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    We introduce Cliffy, a new method for taxonomic sequence classification that significantly improves accuracy and reduces space requirements for metagenomic and evolutionary studies. This computational genomics tool enhances large-scale sequence analysis.

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    Area of Science:

    • Computational genomics
    • Bioinformatics
    • Evolutionary biology

    Background:

    • Taxonomic sequence classification is crucial for metagenomics and evolution.
    • Existing compressed indexing methods struggle to scale with large, diverse taxonomic datasets.
    • Current data structures linking sequences to clades are inefficient for large numbers of genomes.

    Purpose of the Study:

    • To develop a more scalable and space-efficient method for taxonomic sequence classification.
    • To improve the accuracy of identifying organism clades from sequencing reads.
    • To create a tool that enhances the performance of compressed full-text indexes for taxonomic analysis.

    Main Methods:

    • Proposed cliff compression, a novel method reducing space complexity from O(rd) to O(r log d) words.
    • Implemented cliff compression in an open-source tool named Cliffy.
    • Evaluated Cliffy's performance on simulated 16S rRNA gene sequencing reads.

    Main Results:

    • Cliffy achieved over 250x space reduction on the SILVA 16S rRNA gene database.
    • Read-level accuracy of Cliffy surpassed Kraken2 by 11-18% on simulated data.
    • Clade abundance predictions by Cliffy were more accurate than Kraken2 and Bracken.

    Conclusions:

    • Cliffy offers a fast and space-economical extension for compressed full-text indexes.
    • The method enables efficient and accurate taxonomic classification of sequencing reads.
    • Cliffy advances computational genomics by improving scalability and accuracy in sequence analysis.