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EQUIPE 4 : METABOLISME DU FER: DE LA REGULATION A LA THERAPIE
TEAM 4 : IRON METABOLISM:FROM REGULATION TO THERAPY |
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.
- 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.
EXPERTISE
BIOINFOMATICS
Projets de recherche / research projects :
[English]
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.
[Français]
Les variants structuraux (VS) sont définis comme les variants génomiques affectant 50 nucléotides ou plus. Ils peuvent prendre plusieurs formes allant de simples délétions ou insertions à des réarrangements plus complexes composés de plusieurs types de variants tels qu'inversions, translocations et duplications. La majorité des VSs sont peu étudiés du fait des difficultés techniques pour les identifier. L'impact des VSs est aussi plus difficile à prédire, car ils affectent souvent plusieurs éléments génomiques ou participent à des mécanismes épigénétiques.
Bien que le séquençage de fragments d'ADN court ("lectures courtes") soit très utilisé aujourd'hui, il ne permet pas d'identifier tous les VSs. Le séquençage lectures longues, au contraire, peut identifier des VSs à une meilleure résolution, mais souffre d'un cout élevé. Nous avons montré que ces mêmes VSs peuvent être génotypés à partir de séquençage lectures courtes grâce à l'utilisation des pangénomes. Un pangénome représente plusieurs génomes et permet d'enrichir le génome de référence avec des variants connus.
Ma recherche a pour but de développer des outils utilisant la puissance des pangénomes, pour mieux détecter ces variants, les intégrer dans les études d'association, et prédire leur impact fonctionnel. À l'aide de ces nouveaux outils, le but est d'identifier de nouveaux facteurs génétiques de maladies, ou le variant causal pour des associations connues.
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.
[Français]
Les variants structuraux (VS) sont définis comme les variants génomiques affectant 50 nucléotides ou plus. Ils peuvent prendre plusieurs formes allant de simples délétions ou insertions à des réarrangements plus complexes composés de plusieurs types de variants tels qu'inversions, translocations et duplications. La majorité des VSs sont peu étudiés du fait des difficultés techniques pour les identifier. L'impact des VSs est aussi plus difficile à prédire, car ils affectent souvent plusieurs éléments génomiques ou participent à des mécanismes épigénétiques.
Bien que le séquençage de fragments d'ADN court ("lectures courtes") soit très utilisé aujourd'hui, il ne permet pas d'identifier tous les VSs. Le séquençage lectures longues, au contraire, peut identifier des VSs à une meilleure résolution, mais souffre d'un cout élevé. Nous avons montré que ces mêmes VSs peuvent être génotypés à partir de séquençage lectures courtes grâce à l'utilisation des pangénomes. Un pangénome représente plusieurs génomes et permet d'enrichir le génome de référence avec des variants connus.
Ma recherche a pour but de développer des outils utilisant la puissance des pangénomes, pour mieux détecter ces variants, les intégrer dans les études d'association, et prédire leur impact fonctionnel. À l'aide de ces nouveaux outils, le but est d'identifier de nouveaux facteurs génétiques de maladies, ou le variant causal pour des associations connues.
Techniques et méthodes / TECHNICS AND METHODS
- Analysis of DNA sequencing data (both short and long reads techonologies)
- Computational genomics.
production scientifique / scientific production
ORCID: http://orcid.org/0000-0002-9737-5516
Full list of publications: Pubmed
Selected publications:
Full list of publications: Pubmed
Selected publications:
- W Liao, M Asri, J Ebler, D Doerr, M Haukness, G Hickey, S Lu, JK Lucas, J Monlong, …, G Bourque, MJP Chaisson, P Flicek, AM Phillippy, JM Zook, EE Eichler, D Haussler, T Wang, ED Jarvis, KH Miga, E Garrison, T Marschall, IM Hall, H Li, B Paten.
A draft human pangenome reference. Nature 2023. - G Hickey*, J Monlong*, J Ebler, AM Novak, JM Eizenga, Y Gao, Human Pangenome Reference Consortium, H Li, B Paten
Pangenome graph construction from genome alignments with Minigraph-Cactus. Nature Biotechnology 2023 - J Sirén*, J Monlong*, X Chang*, AM Novak*, JM Eizenga*, C Markello, JA Sibbesen, G Hickey, P Chang, A Carroll, N Gupta, S Gabriel, TW Blackwell, A Ratan, KD Taylor, SS Rich, JI Rotter, D Haussler, E Garrison, B Paten.
Pangenomics enables genotyping of known structural variants in 5202 diverse genomes. Science 2021. - G Hickey*, D Heller*, J Monlong*, JA Sibbesen, J Siren, J Eizenga, ET Dawson, E Garrison, AM Novak, B Paten
Genotyping structural variants in pangenome graphs using the vg toolkit. Genome Biology 2020. - J Monlong, P Cossette, C Meloche, G Rouleau, SL Girard, G Bourque.
- J Monlong*, SL Girard*, …, JL Michaud, G Rouleau, BA Minassian, G Bourque, P Cossette
- M Arseneault*, J Monlong*, …, M Lathrop, G Bourque, Y Riazalhosseini.
Loss of chromosome Y leads to down regulation of KDM5D and KDM6C epigenetic modifiers in clear cell renal cell carcinoma. Scientific Reports 2017. - J Monlong, M Calvo, PG Ferreira, R Guigó.
Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nature Communications 2014.
