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Laurent Jacob
Chargé de recherche - CNRS
courriel :
tél : 04 72 44 85 98
"Biométrie et Biologie Évolutive"
UCB Lyon 1  - Bât. Grégor Mendel
43 bd du 11 novembre 1918
69622 VILLEURBANNE cedex
Bâtiment : Mendel 1er étage Bureau : 140

I am generally interested in developing statistical and machine learning methods to solve problems in molecular biology.

In particular, I have ongoing projects on :

  • Finding genetic determinants of bacterial resistances, with collaborators at Biomérieux.
  • Improving the inference of phylogenetic trees, with local collaborators at LBBE.
  • Detecting differential splicing, with local collaborators at LBBE.
  • Removing unwanted variation from gene expression data, with collaborators at Agendia, UCSF and WEHI.
  • To appear

    Louise Cheynel, Jean-François Lemaître, Jean-Michel Gaillard, Benjamin Rey, Gilles Bourgoin, Hubert Ferté, Maël Jégo, François Débias, Maryline Pellerin, Laurent Jacob and Emmanuelle Gilot-Fromont
    Immunosenescence patterns differ between populations but not between sexes in a long-lived mammal.Scientific Reports, September 2017

    Johann Gagnon-Bartsch, Laurent Jacob, and Terence P. Speed.
    Removing unwanted variation from high dimensional data with negative controls. To appear as a monograph from the Institute of Mathematical Statistics.

  • Software

    The code I use for my research is available either as a tarball or an R package :

    • dbgwas : software and data used in our bacterial GWAS paper.
    • Code used in the pepa test proteomics paper.
    • Code and data used to generate the plots in our random sampling study of the healthy ageing signature paper.
    • RUVnormalize : remove unwanted variation from gene expression data (bioconductor).
    • FlipFlop : identify transcripts and their abundances from RNASeq data (bioconductor).
    • NCIGraph : use NCI pathway database integration networks in R (bioconductor).
    • DEGraph : detect differentially expressed gene networks from expression data (bioconductor).
    • Overlasso : code and data used for the ICML 2009 overlapping group lasso paper, for reproducibility purpose. For efficiency, I recommend using the SPAMS library instead.
    • Clustered multi-task : code and data used for the NIPS 2008 multi-task paper.
    • GPCR : code and data used for the 2008 GPCR paper.
    • MHC : code and data used for the 2008 epitope prediction paper. We also developped KISS, a web application.