Section: Partnerships and Cooperations

International Initiatives

• Prevac-Up: this EDCTP-funded research program will collect and analyze important information on the effectiveness and maintainability of Ebola vaccination in sub-Saharan Africa.

• NIPAH program: a Franco-Chinese collaboration of 1 million euros per country over 5 years to develop a program to better understand Nipah Virus infection, its physiopathology and to develop new tools for diagnosis, treatment and prevention.

Inria International Labs

Inria@SiliconValley

Associate Team involved in the International Lab:

SWAGR

• Title: Statistical Workforce for Advanced Genomics using RNAseq

• International Partner (Institution - Laboratory - Researcher):

  • RAND Corporation (United States) - Statistics group - Denis Agniel

• Start year: 2018

• See also: https://team.inria.fr/swagr/

• The SWAGR Associate Team aims at bringing together a statistical workforce for advanced genomics using RNAseq. SWAGR combines the biostatistics experience of the SISTM team from Inria BSO with the mathematical expertise of the statistics group at the RAND Corporation in an effort to improve RNAseq data analysis methods by developing a flexible, robust, and mathematically principled framework for detecting differential gene expression. Gene expression, measured through the RNAseq technology, has the potential of revealing deep and complex biological mechanisms underlying human health. However, there is currently a critical limitation in widely adopted approaches for the analysis of such data, as edgeR, DESeq2 and limma-voom can all be shown to fail to control the type-I error, leading to an inflation of false positives in analysis results. False positives are an important issue in all of science. In particular in biomedical research when costly studies are failing to reproduce earlier results, this is a pressing issue. SWAGR propose to develop a rigorous statistical framework modeling complex transcriptomic studies using RNAseq by leveraging the synergies between the works of B. Hejblum and D. Agniel. The new method will be implemented in open-source software as a Bioconductor R package, and a user friendly web-application will be made available to help dissemination. The new method will be applied to clinical studies to yield significant biological results, in particular in vaccine trials through existing SISTM partnerships. The developed method is anticipated to become a new standard for the analysis of RNAseq data, which are rapidly becoming common in biomedical studies, and has therefore the potential for a large impact.

Inria International Partners

• Fred Hutchinson Cancer center, Seattle;

• Baylor Institute for Immunology (Dallas);

• Duke University -Duke Global Health Institute, Elizabeth Turner.

• Harvard University - Department of Population Medicine - Rui Wang. M. Prague is a co-PI in an amfAR project for co-supervizion of a PhD student.

• university of San Diego UCSD / Harvard School of public health - NIH program project grant "Revealing Reservoirs During Rebound", Harvard School of Public Health (HSPH) and the University of California, San Diego (P01AI131385, total budget $1.5M/yr for 5 years starting Oct 2017, both university manage the funding. Mélanie Prague is part of modelling unit of the "Quantitative Methods" research project (budget $220,000/yr). The principal investigator for this core is Victor de Grutolla (HSPH) The overall goal of this grant is to characterize viral rebound following antiretroviral therapy cessation in cohorts of patients who have started therapy early in infection, as well as in a cohort of terminally-ill patients who will interrupt therapy before death and subsequently donate their bodies to research.

• Harvard University - program for evolutionary dynamics - Alison hill and Martin Nowak. Project submitted by M Prague for the Inria associated team with this laboratory.

• Collaborations through clinical trials: NIH and University of Minnesota for the Prevac trial, NGO Alima for the Prevac trial, Several African clinical sites for Ebovac2 and Prevac trials;

• Tianxi Cai from Harvard University on developing methods for the linkage and analysis of Electronic Health Records data (Boris Hejblum).

• Katherine Liao from Harvard University on the analysis of Electronic Health Records data in the context of Rheumatoid Arthritis (Boris Hejblum).

• Sylvia Richardson from MRC Biostatistics Unit Cambridge University on the scaling up of nonparametrics Bayesian appraches to Big data (Boris Hejblum).

• Machine learning team Data61 at CSIRO, Australia (Marta Avalos)