Our main scientific objective is to establish novel analysis frameworks for large scale omics analysis in human health.

The bioinformatics group includes biologists and bioinformaticians specialists in text-based algorithms who focus on designing new tools and structures for RNA-Seq analysis. We develop software and data structures for RNA-Seq data analysis (such as Gk-Arrays, CRAC, CracTools, ChimCT).  We create new strategies based on kmers capable of organizing reads to quickly respond to specific queries, such as the following pipelines: De-Kupl, Kmerator Suite and KmerExploR. see also: https://bio2m.montp.inserm.fr/)

As example of application of our tools, we used CRAC and its dedicated modules to characterize and classify fusion RNA or chimeric RNA (chRNA) in cancer (F Ruffle et al, 2017). The chRNA classification was later refined with machine learning approaches using private and public myeloid leukemia data sets (France Génomique sequencing project and LEUCEGENE cohort).

Another example of application is the use of “Kmerator Suite”, a bioinformatics pipeline we developed, in the purpose of building a cell-specific catalogue of unannotated long non-coding RNAs (lncRNAs). The pipeline uses a specific k-mer approach methodologies for naive quantification of expression in numerous RNAseq data (Riquier et al, BMC Genomics, 2021).

New development now seeks to establish a novel analysis framework for large scale OMICS data analysis in human health. In a primary approach, as a proof of concept, we attempt to establish a complete Encyclopedia of kmers as signature of abnormal transcripts in the context of acute myeloid leukemia (AML). The establishment of such signature should allow to explore better diagnostic and prognosis models for new therapeutic strategies

In 2015, the team, in association with CHU-Montpellier, created a bioinformatic platform for RNA-seq analysis called “BIO2M”. Link to Bio2M web page (https://bio2m.montp.inserm.fr/)