accurate genome relative abundance estimation based on shotgun metagenomic reads准确的基因组相对丰度估计基于猎枪metagenomic读取.pdfVIP

Accurate Genome Relative Abundance Estimation Based on Shotgun Metagenomic Reads 1 2 1,3 2 1,3 Li C. Xia , Jacob A. Cram , Ting Chen , Jed A. Fuhrman , Fengzhu Sun * 1 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America, 2 Marine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America, 3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, People’s Republic of China 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 sampl