Author

Byron J. Smith

Gladstone InstitutesORCID 0000-0002-0182-404XUnclaimed ORCID profile · follow to get notified of new preprints

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Byron J. Smith1 paper

Biology Disciplines

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Microbiology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:clustering· 2 Research Focus:gene regulatory network inference· 2 Assay:metagenomics· 2 Research Focus:benchmarking· 1 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Katherine S. PollardiD· 3 shared Chunyu ZhaoiD· 2 shared Veronika B. DubinkinaiD· 2 shared Adam R. AbateiD· 1 shared Cindy PinoiD· 1 shared Eran SegaliD· 1 shared Jacqueline Moltzau AndersoniD· 1 shared Liron ZahaviiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersMicrobiology

Katherine S. PollardORCID

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3 papers

Recent·Most cited

Linkage of nucleotide and functional diversity varies across gut bacteria.

Veronika B. Dubinkina, Byron J. Smith, Chunyu Zhao, Cindy Pino, Katherine S. Pollard

The paper examines genomic diversity in gut bacteria, finding that accessory genes show faster linkage decay compared to core SNPs, with mobile genetic elements showing minimal linkage to core SNPs.

Proceedings of the National Academy of Sciences · May 6, 2026Peer-reviewedMicrobiology

Accurate estimation of intraspecific microbial gene content variation in metagenomic data with MIDAS v3 and StrainPGC

Byron J. Smith, Chunyu Zhao, Veronika B. Dubinkina, Xiaofan Jin, Liron Zahavi, Saar Shoer, Jacqueline Moltzau Anderson, Eran Segal, Katherine S. Pollard

The paper introduces StrainPGC and MIDAS v3 to accurately estimate intraspecific microbial gene content variation in metagenomic data, outperforming existing methods and revealing functional diversity in gut microbiomes.

bioRxiv · Apr 10, 2024Microbiology

Scalable microbial strain inference in metagenomic data using StrainFacts

Byron J. Smith, Xiangpeng Li, Zhou Jason Shi, Adam R. Abate, Katherine S. Pollard

StrainFacts enables scalable strain inference in metagenomic data, showing comparable accuracy to intensive tools and revealing strain diversity patterns in human stool samples.

Frontiers in Bioinformatics · May 16, 2022Peer-reviewedBioinformatics