Mini‐Metagenomics and Nucleotide Composition Aid the Identification and Host Association of Novel Bacteriophage Sequences. Issue 11 (16th August 2019)

• Record Type: Journal Article
• Authors: Deaton, Jonathan; Yu, Feiqiao Brian; Quake, Stephen R.
• Is Part Of: Advanced biosystems. Volume 3:Issue 11(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-16
• Subjects: hot spring -- mini‐metagenomics -- phage -- PhaMers -- yellowstone; Biological systems -- Periodicals; Biotechnology -- Periodicals; Bioengineering -- Periodicals; Biomedical engineering -- Periodicals; Biological Science Disciplines; Periodicals; Periodicals; 660.6
• DOI: 10.1002/adbi.201900108
• Languages: English
• ISSNs: 2366-7478

Abstract

Abstract: A broad spectrum of metagenomic and single cell sequencing techniques have become popular for dissecting environmental microbial diversity, leading to the characterization of thousands of novel microbial lineages. In addition to recovering bacterial and archaeal genomes, metagenomic assembly can also produce genomes of viruses that infect microbial cells. Because of their diversity, lack of marker genes, and small genome size, identifying novel bacteriophage sequences from metagenomic data is often challenging, especially when the objective is to establish phage–host relationships. The present work describes a computational approach that uses supervised learning to classify metagenomic contigs as phage or non‐phage as well as assigning phage taxonomy based on tetranucleotide frequencies. Furthermore, the method assigns phage–host relationships using co‐occurrence statistics derived from a recently developed mini‐metagenomic experimental technique. This work evaluates method performance at identifying viral contigs and predicting taxonomic classification using publicly available references. Then, using two mini‐metagenomic datasets, over 100 novel phage contigs from hot spring samples of Yellowstone National Park are identified and assigned to putative microbial hosts. Results of this work demonstrate the value of combining viral sequence identification with mini‐metagenomic experimental methods to understand the microbial ecosystem. Abstract : A computational tool (PhaMers) based on supervised learning techniques to identify and taxonomically classify bacteriophage from assembled metagenomic contigs is developed in this work. PhaMers identifies hundreds of putative phages from hot spring datasets. Combining mini‐metagenomic co‐occurrence enables the assignment of phage contigs to putative microbial host genomes.