Author

Juan Carlos Mier

Google (United States)ORCID 0009-0002-5189-5337Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Assay:long-read sequencing· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Alexey KolesnikoviD· 2 shared Andrew W. CarrolliD· 2 shared Benedict PateniD· 2 shared Daniel E. CookiD· 2 shared Kishwar ShafiniD· 2 shared Lucas Brambrink· 2 shared Pi-Chuan ChangiD· 2 shared Adam M. NovakiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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2 shared papersBioinformatics

Alexey KolesnikovORCID

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2 papers

Recent·Most cited

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

Jimin Park, Daniel E. Cook, Pi-Chuan Chang, Alexey Kolesnikov, Lucas Brambrink, Juan Carlos Mier, Joshua M.V. Gardner, Brandy McNulty, Samuel Sacco, Ayşe Gökçe Keşküş, Asher Bryant, Tanveer Ahmad, Jyoti Shetty, Yongmei Zhao, Bao Quoc Tran, , Kishwar Shafin

DeepSomatic is a deep learning method for detecting somatic variants from short-read and long-read sequencing data, outperforming existing callers, especially for indels.

Nature Biotechnology · Oct 16, 2025Peer-reviewedBioinformatics

Pangenome-aware DeepVariant

Mobin Asri, Pi-Chuan Chang, Juan Carlos Mier, Jouni Sirén, Parsa Eskandar, Alexey Kolesnikov, Daniel E. Cook, Lucas Brambrink, Glenn Hickey, Adam M. Novak, Elizabeth H. Dorfman, Dale R. Webster, Andrew W. Carroll, Benedict Paten, Kishwar Shafin

This paper introduces pangenome-aware DeepVariant, a variant caller that improves accuracy by using a pangenome reference, outperforming linear-reference-based DeepVariant by up to 25.5%.

bioRxiv · Jun 5, 2025 · PreprintBioinformatics