Author

Vladimír Janout

Bellvitge University HospitalORCID 0000-0003-1594-3901Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:GWAS / association mapping· 2 Organism:human· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Lenka ForetováiD· 3 shared Andreas J. ForstneriD· 2 shared Angela FavaroiD· 2 shared Anke HinneyiD· 2 shared Antonio JuliàiD· 2 shared Beate Herpertz‐DahlmanniD· 2 shared Christian Dina CiD· 2 shared Christopher HübeliD· 2 shared

Activity by Year

2020-2020 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersGenomics

Lenka ForetováORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

GenomicsNature GeneticsPeer-reviewedApr 27, 2020

Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Novel Insights Into the Etiology of Parkinson's Disease

Julien Bryois, Nathan Skene, Thomas Folkmann Hansen…·Jaakko A. Kaprio

This study identifies cell types associated with brain complex traits using GWAS and single-cell transcriptomics, revealing novel roles for oligodendrocytes in Parkinson's disease.

368 citationstop 1%

humanbulk RNA-seq

𝕏 PostBluesky

BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedApr 20, 2020

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

Tiffany Myriam Delhomme, Patrice Hodonou Avogbe, Aurélie AG Gabriel…·Matthieu Foll

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

14 citations

whole-genome sequencingmachine learning (classical)

𝕏 PostBluesky

GeneticsAddiction BiologyPeer-reviewedFeb 16, 2020

Shared Genetic Risk between Eating Disorder- and Substance-Use-Related Phenotypes: Evidence from Genome-Wide Association Studies

Melissa A. Munn‐Chernoff, Emma C. Johnson, Yi‐Ling Chou…·Maria C. La Via

The paper estimates genetic correlations between eating disorder and substance-use-related phenotypes using GWAS data, finding both positive and negative associations.

76 citationstop 4%

humanGWAS summary statistics

𝕏 PostBluesky