Experience

Positions after PhD

 
 
 
 
 

Research director

LIRMM, CNRS

Jan 2016 – Present Montpellier, France

Responsibilities include:

  • Head of Institute of Computational Biology (2015-2019)
  • Head of French Molecular Bioinformatics network (2010-2016)
  • Head of computer science dpt (2007-2010)
 
 
 
 
 

Reasearcher

LIRMM, CNRS

Jan 2008 – Oct 1999 Montpellier, France
Bioinformatics and algorithmics research.
 
 
 
 
 

Postdoctoral fellow

German Cancer Research Center (Deutsches Krebsforschung Zentrum - DKFZ)

Sep 1996 – Oct 1999 Heidelberg, Germany
Algorithms for transcriptomics.

News

News about software, results, publications, or collaborations

In a consortium allying a Mass Spectrometry platform, a cancer biology lab, several clinicians, and a bioinformatic team, we investigated how epigenetic marks on the RNA (a.k.a., the epitranscriptome) can help distinguishing the different grades of glioma tumors from tissue samples. We came up with a pipeline that proceeds surgical samples, measures the level of epitranscriptomic marks, and predicts the grade of this cancer. Our method delivers grade prediction with unmet sensitivity and specificity.

Nice workshop about strings, data structures, and algorithms in Bioinformatics

Jordan successfully defended his Master thesis and won a PhD fellowship

Projects

ALL THINGS ARE DIFFICULT BEFORE THEY ARE EASY

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ALPACA

EU funded Int’al Training Network on Computational Pan-Genomics

Superstring

Algorithms for shortest superstring questions

Fast_place

Software for efficient metagenomics and applications to virus metagenomics

GEM

Information fuelled biophysical models for the control of gene expression

Recent Publications

One of the main challenges in cancer management relates to the discovery of reliable biomarkers, which could guide decision-making and predict treatment outcome. In particular, the rise and democratization of high-throughput molecular profiling technologies bolstered the discovery of “biomarker signatures” that could maximize the prediction performance. Such an approach was largely employed from diverse OMICs data (i.e., genomics, transcriptomics, proteomics, metabolomics) but not from epitranscriptomics, which encompasses more than 100 biochemical modifications driving the post-transcriptional fate of RNA: stability, splicing, storage, and translation. We and others have studied chemical marks in isolation and associated them with cancer evolution, adaptation, as well as the response to conventional therapy. In this study, we have designed a unique pipeline combining multiplex analysis of the epitranscriptomic landscape by high-performance liquid chromatography coupled to tandem mass spectrometry with statistical multivariate analysis and machine learning approaches in order to identify biomarker signatures that could guide precision medicine and improve disease diagnosis. We applied this approach to analyze a cohort of adult diffuse glioma patients and demonstrate the existence of an “epitranscriptomics-based signature” that permits glioma grades to be discriminated and predicted with unmet accuracy. This study demonstrates that epitranscriptomics (co)evolves along cancer progression and opens new prospects in the field of omics molecular profiling and personalized medicine.

Mechanisms of drug-tolerance remain poorly understood and have been linked to genomic but also to non-genomic processes. 5-fluorouracil (5-FU), the most widely used chemotherapy in oncology is associated with resistance. While prescribed as an inhibitor of DNA replication, 5-FU alters all RNA pathways. Here, we show that 5-FU treatment leads to the production of fluorinated ribosomes exhibiting altered translational activities. 5-FU is incorporated into ribosomal RNAs of mature ribosomes in cancer cell lines, colorectal xenografts, and human tumors. Fluorinated ribosomes appear to be functional, yet, they display a selective translational activity towards mRNAs depending on the nature of their 5′-untranslated region. As a result, we find that sustained translation of IGF-1R mRNA, which encodes one of the most potent cell survival effectors, promotes the survival of 5-FU-treated colorectal cancer cells. Altogether, our results demonstrate that “man-made” fluorinated ribosomes favor the drug-tolerant cellular phenotype by promoting translation of survival genes.

The last decade has seen mRNA modification emerge as a new layer of gene expression regulation. The Fat mass and obesity-associated protein (FTO) was the first identified eraser of N6-methyladenosine (m6A) adducts, the most widespread modification in eukaryotic messenger RNA. This discovery, of a reversible and dynamic RNA modification, aided by recent technological advances in RNA mass spectrometry and sequencing has led to the birth of the field of epitranscriptomics. FTO crystallized much of the attention of epitranscriptomics researchers and resulted in the publication of numerous, yet contradictory, studies describing the regulatory role of FTO in gene expression and central biological processes. These incongruities may be explained by a wide spectrum of FTO substrates and RNA sequence preferences: FTO binds multiple RNA species (mRNA, snRNA and tRNA) and can demethylate internal m6A in mRNA and snRNA, N6,2′-O-dimethyladenosine (m6Am) adjacent to the mRNA cap, and N1-methyladenosine (m1A) in tRNA. Here, we review current knowledge related to FTO function in healthy and cancer cells. In particular, we emphasize the divergent role(s) attributed to FTO in different tissues and subcellular and molecular contexts.

