Lossless compression of protein databases for efficient and accurate metagenomic sequence classification with Centrifuger
- 1Dartmouth College
Abstract
We present a lossless compression algorithm for indexing a protein database while supporting fast taxonomic classification in the method Centrifuger. The algorithm is a new scheme of the previously proposed run-block compression algorithm to reduce the size of the Ferragina-Manzini (FM) index, and it scales better with alphabet size than the original. On the RefSeq prokaryotic and viral protein sequences, Centrifuger reduces the memory footprint by over a third compared to the method Kaiju that builds on a plain FM-index, while having comparable running time. Furthermore, the compressed FM-index is lossless and can locate matches of arbitrary length, which helps Centrifuger achieve greater accuracy than Kraken2, a k-mer-based taxonomic classification method. We leverage the computational efficiency of Centrifuger to create an index of size 182 GB for classifying the reads against the full nr database that contains about 250 billion amino acid characters. Using this index, Centrifuger reveals different SARS-CoV-2 infection states and viral transcriptome profiles across human cell types from single-cell RNA-seq data.
Related
- Hiding in plain sight: rare Bacillus cereus group species are present among unassembled metagenomic samples in public databases
- Codon-aware multi-scale feature fusion for metagenomic sequence classification
- Storage-Centric System Designs for Enabling Fast, Efficient, and Low-Cost Genomic and Metagenomic Analyses
- MI-PEFT: Mixture-of-Experts Integrated Parameter-Efficient Fine-Tuning Protein Language Models Improves Acidophilic Proteins Classification
- GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification
BibTeX
@misc{song2026lossless,
title = {{Lossless compression of protein databases for efficient and accurate metagenomic sequence classification with Centrifuger}},
author = {L. Song},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.17.752512},
url = {https://www.biorxiv.org/content/10.64898/2026.09.17.752512v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Lossless compression of protein databases for efficient and accurate metagenomic sequence classification with Centrifuger
AU - L. Song
PY - 2026
DA - 2026/09/17
PB - bioRxiv
DO - 10.64898/2026.09.17.752512
UR - https://www.biorxiv.org/content/10.64898/2026.09.17.752512v1
AB - We present a lossless compression algorithm for indexing a protein database while supporting fast taxonomic classification in the method Centrifuger. The algorithm is a new scheme of the previously proposed run-block compression algorithm to reduce the size of the Ferragina-Manzini (FM) index, and it scales better with alphabet size than the original. On the RefSeq prokaryotic and viral protein sequences, Centrifuger reduces the memory footprint by over a third compared to the method Kaiju that builds on a plain FM-index, while having comparable running time. Furthermore, the compressed FM-index is lossless and can locate matches of arbitrary length, which helps Centrifuger achieve greater accuracy than Kraken2, a k-mer-based taxonomic classification method. We leverage the computational efficiency of Centrifuger to create an index of size 182 GB for classifying the reads against the full nr database that contains about 250 billion amino acid characters. Using this index, Centrifuger reveals different SARS-CoV-2 infection states and viral transcriptome profiles across human cell types from single-cell RNA-seq data.
ER -