Author

Rachel Byron

Adaptive Biotechnologies (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· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Organism:mouse· 1

Frequent Co-Authors

André FrankeiD· 1 shared Brittney Robinett· 1 shared Bryan Howie· 1 shared Haiyin Chen‐HarrisiD· 1 shared Harlan S. RobinsiD· 1 shared Hesham ElAbdiD· 1 shared Jessica M. Halow· 1 shared Joe Sulc· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

André FrankeORCID

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2 papers

Recent·Most cited

Seq2MAIT: A Novel Deep Learning Framework for Identifying Mucosal Associated Invariant T (MAIT) Cells

Hesham ElAbd, Rachel Byron, Steven Woodhouse, Brittney Robinett, Joe Sulc, André Franke, Mitchell W. Pesesky, Wenyu Zhou, Haiyin Chen‐Harris, Bryan Howie, Ruth T. Taniguchi, Harlan S. Robins

Seq2MAIT identifies MAIT cells from bulk TCR-sequencing data using a transformer-based model, achieving AUROC >0.80 across datasets.

bioRxiv · Mar 12, 2024 · PreprintBioinformatics

Tissue context determines the penetrance of regulatory DNA variation

Jessica M. Halow, Rachel Byron, Megan S. Hogan, Raquel Ordóñez, Mark T. Groudine, M. A. Bender, John A. Stamatoyannopoulos, Matthew T. Maurano

The paper quantifies how tissue context influences the effect of regulatory DNA variants on transcription factor activity using allelic accessibility maps in mice.

Nature Communications · May 14, 2021Peer-reviewedGenomics