TOXIFY: a deep learning approach to classify animal venom proteins
A peer-reviewed article of this Preprint also exists.
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Abstract
In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins.
Availability: https://www.github.com/tijeco/toxify
Cite this as
2019. TOXIFY: a deep learning approach to classify animal venom proteins. PeerJ Preprints 7:e27498v1 https://doi.org/10.7287/peerj.preprints.27498v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ for review.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
T Jeffrey Cole conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Michael S Brewer conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
Funding
This work was supported by a National Science Foundation Graduate Research Fellowship to T. Jeffrey Cole and the East Carolina University Department of Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
