Researcher
Bioinformatics Research In Disease Genomics (BRIDGe)
jean.monlong@inserm.fr
https://jmonlong.github.io/
0000-0002-9737-5516
jmonlong
05 62 74 45 30
B522

Mini-CV

• I graduated from the École nationale supérieure d’informatique et de mathématiques appliquées (ENSIMAG) in Grenoble with a specialization in bioinformatics.
• My engineering school studies ended with a research project studying alternative splicing from RNA sequencing data at the Center for Genomic Regulation (CRG) in Barcelona (Spain) under the supervision of Dr. Roderic Guigó
• For my PhD, I developed methods to detect and study copy-number variation in human cohorts with whole-genome sequencing data in the laboratory of Dr. Guillaume Bourque, at McGill University, in Montreal (Canada).
• As a postdoc, I combined my interest for structural variation with the new pangenomic methods that were being developed at the University of California, Santa Cruz (UCSC) in the Computational Genomics Lab of Dr. Benedict Paten, in Santa Cruz (USA).
• I joined as a CRCN INSERM in May 2023. I will continue developing and using pangenomic approaches to study structural variation, with more focus toward characterizing their functional impact, especially in the disease context.

More up-to-date informations, publications, … at jmonlong.github.io/.

Expertise: Genomics, variant analysis, pangenomics.

Research projects

Structural variants (SVs) are defined as genomic variations involving 50 nucleotides or more. They can take multiple forms, from simple deletions and insertions to complex rearrangements formed by multiple SV types like inversions, translocations and duplications. Most SVs are under-studied because of the technical challenges to identify them. Their impact is also more difficult to predict, as they often affect multiple functional elements or can trigger epigenetic changes.

Although the sequencing of short DNA fragments of about 300 nucleotides (short read sequencing) is widely used today, it cannot identify many types of SVs. Long read sequencing, in contrast, can sequence longer DNA fragments (thousands of nucleotides) and detect SVs at much higher resolution, but remains costly. We have shown that short read sequencing data can be analyzed with pangenomes to accurately genotype SVs that had been discovered with long read sequencing. A pangenome represents multiple genomes and provides a framework to enhance a reference genome by integrating known variants.

My research goal is to develop pangenome-oriented tools to better detect SVs, integrate them into genome-wide association studies (GWAS), and predict their functional impact. With those tools, the aim is to identify novel disease genetic factors or identify the causal variant of known disease associations.

Publications

• ORCID: http://orcid.org/0000-0002-9737-5516
• PubMed