Author

Marie V. Brasseur

Leibniz Institute for the Analysis of Biodiversity ChangeORCID 0000-0003-2044-4867Unclaimed 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:benchmarking· 2 Approach:benchmark study· 1 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1 Assay:long-read sequencing· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Organism:non-human primate· 1

Frequent Co-Authors

Florian LeeseiD· 2 shared Christoph MayeriD· 1 shared Christopher A. HempeliD· 1 shared Dan TulpaniD· 1 shared Dirk SteinkeiD· 1 shared Dominik BuchneriD· 1 shared Leoni MackiD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 16 mutual co-author links

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2 shared papersBioinformatics

Florian LeeseORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 1, 2025

Comparative Analysis of De Novo Assemblers and Quantification Software for RNA-sequencing Data in Non-Model Arthropods

Marie V. Brasseur, Florian Leese, Christoph Mayer

This study evaluates de novo transcriptome assemblers and quantification tools for non-model arthropods, finding that Trinity and rnaSPAdes outperform IDBA-tran, and Salmon provides faster and more sensitive quantification than RSEM.

DOI 10.1101/2025.08.01.668104

non-human primatebulk RNA-seq

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GenomicsFrontiers in MicrobiologyPeer-reviewedNov 24, 2023

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Christopher A. Hempel, Dominik Buchner, Leoni Mack…·Dirk Steinke

This study compares amplicon sequencing, metagenomics, and total RNA sequencing for predicting environmental stressors using machine learning, finding 16S sequencing outperforms others despite moderate performance.

13 citations

metagenomicslong-read sequencing

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