WGSQuikr: Fast Whole-Genome Shotgun Metagenomic Classification

By David Koslicki, Simon Foucart, Gail L. Rosen. 2014

43 citations recorded by the source corpus.

With the decrease in cost and increase in output of whole-genome shotgun technologies, many metagenomic studies are utilizing this approach in lieu of the more traditional 16S rRNA amplicon technique. Due to the large number of relatively short reads output from whole-genome shotgun technologies, there is a need for fast and accurate short-read OTU classifiers. While there are relatively fast and accurate algorithms available, such as MetaPhlAn, MetaPhyler, PhyloPythiaS, and PhymmBL, these algorithms still classify samples in a read-by-read fashion and so execution times can range from hours to days on large datasets. We introduce WGSQuikr, a reconstruction method which can compute a vector of taxonomic assignments and their proportions in the sample with remarkable speed and accuracy. We demonstrate on simulated data that WGSQuikr is typically more accurate and up to an order of magnitude faster than the aforementioned classification algorithms. We also verify the utility of WGSQuikr on real biological data in the form of a mock community. WGSQuikr is a Whole-Genome Shotgun QUadratic, Iterative, -mer based Reconstruction method which extends the previously introduced 16S rRNA-based algorithm Quikr. A MATLAB implementation of WGSQuikr is available at: http://sourceforge.net/projects/wgsquikr.

Subfields: Ecology, Molecular Biology

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Citations in the corpus

CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers (2015, background): “A related group of metagenomic tools, such as METAPHLAN [7] and WGSQUIKR [13] addresses the abundance estimation problem, that is, they estimate from the reads the proportion of each organism present in the sample.”
Machine learning for metagenomics: methods and tools (2015, methodology): “, 2013) (and its whole genome version WGSQuikr (Koslicki et al., 2014)) and Taxy, a mixture modeling approach of the overall oligonucleotide distribution of a metagenomic dataset (Meinicke et al.”
Large-scale machine learning for metagenomics sequence classification (2015, methodology): “Tuning the method involves training machine learning models on about 108 samples in 107 dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning.”