Author

Ryokei Tanaka

Cornell UniversityORCID 0000-0002-3479-377XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:gene regulatory network inference· 2 Organism:zebrafish· 2 Research Focus:differential expression· 1 Assay:GWAS summary statistics· 1 Organism:plant (other)· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

C. Robin BuelliD· 3 shared Dean DellaPennaiD· 3 shared Di WuiD· 3 shared John P. HamiltoniD· 3 shared Joshua C. WoodiD· 3 shared Michael A. GoreiD· 3 shared Brieanne VaillancourtiD· 2 shared Gregory R. SchoenbaumiD· 2 shared

Activity by Year

2022-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersGenetics

C. Robin BuellORCID

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3 papers

Recent·Most cited

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

Ryokei Tanaka, Di Wu, Xiaowei Li, Laura E. Tibbs‐Cortes, Joshua C. Wood, Maria Magallanes‐Lundback, Nolan Bornowski, John P. Hamilton, Brieanne Vaillancourt, Xianran Li, Nicholas Deason, Gregory R. Schoenbaum, C. Robin Buell, Dean DellaPenna, Jianming Yu, Michael A. Gore

This study evaluates prediction models for tocochromanol content in maize grain using genomic and transcriptomic data, finding that incorporating biological knowledge improves predictive ability.

The Plant Genome · Nov 2, 2022Peer-reviewedGenetics

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

Di Wu, Xiaowei Li, Ryokei Tanaka, Joshua C. Wood, Laura E. Tibbs‐Cortes, Maria Magallanes‐Lundback, Nolan Bornowski, John P. Hamilton, Brieanne Vaillancourt, Christine Diepenbrock, Xianran Li, Nicholas Deason, Gregory R. Schoenbaum, Jianming Yu, C. Robin Buell, , Michael A. Gore

The paper combines GWAS and TWAS to identify 13 candidate causal genes for tocochromanol levels in maize grain, including five previously unassociated genes.

Genetics · Jun 6, 2022Peer-reviewedGenetics

Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels

Jenna L. Hershberger, Ryokei Tanaka, Joshua C. Wood, Nicholas S. Kaczmar, Di Wu, John P. Hamilton, Dean DellaPenna, C. Robin Buell, Michael A. Gore

This study uses transcriptome-wide association and prediction to identify genes related to carotenoid and tocochromanol levels in sweet corn and shows transcriptomic data improve predictive models.

The Plant Genome · Mar 8, 2022Peer-reviewedGenomics