CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers

By Rachid Ounit, Steve Wanamaker, Timothy J. Close, Stefano Lonardi. BMC Genomics, 2015

600 citations recorded by the source corpus.

The problem of supervised DNA sequence classification arises in several fields of computational molecular biology. Although this problem has been extensively studied, it is still computationally challenging due to size of the datasets that modern sequencing technologies can produce. We introduce Clark a novel approach to classify metagenomic reads at the species or genus level with high accuracy and high speed. Extensive experimental results on various metagenomic samples show that the classification accuracy of Clark is better or comparable to the best state-of-the-art tools and it is significantly faster than any of its competitors. In its fastest single-threaded mode Clark classifies, with high accuracy, about 32 million metagenomic short reads per minute. Clark can also classify BAC clones or transcripts to chromosome arms and centromeric regions. Clark is a versatile, fast and accurate sequence classification method, especially useful for metagenomics and genomics applications. It is freely available at http://clark.cs.ucr.edu/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6bd3a44e70c62195170928ea15385b17ba645deb

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Citations in the corpus

A review of methods and databases for metagenomic classification and assembly (2019, methodology): “Both Kraken and CLARK set k¼ 31 by default, although the database can be built with any length k-mer.”
Improved metagenomic analysis with Kraken 2 (2019, background): “[2], Kraken 1 [4], and KrakenUniq [5]) and a translated search classifier (Kaiju [3]).”
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts (2018, methodology): “[20], CLARK-S [21], Kraken [9], LMAT [22], and NBC [23]; and the marker-based methods GOTTCHA [24], MetaPhlAn2 [25], and PhyloSift [26].”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, background): “QIIME and mothur offer the possibility to readily calculate many β-diversity metrics (Schloss et al., 2009; Navas-Molina et al., 2013) and so does the R package vegan (Oksanen et al., 2015).”
Current challenges and best-practice protocols for microbiome analysis (2019, methodology): “Another tool CLARK utilizes a modified approach of keeping only species or genus-level k-mers and discarding the rest of the k-mers that map to higher taxonomic classifications [115].”