Author

Logan Persyn

The University of Texas at AustinORCID 0009-0004-2286-7657Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 4 Organism:mouse· 4 Assay:Ribo-seq· 4 Organism:human· 3 Approach:statistical model· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Can CenikiD· 4 shared Dinghai ZhengiD· 2 shared Jun Wang· 2 shared Vighnesh GhatpandeiD· 2 shared Vikram AgarwaliD· 2 shared Yue LiuiD· 2 shared Aden Y. Le· 1 shared Dayea Park· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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4 shared papersBioinformatics

Can CenikORCID

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4 papers

Recent·Most cited

Ribo-ITP expands the translatome of limited input samples

Vighnesh Ghatpande, Uma Paul, Logan Persyn, Yifan Tian, MacKenzie Allen Howard, Can Cenik

Ribo-ITP identifies thousands of translons from low-input samples, revealing their regulatory roles and impact on cell growth.

Nature Communications · Jul 31, 2026Peer-reviewedGenomics

Translational buffering tunes gene expression in mice and humans

Shilpa Rao, Aden Y. Le, Logan Persyn, Can Cenik

The paper analyzes translational buffering across human and mouse datasets, finding conserved patterns and highlighting the role of cellular context in gene expression regulation.

Genome biology · Feb 26, 2026Peer-reviewedBioinformatics

Predicting the translation efficiency of messenger RNA in mammalian cells

Dinghai Zheng, Logan Persyn, Jun Wang, Yue Liu, Fernando Ulloa‐Montoya, Can Cenik, Vikram Agarwal

The paper predicts translation efficiency (TE) of mRNA in mammalian cells using a deep learning model trained on ribosomal profiling data across multiple cell types.

Nature Biotechnology · Jul 25, 2025Peer-reviewedBioinformatics

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Yue Liu, Shilpa J Rao, Ian Hoskins, Michael M. Geng, Qiuxia Zhao, Jonathan Chacko, Vighnesh Ghatpande, Kangsheng Qi, Logan Persyn, Jun Wang, Dinghai Zheng, Yochen Zhong, Dayea Park, Elif Sarinay Cenik, Vikram Agarwal, , Can Cenik

The paper analyzes ribosome profiling data to show translation efficiency covariation (TEC) is conserved across human and mouse cells, revealing gene functions not evident from RNA or protein co-expression.

Nature Biotechnology · Jul 25, 2025Peer-reviewedBioinformatics