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This article is part of the supplement: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM 2013): Genomics

Open Access Research

MetaID: A novel method for identification and quantification of metagenomic samples

Satish M Srinivasan1 and Chittibabu Guda12*

Author Affiliations

1 Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198-5805, USA

2 Bioinformatics and Systems Biology Core, University of Nebraska Medical Center, Omaha, NE 68198-5805, USA

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BMC Genomics 2013, 14(Suppl 8):S4  doi:10.1186/1471-2164-14-S8-S4

Published: 9 December 2013

Abstract

Background

Advances in next-generation sequencing (NGS) technology has provided us with an opportunity to analyze and evaluate the rich microbial communities present in all natural environments. The shorter reads obtained from the shortgun technology has paved the way for determining the taxonomic profile of a community by simply aligning the reads against the available reference genomes. While several computational methods are available for taxonomic profiling at the genus- and species-level, none of these methods are effective at the strain-level identification due to the increasing difficulty in detecting variation at that level. Here, we present MetaID, an alignment-free n-gram based approach that can accurately identify microorganisms at the strain level and estimate the abundance of each organism in a sample, given a metagenomic sequencing dataset.

Results

MetaID is an n-gram based method that calculates the profile of unique and common n-grams from the dataset of 2,031 prokaryotic genomes and assigns weights to each n-gram using a scoring function. This scoring function assigns higher weightage to the n-grams that appear in fewer genomes and vice versa; thus, allows for effective use of both unique and common n-grams for species identification. Our 10-fold cross-validation results on a simulated dataset show a remarkable accuracy of 99.7% at the strain-level identification of the organisms in gut microbiome. We also demonstrated that our model shows impressive performance even by using only 25% or 50% of the genome sequences for modeling. In addition to identification of the species, our method can also estimate the relative abundance of each species in the simulated metagenomic samples. The generic approach employed in this method can be applied for accurate identification of a wide variety of microbial species (viruses, prokaryotes and eukaryotes) present in any environmental sample.

Conclusions

The proposed scoring function and approach is able to accurately identify and estimate the entire taxa in any metagenomic community. The weights assigned to the common n-grams by our scoring function are precisely calibrated to match the reads up to the strain level. Our multipronged validation tests demonstrate that MetaID is sufficiently robust to accurately identify and estimate the abundance of each taxon in any natural environment even when using incomplete or partially sequenced genomes.

Keywords:
MetaID; Metagenomics; Gut microbiome; Personalized medicine