Author

Martina Maria Braun

Barcelona Institute of Science and TechnologyORCID 0009-0004-1274-6317Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Assay:DNA methylation· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1

Frequent Co-Authors

Michael SchereriD· 2 shared Agostina BianchiiD· 1 shared Alejo Rodriguez-FraticelliiD· 1 shared Ana Martín-VillalbaiD· 1 shared Ansuman T. SatpathyiD· 1 shared Caleb A. LareauiD· 1 shared Chelsea Szu‐TuiD· 1 shared Christoph PlassiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersGenomics

Michael SchererORCID

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3 papers

Recent·Most cited

GenomicsNaturePeer-reviewedMay 21, 2025

Somatic epimutations enable single-cell lineage tracing in native hematopoiesis across the murine and human lifespan

Michael Scherer, Indranil Singh, Martina Maria Braun…·Lars Velten

This paper introduces EPI-clone, a method using DNA methylation to trace clonal lineages in hematopoiesis without genetic engineering, showing clonal complexity declines with age in mice and expands in humans.

67 citationstop 1%

mousehuman

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BioinformaticsGenome biologyPeer-reviewedDec 2, 2024

EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data

Katherine Kelly, Michael Scherer, Martina Maria Braun…·Christoph Plass

The paper introduces epiCHAOS, a metric to quantify cell-to-cell epigenomic heterogeneity in single-cell data and shows its utility in assessing stemness and plasticity.

10 citations

scATAC-seqscRNA-seq

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BioinformaticsNature MethodsPeer-reviewedJul 31, 2024

Analyzing single-cell bisulfite sequencing data with MethSCAn

Lukas P. M. Kremer, Martina Maria Braun, Svetlana Ovchinnikova…·Simon Anders

The paper introduces MethSCAn, a tool for analyzing scBS data that improves methylation quantification and DMR detection, enhancing cell type discrimination with fewer cells.

30 citationstop 5%

DNA methylationstatistical model

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