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Vorpal: A novel RNA virus feature-extraction algorithm demonstrated through interpretable genotype-to-phenotype linear models

Phillip Davis, John Bagnoli, David Yarmosh, Alan Shteyman, Lance Presser, Sharon Altmann, Shelton Bradrick, Joseph A. Russell
doi: https://doi.org/10.1101/2020.02.28.969782
Phillip Davis
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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  • For correspondence: pdavis@mriglobal.org
John Bagnoli
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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David Yarmosh
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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Alan Shteyman
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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Lance Presser
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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Sharon Altmann
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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Shelton Bradrick
2MRIGlobal – 425 Volker Blvd., Kansas City, MO, USA
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Joseph A. Russell
1MRIGlobal – 65 West Watkins Mill Rd., Gaithersburg, MD, USA
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SUMMARY

In the analysis of genomic sequence data, so-called “alignment free” approaches are often selected for their relative speed compared to alignment-based approaches, especially in the application of distance comparisons and taxonomic classification1,2,3,4. These methods are typically reliant on excising K-length substrings of the input sequence, called K-mers5. In the context of machine learning, K-mer based feature vectors have been used in applications ranging from amplicon sequencing classification to predictive modeling for antimicrobial resistance genes6,7,8. This can be seen as an analogy of the “bag-of-words” model successfully employed in natural language processing and computer vision for document and image classification9,10. Feature extraction techniques from natural language processing have previously been analogized to genomics data11; however, the “bag-of-words” approach is brittle in the RNA virus space due to the high intersequence variance and the exact matching requirement of K-mers. To reconcile the simplicity of “bag-of-words” methods with the complications presented by the intrinsic variance of RNA virus space, a method to resolve the fragility of extracted K-mers in a way that faithfully reflects an underlying biological phenomenon was devised. Our algorithm, Vorpal, allows the construction of interpretable linear models with clustered, representative ‘degenerate’ K-mers as the input vector and, through regularization, sparse predictors of binary phenotypes as the output. Here, we demonstrate the utility of Vorpal by identifying nucleotide-level genomic motif predictors for binary phenotypes in three separate RNA virus clades; human pathogen vs. non-human pathogen in Orthocoronavirinae, hemorrhagic fever causing vs. non-hemorrhagic fever causing in Ebolavirus, and human-host vs. non-human host in Influenza A. The capacity of this approach for in silico identification of hypotheses which can be validated by direct experimentation, as well as identification of genomic targets for preemptive biosurveillance of emerging viruses, is discussed. The code is available for download at https://github.com/mriglobal/vorpal.

Footnotes

  • https://github.com/mriglobal/vorpal

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 02, 2020.
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Vorpal: A novel RNA virus feature-extraction algorithm demonstrated through interpretable genotype-to-phenotype linear models
Phillip Davis, John Bagnoli, David Yarmosh, Alan Shteyman, Lance Presser, Sharon Altmann, Shelton Bradrick, Joseph A. Russell
bioRxiv 2020.02.28.969782; doi: https://doi.org/10.1101/2020.02.28.969782
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Vorpal: A novel RNA virus feature-extraction algorithm demonstrated through interpretable genotype-to-phenotype linear models
Phillip Davis, John Bagnoli, David Yarmosh, Alan Shteyman, Lance Presser, Sharon Altmann, Shelton Bradrick, Joseph A. Russell
bioRxiv 2020.02.28.969782; doi: https://doi.org/10.1101/2020.02.28.969782

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