that these atlases combined with optical observations of labeled entities will accelerate the life sciences by allowing us to visualize these systems from any vantage point and as a system, thus leading to many discoveries such as the nature of the genetic control of fly wing development.
Since arriving in Dresden five years ago we have made significant progress on hard segmentation and tracking problems with the use of AI techniques developed in the computer vision community. We will present several examples of current projects that exemplify various such techniques and present the quality of results we obtain with them.
Despite these improvements we still find ourselves at the limit of what can be determined because of the limited resolution and contrast of the imagery. Fortunately, advances in microscope componentry such as adaptive optics, spatial light modulators, and ultra high-speed cameras present opportunities for improving the underlying imagery. We will report on two microscope development projects in our lab, where the aim is to improve resolution by making multiple observations of a volume and from them computing a better reconstruction of the object under observation using deep neural networks. We think better microscopy through computation and dynamic onboard control of acquisitions is an emerging trend that we generally call computational optics.


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Seminars – Linking gene and function by integrated approaches: how to improve the poor annotation status of sequenced genomes

EVENT : C3BI Seminars

Linking gene and function by integrated approaches: how to improve the poor annotation status of sequenced genomes


Main speaker : Valérie de Crécy-Lagard, from Professor University of Florida – Department of Microbiology and Cell Science & Genetics Institute Date : 07/09/2017 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Linking gene and function by integrated approaches: how to improve the poor annotation status of sequenced genomes

Identifying the function of every gene in all sequenced organisms is the major challenge of the post-genomic era and an obligate step for any systems biology approach. This objective is far from reached. By various estimates, at least 30-50% of the genes of any given organism are of unknown function, incorrectly annotated, or have only a generic annotation such as “ATPase”. Moreover, with ~8000 genomes sequenced and ~80,000 in the pipeline (http://www.genomeson.line.org), the numbers of unknown genes are increasing, and annotation errors are proliferating rapidly. For some gene families, 40% of the annotations are wrong. On the other side of the coin, there are still ~1,900 known enzyme activities for which no corresponding gene has been identified and these numbers are also increasing. This biochemical knowledge is yet to be captured in genome annotations.
Using mainly a comparative genomic approach, we have linked gene and function for around 50 gene families related mainly to the fields of coenzyme metabolism, tRNA modification, protein modification and more recently metabolite repair. This approach integrates several types of data and uses filters, sieves, and associations to make predictions that can then be tested experimentally. An unknown gene’s function may thus be predicted from those of its associates: the ‘guilt by association’ principle. Associations that can be derived from whole genome datasets include: gene clustering, gene fusion events, phylogenetic occurrence profiles or signatures and shared regulatory sites. Post-genomic experimental sources such as protein interaction networks, gene expression profiles and phenomics data can also be used to find associations. In practice it is often ‘guilt by multiple association’ as genes can be associated in several ways, and analyzing more than one of these improves the accuracy of predictions. If these types of comparative genomic approaches were systematically used to annotate genomes, the quality of annotations would greatly improve. Also the experimentalists need to be more involved in the annotation process, as without expert knowledge the curation effort is beyond what annotation resources such as Uniprot or NCBI can do alone.


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