Author

Santino Faack

University of FreiburgORCID 0009-0004-0382-2023Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:metagenomics· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Bérénice BatutiD· 2 shared Martín BeracocheaiD· 2 shared Paul F. ZierepiD· 2 shared Santiago SancheziD· 2 shared Björn Andreas GrüningiD· 1 shared Bruno FossoiD· 1 shared Giuseppe DefazioiD· 1 shared Hottmann AlexandraiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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2 shared papersBioinformatics

Bérénice BatutORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 5, 2026

FAIRyMAGs - a series of FAIR Galaxy workflows for the generation of metagenome assembled genomes

Paul F. Zierep, Mina Ansari, Patrick Bühler…·Bérénice Batut

FAIRyMAGs provides a modular, FAIR-compliant Galaxy pipeline for generating and analyzing metagenome-assembled genomes, improving accessibility and reproducibility in genome-resolved metagenomics.

DOI 10.64898/2026.07.31.741430

metagenomicsexperimental assay

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MicrobiologybioRxivPreprintMay 13, 2026

Machine learning-based prediction of memory requirements for metagenomic assembly in high-performance computing environments

Santiago Sanchez, Santino Faack, Martín Beracochea…·Paul F. Zierep

This study develops a machine learning framework to predict memory requirements for metagenomic assembly, improving efficiency in high-performance computing environments.

DOI 10.64898/2026.05.12.724571

metagenomicsmachine learning (classical)

𝕏 PostBluesky