Beyond classification: gene-family phylogenies from shotgun metagenomic reads enable accurate community analysis

• Published in: BMC genomics Vol. 14; no. 1; p. 419
• Main Authors: Riesenfeld, Samantha J, Pollard, Katherine S
• Format: Journal Article
• Language: English
• Published: London BioMed Central 22.06.2013; BioMed Central Ltd; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Animal Genetics and Genomics; Biomedical and Life Sciences; Colleges & universities; Community; Comparative and evolutionary genomics; Computer programs; Databases, Genetic; Design; False Positive Reactions; Genetic aspects; Genomes; Genomics; Life Sciences; Metagenomics - methods; Microarrays; Microbial activity; Microbial Genetics and Genomics; Microbiology; Microorganisms; Nucleotide sequence; Phylogenetics; Phylogeny; Plant Genetics and Genomics; Proteomics; Research Article; Research Design; Software; Statistical methods; Taxonomy; Trees; United States > US; San Francisco California; Phylogenetics; Metagenomics; Simulations
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-14-419

Background Sequence-based phylogenetic trees are a well-established tool for characterizing diversity of both macroorganisms and microorganisms. Phylogenetic methods have recently been applied to shotgun metagenomic data from microbial communities, particularly with the aim of classifying reads. But the accuracy of gene-family phylogenies that characterize evolutionary relationships among short, non-overlapping sequencing reads has not been thoroughly evaluated. Results To quantify errors in metagenomic read trees, we developed MetaPASSAGE, a software pipeline to generate in silico bacterial communities, simulate a sample of shotgun reads from a gene family represented in the community, orient or translate reads, and produce a profile-based alignment of the reads from which a gene-family phylogenetic tree can be built. We applied MetaPASSAGE to a variety of RNA and protein-coding gene families, built trees using a range of different phylogenetic methods, and compared the resulting trees using topological and branch-length error metrics. We identified read length as one of the major sources of error. Because phylogenetic methods use a reference database of full-length sequences from the gene family to guide construction of alignments and trees, we found that error can also be substantially reduced through increasing the size and diversity of the reference database. Finally, UniFrac analysis, which compares metagenomic samples based on a summary statistic computed over all branches in a read tree, is very robust to the level of error we observe. Conclusions Bacterial community diversity can be quantified using phylogenetic approaches applied to shotgun metagenomic data. As sequencing reads get longer and more genomes across the bacterial tree of life are sequenced, the accuracy of this approach will continue to improve, opening the door to more applications.