keyboard_arrow_leftBack

Join us

teaching

We are always keen to hear from candidates with an interest in DNA methylation, noncoding RNA and nuclear architecture.

Ph.D students and Postdocs fellows interested in joining the laboratory should send us a cover letter, a full CV and the names of three referees in order to study national and international solutions for fellowships.

Send all requests to Claire Francastel





Computational Biologist in Epigenetics

The appointment will be financed for up to 2 years (Plan Cancer)

Team: “DNA methylation and noncoding RNA in health and disease”
Address: CNRS UMR7216 Epigenetics and Cell Fate – University Paris Diderot
35, rue Hélène Brion
Bâtiment Lamarck
75205 PARIS Cedex 13

Contact: Claire Francastel

Expiration: Open until filled

Scientific context: Integrated view of epigenetic and transcriptional defects in physiopathological situations
Key words: Epigenetics, Disease, Bioinformatics, Molecular biology, DNA methylation, Chromatin, Functional analyses, Trascriptome, High-throughput data, Omics

A computational biologist position is available in the team of Claire Francastel at CNRS/University Paris Diderot University to generate and analyze Omics data aimed at providing an integrated view of epigenetic and transcriptomic perturbations that affect cells from mouse models or patients with compromised DNA methylation.
Work will be performed within the UMR “Epigenetics and Cell Fate” on the campus of the University Paris Diderot-Rive Gauche, in a fundamental research team which is interested in the molecular and cellular bases of a rare human disease affecting DNA methylation at repeated regions of the genome and unique loci. The projects capitalize on unique cellular models, including mouse models, ES cells and cells from patients, and on the generation of high throughput data. Pan-genomic methylation maps have already been generated in patients (Illumina 450K) and mouse models (RRBS). We now aim at providing a comprehensive and integrated view of the consequences of perturbed DNA methylation on epigenetic landscapes and on transcriptional output, including long and short regulatory non-coding RNA and splice variants, and functional assays to validate the data.

Profile and skills:

  • Master or PhD in bioinformatics or in molecular biology with strong experience in bioinformatics
  • Experience in analysis of large datasets of gene expression and epigenetic landscapes
  • Analyses of pathways and enrichment (Networks, Functional, Transcription Factor Binding Sites…)
  • Experience in use of bioinformatics
  • Programming skills (R, Python or equivalent)
  • Knowledge in genomics and epigenomics of human and/or mouse and databases
  • Communication in an interdisciplinary research group, organization, team work, autonomy, initiative, scientific rigor

  • Salary: Depending on diploma and experience (CNRS scale)

    Applications in the form of a CV with a brief statement of research experience, technical expertise and interests, as well as contact details of at least 2 references should be sent to Claire Francastel


    M2 Proposal

    Une solution logicielle pour catégoriser les petits ARN non codants 

    Nous venons d'identifier une nouvelle source de production de petits ARN non codants régulateurs issus de l'épissage d'introns. Afin de les caractériser et de les catégoriser, un nouvel outil est nécessaire.

    Il existe plusieurs serveurs web ou solutions logicielles permettant de détecter la présence d'un pre-miARN dans une séquence génomique. De même, les solutions pour identifier un ARNt ou un snoARN existent depuis plus de 10 ans, et malgré les découvertes récentes de nombreuses nouvelles séquences, et de caractéristiques particulières et spécifiques à chacun, aucun nouveau système d'identification n'a vu le jour. Enfin, aucune de ces solutions n'indiquent quelles sont les "chances" d'avoir un miARN plutôt qu'un snoARN. C'est pourquoi, nous souhaiterions développer une solution "tout-en-un", reprenant les caractéristiques récentes de chaque petits ARN non codants, de les "scorer", et de permettre une identification fiable de chacun d'entre eux. Il s'agira donc de récupérer les informations de la littérature, de classifier et pondérer les différents critères retenus, et de programmer (à l'aide de script ou programme déjà écrit sur le web) un logiciel complet et efficace. Des jeux de données connues (miRBase, snoRNAbase, etc…) serviront de jeux de tests.

    Send all requests to Florent Hubé