Author

Benjamin Vyshedskiy

University of Cambridge

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:DNA methylation· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Assay:long-read sequencing· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Dori C. WoodsiD· 2 shared Jonathan L. TillyiD· 2 shared Konstantin KhrapkoiD· 2 shared Konstantin Yu. PopadiniD· 2 shared Mark KhrapkoiD· 2 shared Melissa FrancoiD· 2 shared Zoë FleischmanniD· 2 shared Akiko ShimamuraiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersGenetics

Dori C. WoodsORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Positive germline selection of mtDNA: evidence from the oocyte.

Melissa Franco, Zoë Fleischmann, Sofia Annis, Auden Cote‐L’Heureux, Dylan Aidlen, Mark Khrapko, Benjamin Vyshedskiy, Mary K. Hartigan, Daniil Mirzoyan, Jacob Bandell, Konstantin Yu. Popadin, Dori C. Woods, Jonathan L. Tilly, Konstantin Khrapko

The paper analyzes mtDNA mutation dynamics in human oocytes and finds evidence of positive selection for noncoding and coding mutations, suggesting a shift from positive to purifying selection upon inheritance.

bioRxiv · Jul 14, 2026 · PreprintGenetics

Analyzing long-read CRISPR experiments with CRISPRLungo

Gue‐Ho Hwang, Benjamin Vyshedskiy, Timothy Barry, Jing Zeng, John P. Manis, Akiko Shimamura, Daniel E. Bauer, Luca Pinello

CRISPRLungo is a computational pipeline for analyzing long-read CRISPR experiments, improving accuracy in detecting structural variants and small indels through UMI-based error correction and benchmarking.

bioRxiv · Oct 21, 2025 · PreprintBioinformatics

A novel, wave-shaped profile of germline selection of pathogenic mtDNA mutations is discovered by bypassing a classical statistical bias.

Melissa Franco, Auden Cote-L’Heureux, Zoë Fleischmann, Zhibin Chen, Mark Khrapko, Benjamin Vyshedskiy, Maxim Braverman, Konstantin Yu. Popadin, Sarah J. Pickett, Dori C. Woods, Jonathan L. Tilly, Douglass M. Turnbull, Konstantin Khrapko

This paper reveals a wave-shaped germline selection profile of mtDNA mutations by correcting for regression to the mean bias, showing how mutations are filtered in the germline.

bioRxiv · Nov 21, 2023 · PreprintGenomics