Author

Shota Nakade

Hiroshima UniversityORCID 0000-0003-4721-9408Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Akinori AwazuiD· 1 shared Kazuki NakamaeiD· 1 shared Mitsumasa Takenaga· 1 shared Naoaki SakamotoiD· 1 shared Takashi YamamotoiD· 1 shared Tetsushi SakumaiD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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1 shared paperBioinformatics

Akinori AwazuORCID

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1 paper

Recent·Most cited

BioinformaticsbioRxivPreprintJun 27, 2022

Detailed profiling with MaChIAto reveals various genomic and epigenomic features affecting the efficacy of knock-out, short homology-based knock-in and Prime Editing

Kazuki Nakamae, Mitsumasa Takenaga, Shota Nakade…·Tetsushi Sakuma

MaChIAto profiles genomic and epigenomic features affecting CRISPR edit efficiency and accuracy, revealing context-dependent factors influencing knock-out, knock-in, and Prime Editing outcomes.

1 citation

whole-genome sequencingstatistical model

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