Evidence map›Paper›PMID 35937545›Full record

ArticleNAR genomics and bioinformatics2022

Mining bacterial NGS data vastly expands the complete genomes of temperate phages.

Xianglilan Zhang, Ruohan Wang, Xiangcheng Xie, Yunjia Hu, Jianping Wang, Qiang Sun, Xikang Feng, Wei Lin, Shanwei Tong, Wei Yan and 12 more

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Prophage: agent provocateur?Archives of microbiology · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. The trade-off ofFrontiers in microbiology · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Xianglilan ZhangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, People's Republic of China.ORCID https://orcid.org/0000-0002-4946-4880
Ruohan WangDepartment of Computer Science, City University of Hong Kong, Hong Kong 999077, People's Republic of China.ORCID https://orcid.org/0000-0003-2327-2888
Xiangcheng XieCollege of Computer, National University of Defense Technology, Changsha 410073, People's Republic of China.
Yunjia HuBeijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.ORCID https://orcid.org/0000-0001-6642-6985
Jianping WangDepartment of Computer Science, City University of Hong Kong, Hong Kong 999077, People's Republic of China.
Qiang SunThe 964th Hospital, Changchun 130021, People's Republic of China.
Xikang FengSchool of Software, Northwestern Polytechnical University, Xi'an 710072, People's Republic of China.ORCID https://orcid.org/0000-0003-4029-3795
Wei LinBeijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
Shanwei TongBioinformatics Graduate Program, University of British Columbia, VancouverBC V6T 1Z4, Canada.
Wei YanNational Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
Huiqi WenState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, People's Republic of China.
Mengyao WangDepartment of Computer Science, City University of Hong Kong, Hong Kong 999077, People's Republic of China.
Shixiang ZhaiYantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, People's Republic of China.
Cheng SunSchool of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, People's Republic of China.
Fangyi WangDepartment of Statistics, the Ohio State University, Columbus,OH 43210, USA.
Qi NiuSchool of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, People's Republic of China.
Andrew M KropinskiDepartments of Food Science, and Pathobiology, University of Guelph, Guelph, ONN1G 2W1, Canada.
Yujun CuiState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, People's Republic of China.
Xiaofang JiangNational Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.ORCID https://orcid.org/0000-0002-0955-8284
Shaoliang PengSchool of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, People's Republic of China.
Shuaicheng LiDepartment of Computer Science, City University of Hong Kong, Hong Kong 999077, People's Republic of China.ORCID https://orcid.org/0000-0001-6246-6349
Yigang TongBeijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.ORCID https://orcid.org/0000-0002-8503-8045

Funding

Comparative genomic analysis of bacteria speciesZIALM202010 · NLM · NATIONAL LIBRARY OF MEDICINE · PI JIANG, XIAOFANG · 2020 to 2025
$6.9M
6 · The paper itself

Abstract

Temperate phages (active prophages induced from bacteria) help control pathogenicity, modulate community structure, and maintain gut homeostasis. Complete phage genome sequences are indispensable for understanding phage biology. Traditional plaque techniques are inapplicable to temperate phages due to their lysogenicity, curbing their identification and characterization. Existing bioinformatics tools for prophage prediction usually fail to detect accurate and complete temperate phage genomes. This study proposes a novel computational temperate phage detection method (TemPhD) mining both the integrated active prophages and their spontaneously induced forms (temperate phages) from next-generation sequencing raw data. Applying the method to the available dataset resulted in 192 326 complete temperate phage genomes with different host species, expanding the existing number of complete temperate phage genomes by more than 100-fold. The wet-lab experiments demonstrated that TemPhD can accurately determine the complete genome sequences of the temperate phages, with exact flanking sites, outperforming other state-of-the-art prophage prediction methods. Our analysis indicates that temperate phages are likely to function in the microbial evolution by (i) cross-infecting different bacterial host species; (ii) transferring antibiotic resistance and virulence genes and (iii) interacting with hosts through restriction-modification and CRISPR/anti-CRISPR systems. This work provides a comprehensively complete temperate phage genome database and relevant information, which can serve as a valuable resource for phage research.

Identifiers

PMID35937545
PMCPMC9346568

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.