Author

Jan Claas Behrends

University of FreiburgORCID 0000-0001-9686-7263Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Bioengineering· 1 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:long-read sequencing· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:graph neural network· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Tobias EnssleniD· 3 shared Maria FytaiD· 2 shared Mayukh KansariiD· 2 shared Andreas W. SchreiberiD· 1 shared Atefeh Akbari· 1 shared Carolin HenseliD· 1 shared Carolin KleberiD· 1 shared Chih‐Yuan LiniD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBiochemistry

Tobias EnsslenORCID

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3 papers

Recent·Most cited

Sample preparation for direct nanopore based protein identification via tryptic peptide recognition

Carolin Hensel, Carolin Kleber, Ida Suppanz, Atefeh Akbari, Sebastian Gutsch, Tobias Hutzenlaub, Tobias Ensslen, Jan Claas Behrends, Felix von Stetten, Gerhard Mittler, Jan-Niklas Klatt, Andreas W. Schreiber

This paper introduces a sample preparation workflow (SP3) that enables nanopore-based protein identification by decoupling proteolysis from interfering components, demonstrating its effectiveness with tryptic digests of myelin basic protein.

bioRxiv · Oct 23, 2026 · PreprintBiochemistry

Nanopore event detection in a simple and adaptive way

Peijia Wei, Mayukh Kansari, Michael Mierzejewski, Tobias Ensslen, Chih‐Yuan Lin, Kyril Kavetsky, Peter D. Jones, Jan Claas Behrends, Marija Drndić, Maria Fyta

This paper introduces a simple, fast, and adaptable cluster-based event detection algorithm for nanopore signals, showing improved efficiency and reduced noise in biological nanopore data.

bioRxiv · May 11, 2026 · PreprintBioinformatics

Graph-based learning and read-out of nanopore translocation event signals

Mayukh Kansari, Tobias Ensslen, Jan Claas Behrends, Maria Fyta

This paper develops a graph-based framework for label-free detection of nanopore translocation events, showing it can predict event morphology across a range of voltages.

bioRxiv · Apr 30, 2026 · PreprintBioengineering