Author

Arolyn Conwill

Massachusetts Institute of TechnologyORCID 0000-0002-6052-0345Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Alyssa H. MitchelliD· 1 shared Evan B. QuiD· 1 shared Felix M. KeyiD· 1 shared Herui LiaoiD· 1 shared Ian C. Light-MakaiD· 1 shared Jacob S. BakeriD· 1 shared Lilly R. BartschiD· 1 shared Manoshi Sen DattaiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperBioinformatics

Alyssa H. MitchellORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV

Herui Liao, Arolyn Conwill, Ian C. Light-Maka, Martin Fenk, Alyssa H. Mitchell, Evan B. Qu, Paul A. Torrillo, Jacob S. Baker, Lilly R. Bartsch, Felix M. Key, Tami D. Lieberman

AccuSNV is a deep learning tool for high-accuracy bacterial SNV calling that outperforms existing methods in simulated and real datasets.

Genome Research · Sep 1, 2026Peer-reviewedBioinformatics

Rapid, inexpensive measurement of synthetic bacterial community composition by Sanger sequencing

Nathan Cermak, Manoshi Sen Datta, Arolyn Conwill

The paper introduces a method using Sanger sequencing to rapidly and cheaply measure synthetic bacterial community composition, achieving accurate results in under a day for ~$5 per sample.

iScience · Feb 14, 2020Peer-reviewedBioinformatics