New Results
Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
Gemma May, Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, Joel McManus
doi: https://doi.org/10.1101/2021.04.16.440232
Gemma May
1Department of Biological Sciences
Christina Akirtava
1Department of Biological Sciences
Matthew Agar-Johnson
1Department of Biological Sciences
Jelena Micic
1Department of Biological Sciences
John Woolford
1Department of Biological Sciences
Joel McManus
1Department of Biological Sciences
2Computational Biology Department Carnegie Mellon University, Pittsburgh PA, USA
Posted April 17, 2021.
Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
Gemma May, Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, Joel McManus
bioRxiv 2021.04.16.440232; doi: https://doi.org/10.1101/2021.04.16.440232
Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
Gemma May, Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, Joel McManus
bioRxiv 2021.04.16.440232; doi: https://doi.org/10.1101/2021.04.16.440232
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