Incremental and Semi-Supervised Learning of 16S-rRNA Genes for Taxonomic Classification

Emrecan Ozdogan, Norman C. Sabin, Thomas Gracie, Steven Portley, Mali Halac, Thomas Coard, William Trimble, Bahrad Sokhansanj, Gail Rosen, Robi Polikar

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Genome sequencing generates large volumes of data and hence requires increasingly higher computational resources. The growing data problem is even more acute in metagenomics applications, where data from an environmental sample include many organisms instead of just one for the common single organism sequencing. Traditional taxonomic classification and clustering approaches and platforms - while designed to be computationally efficient - are not capable of incrementally updating a previously trained system when new data arrive, which then requires complete re-training with the augmented (old plus new) data. Such complete retraining is inefficient and leads to poor utilization of computational resources. An ability to update a classification system with only new data offers a much lower run-time as new data are presented, and does not require the approach to be re-trained on the entire previous dataset. In this paper, we propose Incremental VSEARCH (I-VSEARCH) and its semi-supervised version for taxonomic classification, as well as a threshold independent VSEARCH (TI-VSEARCH) as wrappers around VSEARCH, a well-established (unsupervised) clustering algorithm for metagenomics. We show - on a 16S rRNA gene dataset - that I-VSEARCH, running incrementally only on the new batches of data that become available over time, does not lose any accuracy over VSEARCH that runs on the full data, while providing attractive computational benefits.

    Original languageEnglish (US)
    Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728190488
    DOIs
    StatePublished - 2021
    Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
    Duration: Dec 5 2021Dec 7 2021

    Publication series

    Name2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

    Conference

    Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
    Country/TerritoryUnited States
    CityOrlando
    Period12/5/2112/7/21

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Computer Science Applications
    • Decision Sciences (miscellaneous)
    • Safety, Risk, Reliability and Quality
    • Control and Optimization

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