Abstract
BackgroundMicroorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. To understand their environments more deeply, the composition of microorganisms in environmental samples has been studied using metagenomes, which are the collections of genomes of the microorganisms. Although many tools have been developed for taxonomy analysis based on different algorithms, variability of analysis outputs of existing tools from the same input metagenome datasets is the main obstacle for many researchers in this field.ResultsHere, we present a novel meta-analysis tool for metagenome taxonomy analysis, called TAMA, by intelligently integrating outputs from three different taxonomy analysis tools. Using an integrated reference database, TAMA performs taxonomy assignment for input metagenome reads based on a meta-score by integrating scores of taxonomy assignment from different taxonomy classification tools. TAMA outperformed existing tools when evaluated using various benchmark datasets. It was also successfully applied to obtain relative species abundance profiles and difference in composition of microorganisms in two types of cheese metagenome and human gut metagenome.ConclusionTAMA can be easily installed and used for metagenome read classification and the prediction of relative species abundance from multiple numbers and types of metagenome read samples. TAMA can be used to more accurately uncover the composition of microorganisms in metagenome samples collected from various environments, especially when the use of a single taxonomy analysis tool is unreliable. TAMA is an open source tool, and can be downloaded at https://github.com/jkimlab/TAMA.
Highlights
Microorganisms are important occupants of many different environments
Evaluation of Taxonomy Analysis pipeline for metagenome using Meta-Analysis (TAMA) using simulated metagenome datasets The performance of TAMA was evaluated by comparing to the three initial taxonomy analysis tools (CLARK [10], Kraken [11], and Centrifuge [12]) used in TAMA in terms of the accuracy of read classification, species prediction, and species abundance estimation using simulated datasets (Implementation)
Application to human gut metagenome We identified a metagenome profile of human gut sample using TAMA
Summary
Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. Microbes are essential occupants in an ecological system that interact with and affect their environment. In the sea, these microbes help recycle nutrients [1]. They compete for nitrogen with plants [2] They are thought to be the cause of many diseases [3,4,5] They can affect the environment in various areas, such as animal development and biofuel production [6, 7]. It is crucial to identify the ecosystem of the microorganism in its environment, it is still hard to decipher the composition and functions of microbes in an environment because most bacteria on Earth cannot be cultivated [8]
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