Dataset Information

MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.

ABSTRACT: BACKGROUND:Many methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample. RESULT:Here we present MetaBinG2, a fast method for metagenomic sequence classification, especially for samples with a large number of unknown organisms. MetaBinG2 is based on sequence composition, and uses GPUs to accelerate its speed. A million 100 bp Illumina sequences can be classified in about 1 min on a computer with one GPU card. We evaluated MetaBinG2 by comparing it to multiple popular existing methods. We then applied MetaBinG2 to the dataset of MetaSUB Inter-City Challenge provided by CAMDA data analysis contest and compared community composition structures for environmental samples from different public places across cities. CONCLUSION:Compared to existing methods, MetaBinG2 is fast and accurate, especially for those samples with significant proportions of unknown organisms. REVIEWERS:This article was reviewed by Drs. Eran Elhaik, Nicolas Rascovan, and Serghei Mangul.

SUBMITTER: Qiao Y

PROVIDER: S-EPMC6104016 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.

Qiao Yuyang Y Jia Ben B Hu Zhiqiang Z Sun Chen C Xiang Yijin Y Wei Chaochun C

Biology direct 20180822 1

<h4>Background</h4>Many methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample.<h4>Result</h4>Here we present MetaBinG2, a fast method for metagenomic sequence classification, especially for samples with a large number of unknown organisms. MetaBinG2 is based on sequence composit ...[more]

PMID: 30134953

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Project description:BackgroundSequence signatures, as defined by the frequencies of k-tuples (or k-mers, k-grams), have been used extensively to compare genomic sequences of individual organisms, to identify cis-regulatory modules, and to study the evolution of regulatory sequences. Recently many next-generation sequencing (NGS) read data sets of metagenomic samples from a variety of different environments have been generated. The assembly of these reads can be difficult and analysis methods based on mapping reads to genes or pathways are also restricted by the availability and completeness of existing databases. Sequence-signature-based methods, however, do not need the complete genomes or existing databases and thus, can potentially be very useful for the comparison of metagenomic samples using NGS read data. Still, the applications of sequence signature methods for the comparison of metagenomic samples have not been well studied.ResultsWe studied several dissimilarity measures, including d2, d2* and d2S recently developed from our group, a measure (hereinafter noted as Hao) used in CVTree developed from Hao's group (Qi et al., 2004), measures based on relative di-, tri-, and tetra-nucleotide frequencies as in Willner et al. (2009), as well as standard lp measures between the frequency vectors, for the comparison of metagenomic samples using sequence signatures. We compared their performance using a series of extensive simulations and three real next-generation sequencing (NGS) metagenomic datasets: 39 fecal samples from 33 mammalian host species, 56 marine samples across the world, and 13 fecal samples from human individuals. Results showed that the dissimilarity measure d2S can achieve superior performance when comparing metagenomic samples by clustering them into different groups as well as recovering environmental gradients affecting microbial samples. New insights into the environmental factors affecting microbial compositions in metagenomic samples are obtained through the analyses. Our results show that sequence signatures of the mammalian gut are closely associated with diet and gut physiology of the mammals, and that sequence signatures of marine communities are closely related to location and temperature.ConclusionsSequence signatures can successfully reveal major group and gradient relationships among metagenomic samples from NGS reads without alignment to reference databases. The d2S dissimilarity measure is a good choice in all application scenarios. The optimal choice of tuple size depends on sequencing depth, but it is quite robust within a range of choices for moderate sequencing depths.

Dataset Information

MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.

Publications

MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.

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