Centrifuger: lossless compression of microbial genomes for efficient and accurate metagenomic sequence classification

Song, Li; Langmead, Ben

doi:10.1186/s13059-024-03244-4

Cited by 3 publications

References 52 publications

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Cliffy: robust 16S rRNA classification based on a compressed LCA index

Ahmed,

Boucher,

Langmead

2024

Preprint

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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 O(rd) 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, since 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, over 250x when indexing the SILVA 16S rRNA gene database. This method uses Theta(r log d) 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 to 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.

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Cliffy: robust 16S rRNA classification based on a compressed LCA index

Ahmed,

Boucher,

Langmead

2024

Preprint

Self Cite

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Scorpio : Enhancing Embeddings to Improve Downstream Analysis of DNA sequences

Refahi,

Sokhansanj,

Mell

et al. 2024

Preprint

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Analyzing genomic and genetic sequences on the DNA level can be challenging due to the limited alphabet and sequence similarity varies depending on the labeling task, which makes tasks dependent on different evolutionary rates. In addition, metagenomic data poses significant challenges due to the vast diversity of taxa and genes within a microbiome. Here, we present Scorpio, a novel framework that employs triplet networks with contrastive learning, utilizing both pre-trained language models and k-mer frequency embeddings, to effectively a) discern taxonomic and gene information in metagenomic data and can be fine-tuned to b) identify drug resistance, etc. from AMR genes and c) identify promoters. Our approach demonstrates robust performance across a variety of tasks. It has notable performance in generalizing to novel taxonomic and gene classification (e.g. identifying known gene labels of sequences from novel taxa).The versatility of our triplet network framework for multitask classification highlights its potential for advancing health and environmental diagnostics. This method enhances our ability to process and interpret complex microbiome metagenomic data, offering significant implications for biomarker identification and the monitoring of disease and environmental health.

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