Mining, analyzing, and integrating viral signals from metagenomic data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

3 Scopus Citations
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Author(s)

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

Detail(s)

Original languageEnglish
Article number42
Journal / PublicationMicrobiome
Volume7
Publication statusPublished - 19 Mar 2019

Link(s)

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.

Research Area(s)

  • Antibiotics, Metagenome, Phage, Phage-host interaction

Citation Format(s)

Mining, analyzing, and integrating viral signals from metagenomic data. / Zheng, Tingting; Li, Jun; Ni, Yueqiong; Kang, Kang; Misiakou, Maria-Anna; Imamovic, Lejla; Chow, Billy K. C.; Rode, Anne A.; Bytzer, Peter; Sommer, Morten; Panagiotou, Gianni.

In: Microbiome, Vol. 7, 42, 19.03.2019.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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