Project summary

You will participate to a on-going project to study how a profile of RNA modifications measured in blood biopsy can help predict glioma at different stages of this brain cancer, as well as the evolution of the tumor.

This part of the project involves designing, developping, and running machine learning algorithms and approaches to exploit vectors of RNA modification quantities observed in patients. The measurements are performed by our local partners (Alex David IRCM, Christophe Hirtz PPC/CHU Montpellier) in a unique and local mass spectrometry platform that has developed special procedures for measurements in RNAs.

2. The second part of the project deals with bioinformatics approaches to detect and study the role mutations in translation of RNA sequences, and their correlation with RNA modifications. The idea is to analyze transcriptome and translatome data to see determine which DNA mutations are transcribed and then translated, and which are not translated. Only mutations located in RNAs that are translated have an impact on the produced proteins. Others mutations don’t. It is known that translation varies with physiological conditions and that ribosomes can select or discard certains RNAs. Translatome of cancer cells have been produced via high throughput sequencing and generate libraries similar to RNA-seq, except that these are restricted to RNA sequences that have been translated.

The goal is to develop computational algorithms and tools to analyse such sequencing data, to predict translated mutations and their potential effect on the fate of an RNA. Once pools of mutations have been detected, it is interesting to correlate them with locations of epitranscriptomic modifications, to seek potential functional interactions.

This part of the work requires knowledge in and interest for algorithms for high throughput sequence analysis, in data structures for sequence data, and in processing of deep sequencing data (see the book of Enno Ohlebusch, entitled “Bioinformatics Algorithms”, 2013, available at: https://www.uni-ulm.de/in/theo/m/ohlebusch/ or the book “Genome-Scale Algorithm Design” by Makinen et al, 2nd edition https://www.genome-scale.info/).

You will develop efficient programs that implement the designed algorithms, and run computational analyses. The results can be confronted to knowledge databases to cancer genes.

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