Evidence map›Paper›PMID 27312960›Full record

ArticleScientific reports2016

Molecular classification based on apomorphic amino acids (Arthropoda, Hexapoda): Integrative taxonomy in the era of phylogenomics.

Hao-Yang Wu, Yan-Hui Wang, Qiang Xie, Yun-Ling Ke, Wen-Jun Bu

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.8field-weighted citation impact, top 10% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

5 authors at 1 institution in 1 country.

Hao-Yang WuInstitute of Entomology, College of Life Sciences, Nankai University, Tianjin 300071, China.
Yan-Hui WangInstitute of Entomology, College of Life Sciences, Nankai University, Tianjin 300071, China.
Qiang XieInstitute of Entomology, College of Life Sciences, Nankai University, Tianjin 300071, China.
Yun-Ling KeGuangdong Entomological Institute, Guangzhou 510260, China.
Wen-Jun BuInstitute of Entomology, College of Life Sciences, Nankai University, Tianjin 300071, China.
Nankai University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the great development of sequencing technologies and systematic methods, our understanding of evolutionary relationships at deeper levels within the tree of life has greatly improved over the last decade. However, the current taxonomic methodology is insufficient to describe the growing levels of diversity in both a standardised and general way due to the limitations of using only morphological traits to describe clades. Herein, we propose the idea of a molecular classification based on hierarchical and discrete amino acid characters. Clades are classified based on the results of phylogenetic analyses and described using amino acids with group specificity in phylograms. Practices based on the recently published phylogenomic datasets of insects together with 15 de novo sequenced transcriptomes in this study demonstrate that such a methodology can accommodate various higher ranks of taxonomy. Such an approach has the advantage of describing organisms in a standard and discrete way within a phylogenetic framework, thereby facilitating the recognition of clades from the view of the whole lineage, as indicated by PhyloCode. By combining identification keys and phylogenies, the molecular classification based on hierarchical and discrete characters may greatly boost the progress of integrative taxonomy.

Indexed as

Evolution, MolecularPhylogenyAnimalsInsectaInsect ProteinsInsect Proteins

Identifiers

PMID27312960
PMCPMC4911608
OpenAlexW2424712522

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.