Author

Lucas Brambrink

Google (United States)

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· 7

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Research Focus:variant calling· 7 Assay:whole-genome sequencing· 7 Organism:human· 6 Approach:deep learning· 4 Assay:long-read sequencing· 2 Approach:statistical model· 2 Assay:whole-exome sequencing· 2 Approach:experimental assay· 1

Frequent Co-Authors

Alexey KolesnikoviD· 7 shared Andrew W. CarrolliD· 7 shared Kishwar ShafiniD· 7 shared Pi-Chuan ChangiD· 7 shared Daniel E. CookiD· 6 shared Benedict PateniD· 4 shared Maria NattestadiD· 4 shared Brandy McNulty· 2 shared

Activity by Year

2021-2025 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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7 shared papersBioinformatics

Alexey KolesnikovORCID

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7 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

Accurate human genome analysis with Element Avidity sequencing

Andrew W. Carroll, Alexey Kolesnikov, Daniel E. Cook, Lucas Brambrink, Kelly N. Wiseman, Sophie M. Billings, Semyon Kruglyak, Bryan R. Lajoie, Junhua Zhao, Shawn Levy, Cory Y. McLean, Kishwar Shafin, Maria Nattestad, Pi-Chuan Chang

This study compares Element Avidity sequencing to Illumina sequencing, finding higher accuracy in mapping and variant calling, especially at lower coverages and in challenging genomic regions.

BMC Bioinformatics · Jul 25, 2025Peer-reviewedBioinformatics

Highly accurate assembly polishing with DeepPolisher

Mira Mastoras, Mobin Asri, Lucas Brambrink, Prajna Hebbar, Alexey Kolesnikov, Daniel E. Cook, Maria Nattestad, Julian Lucas, Sohui T. Won, Pi-Chuan Chang, Andrew W. Carroll, Benedict Paten, Kishwar Shafin

DeepPolisher improves genome assembly accuracy by reducing errors using a transformer model and phasing method, achieving over 70% indel error reduction.

Genome Research · Jul 1, 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

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

Camille A. Daniels, Adetola Abdulkadir, Megan H. Cleveland, Jennifer McDaniel, David Jáspez, Luis A. Rubio‐Rodríguez, Adrián Muñoz‐Barrera, Jose Miguel Lorenzo-Salazar, Carlos Flores, Byunggil Yoo, Sayed Mohammad Ebrahim Sahraeian, Yina Wang, Massimiliano Rossi, Arun Visvanath, Lisa Murray, , Nathan D. Olson

The paper develops a benchmark for detecting low-frequency mosaic variants in the HG002 reference material using high-coverage sequencing data and validates its utility across multiple technologies.

bioRxiv · Dec 2, 2024 · PreprintBioinformatics

Local read haplotagging enables accurate long-read small variant calling

Alexey Kolesnikov, Daniel E. Cook, Maria Nattestad, Lucas Brambrink, Brandy McNulty, John E. Gorzynski, Sneha D. Goenka, Euan Angus Ashley, Miten Jain, Karen H. Miga, Benedict Paten, Pi-Chuan Chang, Andrew W. Carroll, Kishwar Shafin

This paper introduces a local haplotype approximation method to improve variant calling accuracy across long-read sequencing platforms without increasing computational overhead.

Nature Communications · Jul 13, 2024Peer-reviewedBioinformatics

DeepTrio: Variant Calling in Families Using Deep Learning

Lucas Brambrink, Alexey Kolesnikov, Sidharth Goel, Maria Nattestad, Taedong Yun, Gunjan Baid, Howard Yang, Cory Y. McLean, Kishwar Shafin, Pi-Chuan Chang, Andrew W. Carroll

DeepTrio improves variant calling in family trios using deep learning, achieving higher accuracy than DeepVariant, especially at lower coverages.

bioRxiv · Apr 5, 2021 · PreprintBioinformatics