Author

Euphy Y. Wu

University of North Carolina at Chapel HillORCID 0000-0002-8343-7253Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Michael I. LoveiD· 3 shared Noor SinghiD· 2 shared Rob PatroiD· 2 shared Alejandro BarreraiD· 1 shared Andrew S. AlleniD· 1 shared Charles A. GersbachiD· 1 shared Cheryl L. Ackert‐BicknelliD· 1 shared Gary A. ChurchilliD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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3 shared papersBioinformatics

Michael I. LoveORCID

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3 papers

Recent·Most cited

Functional Annotation of the Major Histocompatibility Complex Locus

Lexi R. Bounds, Alejandro Barrera, Maria A. ter Weele, Siyan Liu, Euphy Y. Wu, Shengyu Li, Revathy Venukuttan, Ruhi Rai, W. Mu, Nahid Iglesias, Paola Giusti‐Rodríguez, Timothy E. Reddy, Yun Li, Raluca Gordân, Andrew S. Allen, , Charles A. Gersbach

This study identifies hundreds of new cis-regulatory element-gene connections in the MHC locus using CRISPR screens and single-cell transcriptomics, revealing cell type-specific regulatory mechanisms.

bioRxiv · Feb 3, 2026 · PreprintGenomics

Tree-based differential testing using inferentialuncertainty for RNA-Seq

Noor Singh, Euphy Y. Wu, Jason Fan, Michael I. Love, Rob Patro

The paper introduces mehenDi, a tree-based differential testing method for RNA-Seq that accounts for inferential uncertainty, improving detection of differentially expressed transcripts and inner nodes.

Genome Research · Oct 1, 2025Peer-reviewedBioinformatics

Detecting isoform-level allelic imbalance accounting for inferential uncertainty

Euphy Y. Wu, Noor Singh, Kwangbom Choi, Mohsen Zakeri, Matthew J. Vincent, Gary A. Churchill, Cheryl L. Ackert‐Bicknell, Rob Patro, Michael I. Love

The paper introduces SEESAW, a method to detect isoform-level allelic imbalance while accounting for inferential uncertainty, and evaluates it on simulated and real RNA-seq data.

bioRxiv · Aug 12, 2022 · PreprintBioinformatics