Mining, analyzing, and integrating viral signals from metagenomic data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Tingting Zheng, Jun Li, Yueqiong Ni, Kang Kang, Maria-Anna Misiakou, Lejla Imamovic, Billy K. C. Chow, Anne A. Rode, Peter Bytzer, Morten Sommer, Gianni Panagiotou

ABSTRACT

BACKGROUND: Viruses are important components of microbial communities modulating community structure and function; however, only a couple of tools are currently available for phage identification and analysis from metagenomic sequencing data. Here we employed the random forest algorithm to develop VirMiner, a web-based phage contig prediction tool especially sensitive for high-abundances phage contigs, trained and validated by paired metagenomic and phagenomic sequencing data from the human gut flora. RESULTS: VirMiner achieved 41.06% ± 17.51% sensitivity and 81.91% ± 4.04% specificity in the prediction of phage contigs. In particular, for the high-abundance phage contigs, VirMiner outperformed other tools (VirFinder and VirSorter) with much higher sensitivity (65.23% ± 16.94%) than VirFinder (34.63% ± 17.96%) and VirSorter (18.75% ± 15.23%) at almost the same specificity. Moreover, VirMiner provides the most comprehensive phage analysis pipeline which is comprised of metagenomic raw reads processing, functional annotation, phage contig identification, and phage-host relationship prediction (CRISPR-spacer recognition) and supports two-group comparison when the input (metagenomic sequence data) includes different conditions (e.g., case and control). Application of VirMiner to an independent cohort of human gut metagenomes obtained from individuals treated with antibiotics revealed that 122 KEGG orthology and 118 Pfam groups had significantly differential abundance in the pre-treatment samples compared to samples at the end of antibiotic administration, including clustered regularly interspaced short palindromic repeats (CRISPR), multidrug resistance, and protein transport. The VirMiner webserver is available at http://sbb.hku.hk/VirMiner/ . CONCLUSIONS: We developed a comprehensive tool for phage prediction and analysis for metagenomic samples. Compared to VirSorter and VirFinder-the most widely used tools-VirMiner is able to capture more high-abundance phage contigs which could play key roles in infecting bacteria and modulating microbial community dynamics. TRIAL REGISTRATION: The European Union Clinical Trials Register, EudraCT Number: 2013-003378-28 . Registered on 9 April 2014. More... »

PAGES

42

References to SciGraph publications

  • 2017-01. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure in THE ISME JOURNAL
  • 2017-12. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data in MICROBIOME
  • 2014-07. Classification and quantification of bacteriophage taxa in human gut metagenomes in THE ISME JOURNAL
  • 2007-12. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats in BMC BIOINFORMATICS
  • 2017-01. Metagenomic recovery of phage genomes of uncultured freshwater actinobacteria in THE ISME JOURNAL
  • 2007-04. Multiple molecular mechanisms for multidrug resistance transporters in NATURE
  • 2010-07. Viruses in the faecal microbiota of monozygotic twins and their mothers in NATURE
  • 2017-12. Viral communities of the human gut: metagenomic analysis of composition and dynamics in MOBILE DNA
  • 2016-09. A proposed integrated approach for the preclinical evaluation of phage therapy in Pseudomonas infections in SCIENTIFIC REPORTS
  • 2006-08. Multidrug-resistance efflux pumps ? not just for resistance in NATURE REVIEWS MICROBIOLOGY
  • 2016-08. Uncovering Earth’s virome in NATURE
  • 2013-07. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome in NATURE
  • 2016-03. The initial state of the human gut microbiome determines its reshaping by antibiotics in THE ISME JOURNAL
  • 2013-12. Genome signature-based dissection of human gut metagenomes to extract subliminal viral sequences in NATURE COMMUNICATIONS
  • 2014-12. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes in NATURE COMMUNICATIONS
  • 2014-12. Temporal variability is a personalized feature of the human microbiome in GENOME BIOLOGY
  • 2017-01. Phages rarely encode antibiotic resistance genes: a cautionary tale for virome analyses in THE ISME JOURNAL
  • 2014-08. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes in NATURE BIOTECHNOLOGY
  • 2014-03. When a virus is not a parasite: the beneficial effects of prophages on bacterial fitness in JOURNAL OF MICROBIOLOGY
  • 2009-12. BLAST+: architecture and applications in BMC BIOINFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40168-019-0657-y

    DOI

    http://dx.doi.org/10.1186/s40168-019-0657-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112878859

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30890181


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