RT Journal
A1 Ahmed, Omar
A1 Boucher, Christina
A1 Langmead, Ben
T1 Robust 16S rRNA classification based on a compressed LCA index
JF Genome Research 
JO Genome Research 
YR 2025 
FD December 01 
VO 35 
IS 12 
SP 2650 
OP 2660 
DO 10.1101/gr.279846.124 
UL http://genome.cshlp.org/content/35/12/2650.abstract 
AB Taxonomic sequence classification is a computational problem central to the study of metagenomics and evolution. Advances in compressed indexing with the r-index enable full-text pattern matching against large sequence collections. But the data structures that link pattern sequences to their clades of origin still do not scale well to large collections. Previous work proposed the document array profiles, which use  words of space, where r is the number of maximal equal-letter runs in the Burrows–Wheeler transform, and d is the number of distinct genomes. The linear dependence on d is limiting, because real taxonomies can easily contain 10,000s of leaves or more. We propose a method called cliff compression that reduces this size by a large factor, >250× when indexing the SILVA 16S rRNA gene database. This method uses  words of space in expectation under a random model we propose here. We implemented these ideas in an open-source tool called Cliffy that performs efficient taxonomic classification of sequencing reads with respect to a compressed taxonomic index. When applied to simulated 16S rRNA reads, Cliffy's read-level accuracy is higher than Kraken2's by 11%–18%. Clade abundances are also more accurately predicted by Cliffy compared with Kraken2 and Bracken. Overall, Cliffy is a fast and space-economical extension to compressed full-text indexes, enabling them to perform fast and accurate taxonomic classification queries. Cliffy's accuracy underscores the advantages of full-text indexes, which offer a more precise solution compared with k-mer indexes designed for a specific k value.