Evidence map›Paper›PMID 22084562›Full record

ArticleEvolutionary bioinformatics online2011

Characterization and inference of gene gain/loss along burkholderia evolutionary history.

Bo Zhu, Shengli Zhou, Miaomiao Lou, Jun Zhu, Bin Li, Guanlin Xie, Gulei Jin, René De Mot

Open access · goldAbstract read
In one paragraph

Article in Evolutionary bioinformatics online, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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  4. Tal1Frontiers in microbiology · 2019
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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

8 authors at 2 institutions in 1 country.

Bo ZhuState Key Laboratory of Rice Biology and Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Ministry of Agriculture, Institute of Biotechnology, Zhejiang University, Hangzhou 310029, China.
Shengli Zhou
Miaomiao Lou
Jun Zhu
Bin Li
Guanlin Xie
Gulei Jin
René De Mot
State Key Laboratory of Rice BiologyZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A comparative analysis of 60 complete Burkholderia genomes was conducted to obtain insight in the evolutionary history behind the diversity and pathogenicity at species level. A concatenated multiprotein phyletic pattern and a dataset with Burkholderia clusters of orthologous genes (BuCOGs) were constructed. The extent of horizontal gene transfer (HGT) was assessed using a Markov based probabilistic method. A reconstruction of the gene gains and losses history shows that more than half of the Burkholderia genes families are inferred to have experienced HGT at least once during their evolution. Further analysis revealed that the number of gene gain and loss was correlated with the branch length. Genomic islands (GEIs) analysis based on evolutionary history reconstruction not only revealed that most genes in ancient GEIs were gained but also suggested that the fraction of the genome located in GEIs in the small chromosomes is higher than in the large chromosomes in Burkholderia. The mapping of coexpressed genes onto biological pathway schemes revealed that pathogenicity of Burkholderia strains is probably mainly determined by the gained genes in its ancestor. Taken together, our results strongly support that gene gain and loss especially in ancient evolutionary history play an important role in strain divergence, pathogenicity determinants of Burkholderia and GEIs formation.

Indexed as

Burkholderiacoexpressiongene gain/lossgenomic islandMarkov model

Identifiers

PMID22084562
PMCPMC3210638
OpenAlexW2037232927

What OpenQuestion holds

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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.