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Unsupervised analysis of multi-experiment transcriptomic patterns with SegRNA identifies unannotated transcripts

View ORCID ProfileMickaël Mendez, FANTOM Consortium Main Contributors, View ORCID ProfileMichelle S. Scott, View ORCID ProfileMichael M. Hoffman
doi: https://doi.org/10.1101/2020.07.28.225193
Mickaël Mendez
1Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
2Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Michelle S. Scott
3Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
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Michael M. Hoffman
1Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
2Department of Computer Science, University of Toronto, Toronto, ON, Canada
4Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
5Vector Institute, Toronto, ON, Canada
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  • For correspondence: michael.hoffman@utoronto.ca
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Posted November 22, 2021.
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Unsupervised analysis of multi-experiment transcriptomic patterns with SegRNA identifies unannotated transcripts
Mickaël Mendez, FANTOM Consortium Main Contributors, Michelle S. Scott, Michael M. Hoffman
bioRxiv 2020.07.28.225193; doi: https://doi.org/10.1101/2020.07.28.225193
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Unsupervised analysis of multi-experiment transcriptomic patterns with SegRNA identifies unannotated transcripts
Mickaël Mendez, FANTOM Consortium Main Contributors, Michelle S. Scott, Michael M. Hoffman
bioRxiv 2020.07.28.225193; doi: https://doi.org/10.1101/2020.07.28.225193

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