Author

Zane Kliesmete

Ludwig-Maximilians-Universität MünchenORCID 0000-0001-7017-6616Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Evolutionary Biology· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:clustering· 2 Research Focus:differential expression· 2 Organism:mouse· 2 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Organism:chicken / avian· 1

Frequent Co-Authors

Ines HellmanniD· 3 shared Beate ViethiD· 2 shared Philipp JansseniD· 2 shared Wolfgang EnardiD· 2 shared Aleksandar JanjiciD· 1 shared Anita TérmegiD· 1 shared Christiane SchreiweisiD· 1 shared Cristin AubiniD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersEvolutionary Biology

Ines HellmannORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Evolutionary BiologybioRxivPreprintNov 18, 2025

CroCoNet: a novel framework for cross-species network analysis reveals POU5F1 (OCT4) rewiring

Anita Térmeg, Vladyslav Storozhuk, Zane Kliesmete…·Ines Hellmann

CroCoNet quantitatively compares gene regulatory networks across species, identifying conserved and diverged modules, including POU5F1's rewired network in great apes.

DOI 10.1101/2025.11.18.689002

humanchicken / avian

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NeurosciencebioRxivPreprintJan 3, 2023

Expression profiling of the learning striatum

Eliana Lopes Rio da Silva Lousada, Zane Kliesmete, Aleksandar Janjic…·Christiane Schreiweis

The paper profiles the transcriptome of striatal subregions in mice during learning stages to identify molecular changes and provides a web tool for exploring the data.

1 citation

mousebulk RNA-seq

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BioinformaticsbioRxivPreprintNov 16, 2022

The effect of background noise and its removal on the analysis of single-cell expression data

Philipp Janssen, Zane Kliesmete, Beate Vieth…·Ines Hellmann

This study evaluates background noise in single-cell RNA-seq data and assesses methods for its removal, finding that CellBender performs best for marker gene detection.

3 citations

mousescRNA-seq

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