Author

Annelies Van Rie

University of AntwerpORCID 0000-0001-7666-3263Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 3 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Research Focus:drug response / drug discovery· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Lennert VerboveniD· 2 shared Robin M. WarreniD· 2 shared Tim H. HeupinkiD· 2 shared Vincent RennieiD· 2 shared Abhinav SharmaiD· 1 shared Emmanuel RivièreiD· 1 shared M. FuertesiD· 1 shared Mahdi SafarpouriD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Lennert VerbovenORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Feature-based in-silico model to predict the Mycobacterium tuberculosis bedaquiline phenotype associated with Rv0678 variants

Wilma Ursula Quispe-Rojas, M. Fuertes, Vincent Rennie, Emmanuel Rivière, Mahdi Safarpour, Annelies Van Rie

The paper develops a machine learning model to predict bedaquiline resistance in Mycobacterium tuberculosis based on Rv0678 variants, achieving high accuracy in internal validation.

bioRxiv · Feb 14, 2026 · PreprintBioinformatics

The MAGMA pipeline for comprehensive genomic analyses of clinical Mycobacterium tuberculosis samples

Tim H. Heupink, Lennert Verboven, Abhinav Sharma, Vincent Rennie, Miguel de Diego Fuertes, Robin M. Warren, Annelies Van Rie

The MAGMA pipeline improves variant calling and drug resistance analysis for Mycobacterium tuberculosis samples with low coverage and high contamination.

medRxiv · Oct 4, 2023 · PreprintGenetic & Genomic Medicine

Comprehensive and accurate genetic variant identification from contaminated and low coverage Mycobacterium tuberculosis whole genome sequencing data.

Tim H. Heupink, Lennert Verboven, Robin M. Warren, Annelies Van Rie

This paper introduces XBS, a bioinformatics pipeline that improves variant detection in contaminated and low coverage Mycobacterium tuberculosis sequencing data, outperforming existing methods in complex genomic regions and low-depth samples.

Microbial Genomics · Nov 18, 2021Peer-reviewedGenomics