Cancer stem cells (CSCs) are a small but critical cell population for cancer biology since they display inherent resistance to standard therapies and give rise to metastases. Despite accruing evidence establishing a link between deregulation of epitranscriptome-related players and tumorigenic process, the role of messenger RNA (mRNA) modifications in the regulation of CSC properties remains poorly understood. Here, we show that the cytoplasmic pool of fat mass and obesity-associated protein (FTO) impedes CSC abilities in colorectal cancer through its N6,2’-O-dimethyladenosine (m6Am) demethylase activity. While m6Am is strategically located next to the m7G-mRNA cap, its biological function is not well understood and has not been addressed in cancer. Low FTO expression in patient-derived cell lines elevates m6Am level in mRNA which results in enhanced in vivo tumorigenicity and chemoresistance. Inhibition of the nuclear m6Am methyltransferase, PCIF1/CAPAM, fully reverses this phenotype, stressing the role of m6Am modification in stem-like properties acquisition. FTO-mediated regulation of m6Am marking constitutes a reversible pathway controlling CSC abilities. Altogether, our findings bring to light the first biological function of the m6Am modification and its potential adverse consequences for colorectal cancer management.

Novel recombinant viruses may have important medical and evolutionary significance, as they sometimes display new traits not present in the parental strains. This is particularly concerning when the new viruses combine fragments coming from phylogenetically distinct viral types. Here, we consider the task of screening large collections of sequences for such novel recombinants. A number of methods already exist for this task. However, these methods rely on complex models and heavy computations that are not always practical for a quick scan of a large number of sequences.We have developed SHERPAS, a new program to detect novel recombinants and provide a first estimate of their parental composition. Our approach is based on the precomputation of a large database of ‘phylogenetically-informed k-mers’, an idea recently introduced in the context of phylogenetic placement in metagenomics. Our experiments show that SHERPAS is hundreds to thousands of times faster than existing software, and enables the analysis of thousands of whole genomes, or long-sequencing reads, within minutes or seconds, and with limited loss of accuracy.The source code is freely available for download at https://github.com/phylo42/sherpas.Supplementary data are available at Bioinformatics online.

Popular Topics

Aho-Corasik algebraic technique algorithm algorithms alignment alignment score ALPACA alphabet size Anchor-based strategy ancient DNA Approximability approximate match approximate pattern matching approximate repeats approximation Approximation algorithm approximation algorithms APX assembly autocorrelation award bacteria Bacterial genomes Basic Period binary alphabet Binary Vector binding binding site bioinformatics biophysics BLAST bounds Burrows-Wheeler cancer cDNA character Characterisation chromatin chromosome circular permutation cluster analysis clustering clustering algorithms coding coiled coil Collinear fragment chaining common word Comparative genomics complexity compressed data structures compression compression algorithms compression gain computer science Concat-Cycles concensus string conformation Connectivity cross-over cyclic cover Cyclic string cyclic strings Cytoplasmic Male Sterility Data compression data structure Data structures database DCJ de Bruijn graph discrete line DNA dominance order double cut and join duplication dynamic programming edit distance encoding enumeration epitranscriptome equality EST Eulerian tour evaluation evolution Exact Match exponential filtration Gapped seed genetics genome genome rearrangement genome sequencing genomics Golomb ruler graph greedy greedy algorithm Greedy conjecture Haemophilus influenzae Hamiltonian path heuristic algorithms Hi-C homologous sequences human hybrid zone Hypergraph incomplete lineage sorting indexing information content information theory input string INS insulin integer sequence internal duplication intragenic recombination irreducible factor kinship Kolmogorov complexity lattice LCS Levenshtein distance linear superstring linear time linear time algorithm Longest common subsequence mapping tool matroid maximal chain Maximum coverage Maximum independent set Maximum stable set medecine memory metagenome metagenomics microorganisms microsatellite evolution Minimum assignment minisatellite minisatellite locus minisatellites MIS monkey test motif motif size mouse mRNA MS-Align multiple alignment multiple read mutation MVR N-gram NGS NP-complete NP-hard On-line algorithms optimal coding oryza line overlap overlap graph Pairwise alignment parameterized complexity path pattern Pattern recognition pattern search perfect detection periods Permutation phylogenetic profile Polynomial Time Approximation Scheme proportional length protein domain PWM radish genome random-access memory random text Read mapping rearrangement Recognition recombinant regular expression regularity detection regulation relative compression repeats reverse complementary sequence RLE RNA search algorithm seed segment tree seminar sequence sequence alignment sequence classification sequence comparison Sequence graph short tandem repeats Shortest cyclic cover of strings shortest DNA cyclic cover problem Shortest Superstring Problem similarity similarity metrics single cell software spaced-seed String matching stringology Stringology Text Algorithms Indexing Data Structures De Bruijn Graph Assembly Space Complexity Dynamic Update strings student sturgeon phylogeny subset system suffix array suffix tree superstring sweep line tandem duplication tandem repeat tandem repeat alignment tandem repeats team text text compression Text indexing Tiling time complexity tool training transcription factor transcriptome transcriptomics translation tree tree alignment validation score virus VNTR W[1]-hard web resource web server Whole genome alignment word enumeration word RAM model Yakuts zebra fish

Contact

Connect with me

  • (33) 04 67 41 86 64
  • LIRMM - UMR 5506 & University Montpellier (CC 05016) 860 rue de St Priest - 34095 Montpellier cedex 5 FRANCE