Centrifuge: rapid and sensitive classification of metagenomic sequences

By Daehwan Kim, Li Song, Florian P. Breitwieser, Steven L. Salzberg. bioRxiv, 2016

1,262 citations recorded by the source corpus.

Centrifuge is a novel microbial classification engine that enables rapid, accurate and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4,078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI non-redundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer based indexing schemes, which require far more extensive space. Centrifuge is available as free, open-source software from www.ccb.jhu.edu/software/centrifuge

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7de3ecf36cfaa0bbc847e7ca8cefe0a223fcf8a1

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Citations in the corpus

Evaluating the Information Content of Shallow Shotgun Metagenomics (2018, methodology): “Alignment was performed using several existing tools and algorithms, including Bowtie2 (23), Centrifuge (24), Kraken (21), an in-house k-mer-based aligner for comparison with Kraken (25), and an accelerated adaptation of Needleman-Wunsch alignment for exhaustive gapped semiglobal alignment (18, 19).”
A review of methods and databases for metagenomic classification and assembly (2019, methodology): “Centrifuge [80] is a fast and accurate metagenomics classifier using the Burrows–Wheeler transform (BWT) and an FM-index to store and index the genome database.”
VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data (2017, methodology): “The tools Centrifuge [18] and DIAMOND [19] can rapidly map metagenomic reads to optimally indexed nucleotide or protein databases respectively, and can be useful in identifying viral genes in metagenomics.”
Identifying viruses from metagenomic data using deep learning (2020, background): “Kraken [10], Centrifuge [11], and MetaPhlAn [12], which were designed mainly for bacteria composition analysis though, can rapidly map reads to known viral reference genomes.”
Current challenges and best-practice protocols for microbiome analysis (2019, background): “Centrifuge [116], MEGAN6 [117], taxator-tk [100], DUDes [118] and Taxonomer [119], also exist for taxonomic classifications of”
Improved metagenomic analysis with Kraken 2 (2019, methodology): “We processed these data with 4 nucleotide search-based sequence classification programs (Centrifuge [1], CLARK b Kraken 2 applies a spaced seed mask of s spaces to the minimizer and calculates a compact hash code, which is then used as a search query in its compact hash table; the lowest common…”
Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods (2019, methodology): “We searched all Trinity-Fusion (modes D, UC, and C)-reconstructed transcripts against available viral and bacterial sequences using Cen-trifuge [65] (Additional file 1: Table S5).”