Author

Maxime Vallée

Centre international de recherche sur le cancerORCID 0000-0002-3616-9508Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:variant calling· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Amélie Chabrier· 1 shared Aurélie AG GabrieliD· 1 shared Béatrice GrangeiD· 1 shared Behnoush Abedi‐ArdekaniiD· 1 shared Catherine VoegeleiD· 1 shared Claire BardeliD· 1 shared David Georgievich ZaridzeiD· 1 shared Elisabeth M. BrambillaiD· 1 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

Amélie Chabrier

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2 papers

Recent·Most cited

Performances of targeted RNA-sequencing for the analysis of fusion transcripts, gene mutation and expression in haematological malignancies.

Sandrine Hayette, Béatrice Grange, Maxime Vallée, Claire Bardel, Sarah Huet, Isabelle Mosnier, Kaddour Chabane, Thomas Simonet, Marie Balsat, Maël Heiblig, Isabelle Tigaud, Franck Emmanuel Nicolini, Sylvain Mareschal, Gilles Salles, Pierre Sujobert

This study evaluates targeted RNA sequencing for detecting fusion transcripts, mutations, and expression in hematological malignancies, finding it effective for diagnosing leukemia subtypes and identifying most DNA mutations at the mRNA level.

bioRxiv · Sep 7, 2020 · PreprintGenomics

Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

Tiffany Myriam Delhomme, Patrice Hodonou Avogbe, Aurélie AG Gabriel, Nicolas Alcala, Noémie Leblay, Catherine Voegele, Maxime Vallée, Priscilia Chopard, Amélie Chabrier, Behnoush Abedi‐Ardekani, Valérie Gaborieau, Ivana Holcátová, Vladimír Janout, Lenka Foretová, Sasa Milosavljevic, , Matthieu Foll

Needlestack is a variant caller that improves sensitivity for low abundance mutations by learning sequencing error rates from multi-sample data.

NAR Genomics and Bioinformatics · Apr 20, 2020Peer-reviewedBioinformatics