Algorithms for detecting complex variants in unassembled NGS data
In the context of the ANR Colib'read (Calling Biological information from raw reads), we propose a 18 months postdoctoral position. It will be located in the Inria/Irisa genscale team, Rennes, France (http://team.inria.fr/genscale). A major expertise domain of this team is the development of methods for the extraction of relevant biological information from high throughput sequencing data (NGS), bearing in mind the objective to design scalable (both in term of time and memory) algorithms for processing such huge datasets.
The aim of this project is to propose new methods to efficiently detect polymorphisms and variants while working with unassembled NGS data for which no reference genome is available. While such method has already been developed and validated for the detection of small polymorphisms such as SNPs, the successful candidate will focus on more complex variants that are structural variants. She/he will design, implement, and validate the new method. A following line of work will be the integration of the detection of these various biological variants (SNPs, indels, structural variants) and their combinations into one single tool.
For further details visit the team web site: http://team.inria.fr/genscale and the colib'read web site: http://colibread.inria.fr/
Profile: - PhD in computational biology or algorithmics with a strong interest for biology - good programming skills (C, C++, python, ...) - background in sequence and graph algorithmics - a prior experience with high throughput sequencing data will be appreciated
Contacts and application Applicants should send a complete CV, a cover letter and contact information of at least two references to: Claire Lemaitre: claire.lemaitre@inria.fr Pierre Peterlongo: pierre.peterlongo@inria.fr
