PhaMers identifies novel bacteriophage sequences from thermophilic hot springs
Abstract
Metagenomic sequencing approaches have become popular for the purpose of dissecting environmental microbial diversity, leading to the characterization of novel microbial lineages. In addition of bacterial and fungal genomes, metagenomic analysis can also reveal genomes of viruses that infect microbial cells. Because of their small genome size and limited knowledge of phage diversity, discovering novel phage sequences from metagenomic data is often challenging. Here we describe PhaMers (Phage k-Mers). a phage identification tool that uses supervised learning to classify metagenomic contigs as phage or non-phage on the basis of tetranucleotide frequencies. a technique that does not depend on existing gene annotations. PhaMers compares the tetranucleotide frequencies of metagenomic contigs to phage and bacteria references from online databases. resulting in assignments of lower level phage taxonomy based on sequence similarity. Using PhaMers. we identified 103 novel phage sequences from hot spring samples of Yellowstone National Park based on data generated from a microfluidic-based minimetagenomic approach. We analyzed assembled contigs over 5 kbp in length using PhaMers and compared the results with those generated by VirSorter, a publicly available phage identification and annotation package. We analyzed the performance of phage genome prediction and taxonomic classification using PhaMers. and presented putative hosts and taxa for some of the novel phage sequences. Finally. mini-metagenomic occurrence profiles of phage and prokaryotic genomes were used to verify putative hosts.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 21
- FWCI
- 0.95
- Field percentile
- top 26%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Seeker: Alignment-free identification of bacteriophage genomes by deep learning
N. Auslander, A. B. Gussow, … E. V. Koonin·Nucleic Acids ResearchPeer-reviewed·Oct 12, 2020
- Distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach
Shufang Wu, Zhencheng Fang, … Huaiqiu Zhu·GigaSciencePeer-reviewed·Sep 1, 2021
- Phables: from fragmented assemblies to high-quality bacteriophage genomes
Vijini Mallawaarachchi, Michael J. Roach, … Robert Alan Edwards·BioinformaticsPeer-reviewed·Sep 19, 2023
- Gauge your phage: Benchmarking of bacteriophage identification tools in metagenomic sequencing data
S. F. S. Ho, N. Wheeler, … W. van Schaik·bioRxiv·Apr 12, 2021
- Identify phage hosts from metaviromic short reads based on deep learning and Markov chain model
Jie Tan, Zhencheng Fang, … Huaiqiu Zhu·BioinformaticsPeer-reviewed·Aug 10, 2021
- Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences
Yasas Wijesekara, Lingyi Wu, … Lars Kaderali·bioRxiv·Sep 24, 2024