Author

Dottie Yu

University of Southern CaliforniaORCID 0009-0004-5682-7362Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:bulk RNA-seq· 2 Organism:human· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:database / resource· 1 Organism:non-human primate· 1

Frequent Co-Authors

Grigore BoldireviD· 2 shared Ram AyyalaiD· 2 shared Serghei ManguliD· 2 shared Abdullah Al NahidiD· 1 shared Aditya Prasad SarkariD· 1 shared Alex ZelikovskyiD· 1 shared Anushka RajeshiD· 1 shared Anushka YadaviD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Grigore BoldirevORCID

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Author profile →

3 papers

Recent·Most cited

A rigorous benchmarking of alignment-based HLA callers for RNA-seq data

Dottie Yu, Ram Ayyala, Sarah Hany Sadek, Likhitha Chittampalli, Hafsa Farooq, Junghyun Jung, Abdullah Al Nahid, Grigore Boldirev, Mina Jung, Sung‐Min Park, Austin Nguyen, Alex Zelikovsky, Nicholas Mancuso, Jong Wha J. Joo, Reid F. Thompson, , Serghei Mangul

This study benchmarks 12 HLA callers on RNA-seq data across 682 samples to evaluate accuracy, computational cost, and performance differences by ancestry.

bioRxiv · May 22, 2023 · PreprintBioinformatics

A parametrized computational framework for description and design of genetic circuits of morphogenesis based on contact-dependent signaling and changes in cell-cell adhesion

Calvin K. Lam, Sajeev Saluja, George Courcoubetis, Dottie Yu, Christian Chung, Josquin Courte, Leonardo Morsut

ACS Synthetic Biology · Apr 1, 2022Peer-reviewedSynthetic Biology

The systematic assessment of completeness of public metadata accompanying omics studies in the Gene Expression Omnibus

Yu-Ning Huang, Pooja Vinod Jaiswal, Anushka Rajesh, Anushka Yadav, Dottie Yu, Fangyun Liu, Grace Scheg, Emma Shih, Grigore Boldirev, Irina Nakashidze, Aditya Prasad Sarkar, Jay Himanshu Mehta, Ke Wang, Khooshbu Kantibhai Patel, Mustafa Ali Baig Mirza, , Serghei Mangul

This study assesses metadata completeness in public omics studies, finding that most important phenotypes are often omitted, with only 11.5% of studies fully sharing all phenotypes.

bioRxiv · Nov 22, 2021 · PreprintBioinformatics