Mots-Clés

RNA-seq cancer transcriptomics indexation k-mers

Description

Missions
We are seeking a highly motivated postdoc to join a multi-team consortium that invents, implements and applies novel computational tools to discover disease-specific RNA sequences within large RNA datasets. The postdoc will also contribute to the creation of extensive RNA-seq cancer/normal data indexes that will be offered to the scientific community.

Skills
We welcome candidates with:
- either a methodological background with skills in algorithmic and software development (C++, Rust) to participate in the improvement of tools in collaboration with computer science collaborators.
- or a biology oriented background with strong expertise in cancer transcriptomics/genomics and a desire to address cancer biology questions by leveraging the statistical power of large RNA databases.
In all cases, a basic skill set is expected:
- Good command of basic bioinformatics concepts/sequence analysis, RNA-seq data analysis
- Knowledge of Python, Shell, and R languages
- Best practices in bioinformatics
- Ability to work on a computing cluster

Activities
Development and utilization of RNA-seq analysis pipelines using k-mer methods.
For 'methodological' profiles: contribution to the design and optimization of tools.
For 'biologist' profiles: curation of RNA-seq metadata and functional analysis of tumor-specific RNA lists.
In all cases: writing scientific publications in bioinformatics or onco-genomics journals.

Work Context
I2BC is a large institute of molecular and cellular biology located 25 km from Paris (Gif-sur-Yvette), hosting several computational biology teams, a bioinformatics platform, and a high-performance computing center. Our lab (RNA Sequence, Structure and Function) consists of about ten researchers, engineers, and doctoral students interested in non-coding or mis-coding RNAs, particularly in cancer. The core of our work is data mining of large RNA-seq databases. We collaborate with computer scientists specialized in data structures, molecular biologists, and oncologists. Together with these partners, we have secured several grants to use k-mer based methods to create extensive RNA-seq data indexes of cancerous and normal tissues, and to utilize these tools to identify tumor-specific RNAs and RNAs of prognostic or diagnostic value. 

References related to the project
-    Bessière C, Xue H, Guibert B, Boureux A, Rufflé F, Viot J, Chikhi R, Salson M, Marchet C, Commes T, ¬Gautheret D. (2024) Exploring a large cancer cell line RNA-sequencing dataset with k-mers. BioRxiv. https://doi.org/10.1101/2024.02.27.581927
-     Xue H, Gallopin M, Marchet C, Nguyen TNH, Wang Y, Bessière C, Gautheret D. (2024) KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction. Bioinformatics. btae090, https://doi.org/10.1093/bioinformatics/btae090. 
-    Wang Y, Xue H, Aglave M, Lainé A, Gallopin M, Gautheret D. (2022) The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma. NAR Cancer. 4:1. https://doi.org/10.1093/narcan/zcac001  
-     Nguyen Ha TN, Xue H, Firlej V, Ponty Y, Gallopin M, Gautheret D. (2021) Reference-Free Transcriptome Signatures for Prostate Cancer Prognosis. BMC Cancer. 12:394. https://doi-org/10.1186/s12885-021-08021-1
-    Marchet C, Iqbal Z, Gautheret D, Salson M, Chikhi R. (2020) REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets Bioinformatics. 36(suppl.). i177-i185.  https://doi.org/10.1093/bioinformatics/btaa487