Evidence map›Paper›PMID 36602356›Full record

ArticleMicrobiology spectrum2023

PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes.

Yangjie Li, Bin Ma, Kexin Hua, Huimin Gong, Rongrong He, Rui Luo, Dingren Bi, Rui Zhou, Paul R Langford, Hui Jin

Open access · goldAbstract read
In one paragraph

Article in Microbiology spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

10 authors at 2 institutions in 2 countries.

Yangjie LiState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Bin MaState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Kexin HuaState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Huimin GongState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Rongrong HeState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Rui LuoState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.ORCID 0000-0002-1966-251X
Dingren BiState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Rui ZhouState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.ORCID 0000-0002-7903-9965
Paul R LangfordSection of Paediatric Infectious Disease, Imperial College London, St Mary's Campus, London, United Kingdom.ORCID 0000-0002-6368-4724
Hui JinState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.ORCID 0000-0001-8004-2662
Huazhong Agricultural University · CNSt Mary's Hospital · GB

Funding

Biotechnology and Biological Sciences Research Council BB/S019901/1
6 · The paper itself

Abstract

Identification of microbial functional association networks allows interpretation of biological phenomena and a greater understanding of the molecular basis of pathogenicity and also underpins the formulation of control measures. Here, we describe PPNet, a tool that uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial gene association networks of a single species. As an exemplar, we have derived a functional association network in the pig pathogen Streptococcus suis using 81 binary similarity and dissimilarity measures which demonstrates excellent performance based on the area under the receiver operating characteristic (AUROC), the area under the precision-recall (AUPR), and a derived overall scoring method. Selected network associations were validated experimentally by using bacterial two-hybrid experiments. We conclude that PPNet, a publicly available (https://github.com/liyangjie/PPNet), can be used to construct microbial association networks from easily acquired genome-scale data.

Indexed as

Genes, BacterialProkaryotic CellsAnimalsBacteriaGene Regulatory NetworksPhylogenySwinedereplicationfunctional association network inferencephylogenetic profilingprokaryotic genomeStreptococcus suis

Identifiers

PMID36602356
PMCPMC9927313
OpenAlexW4313544512

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.