Author

John Bagnoli

MRIGlobalORCID 0000-0002-6124-9446Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:database / resource· 2 Assay:whole-genome sequencing· 2 Organism:bacteria (other)· 1 Approach:benchmark study· 1 Research Focus:gene regulatory network inference· 1 Research Focus:genome assembly· 1

Frequent Co-Authors

David A. YarmoshiD· 3 shared Alan ShteymaniD· 2 shared Jonathan L. JacobsiD· 2 shared Joseph A. RusselliD· 2 shared Phillip DavisiD· 2 shared Amanda E. Pierola· 1 shared Amy L. ReeseiD· 1 shared Briana BentoniD· 1 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

David A. YarmoshORCID

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Author profile →

3 papers

Recent·Most cited

Comparative Analysis and Data Provenance for 1,113 Bacterial Genome Assemblies

David A. Yarmosh, Juan G. Lopera, Nikhita P. Puthuveetil, Patrick Ford Combs, Amy L. Reese, Corina L. Tabron, Amanda E. Pierola, James Duncan, Samuel R. Greenfield, Robert Marlow, Stephen King, Marco A. Riojas, John Bagnoli, Briana Benton, Jonathan L. Jacobs

The paper compares 1,113 bacterial genome assemblies from authenticated sources with those in RefSeq, highlighting issues in metadata, traceability, and assembly quality.

mSphere · May 2, 2022Peer-reviewedGenomics

PanGIA: A Metagenomics Analytical Framework for RoutineBiosurveillance and Clinical Pathogen Detection

Po‐E Li, Joseph A. Russell, David A. Yarmosh, Alan Shteyman, Kyle Parker, Hillary Wood, Jacob R. Aspinwall, Richard A. Winegar, Karen W. Davenport, Chien‐Chi Lo, John Bagnoli, Phillip Davis, Jonathan L. Jacobs, Patrick Chain

PanGIA is a metagenomics analysis platform designed for pathogen detection and microbial community profiling, offering improved accuracy and usability for non-expert users.

bioRxiv · Apr 20, 2020 · PreprintBioinformatics

Vorpal: A Novel RNA Virus Feature-Extraction Algorithm Demonstrated Through Interpretable Genotype-to-Phenotype Linear Models

Phillip Davis, John Bagnoli, David A. Yarmosh, Alan Shteyman, Lance Presser, Sharon Altmann, Shelton S. Bradrick, Joseph A. Russell

Vorpal constructs interpretable linear models using clustered degenerate K-mers to predict binary phenotypes in RNA viruses, demonstrating its utility in three virus clades.

bioRxiv · Feb 28, 2020 · PreprintBioinformatics