Evidence map›Paper›PMID 33841754›Full record

ReviewComputational and structural biotechnology journal2021

Integrating pan-genome with metagenome for microbial community profiling.

Chaofang Zhong, Chaoyun Chen, Lusheng Wang, Kang Ning

Open access · goldAbstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
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

12 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. ODGI: understanding pangenome graphs.Bioinformatics (Oxford, England) · 2022
    Article
  10. Article
  11. Review
  12. Review
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

4 authors at 2 institutions in 2 countries.

Chaofang ZhongKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Chaoyun ChenKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Lusheng WangDepartment of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China.
Kang NingKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
City University of Hong Kong · HKHuazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in sequencing technology have led to the increased availability of genomes and metagenomes, which has greatly facilitated microbial pan-genome and metagenome analysis in the community. In line with this trend, studies on microbial genomes and phenotypes have gradually shifted from individuals to environmental communities. Pan-genomics and metagenomics are powerful strategies for in-depth profiling study of microbial communities. Pan-genomics focuses on genetic diversity, dynamics, and phylogeny at the multi-genome level, while metagenomics profiles the distribution and function of culture-free microbial communities in special environments. Combining pan-genome and metagenome analysis can reveal the microbial complicated connections from an individual complete genome to a mixture of genomes, thereby extending the catalog of traditional individual genomic profile to community microbial profile. Therefore, the combination of pan-genome and metagenome approaches has become a promising method to track the sources of various microbes and decipher the population-level evolution and ecosystem functions. This review summarized the pan-genome and metagenome approaches, the combined strategies of pan-genome and metagenome, and applications of these combined strategies in studies of microbial dynamics, evolution, and function in communities. We discussed emerging strategies for the study of microbial communities that integrate information in both pan-genome and metagenome. We emphasized studies in which the integrating pan-genome with metagenome approach improved the understanding of models of microbial community profiles, both structural and functional. Finally, we illustrated future perspectives of microbial community profile: more advanced analytical techniques, including big-data based artificial intelligence, will lead to an even better understanding of the patterns of microbial communities.

Indexed as

Integrated pan-genomics with metagenomicsMetagenomeMicrobial communityPan-genome

Identifiers

PMID33841754
PMCPMC8010324
OpenAlexW3133795275

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.