Abstract
Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data- sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes.
Highlights
Microbial organisms are ubiquitous dwellers of the earth’s biosphere whose activities shape the earth’s biogeochemistry
The GRAMMy framework The GRAMMy framework is based on a mixture model for the short metagenomic reads and an Expectation Maximization (EM) algorithm, as outlined in the model schema and the analysis flowchart in Figures 1 and 2
We have developed the GRAMMy framework for estimating genome relative abundance with shotgun metagenomic reads
Summary
Microbial organisms are ubiquitous dwellers of the earth’s biosphere whose activities shape the earth’s biogeochemistry. The knowledge of their presence and abundance in nature is of great relevance to ecology as well as to human well-being. To study microbes in natural environments, researchers frequently apply whole genome shotgun sequencing to uncultured samples to generate genomic sequence reads reflecting the structure of microbial communities [2,3]. As a consequence of the random sampling and sequencing scheme of the shotgun metagenomics approach, the presence and abundance information of metagenomes is preserved in raw reads some studies have shown that biases in sampling can occur, as is true for virtually all approaches [4]. The subsequent analysis of metagenomic data remains a challenging computational problem because of the mixed nature of metagenomes and the fact that we only sequence a small fraction of them
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