Evidence map›Paper›PMID 25266120›Full record

ArticleScientific reports2014

Inferring phylogenies of evolving sequences without multiple sequence alignment.

Cheong Xin Chan, Guillaume Bernard, Olivier Poirion, James M Hogan, Mark A Ragan

Abstract read
In one paragraph

Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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  16. Alignment-Free Sequence Analysis and Applications.Annual review of biomedical data science · 2018
    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.

Cheong Xin ChanInstitute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Australia.
Guillaume BernardInstitute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Australia.
Olivier Poirion1] Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Australia [2].
James M HoganSchool of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.
Mark A RaganInstitute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, QLD 4072, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alignment-free methods, in which shared properties of sub-sequences (e.g. identity or match length) are extracted and used to compute a distance matrix, have recently been explored for phylogenetic inference. However, the scalability and robustness of these methods to key evolutionary processes remain to be investigated. Here, using simulated sequence sets of various sizes in both nucleotides and amino acids, we systematically assess the accuracy of phylogenetic inference using an alignment-free approach, based on D2 statistics, under different evolutionary scenarios. We find that compared to a multiple sequence alignment approach, D2 methods are more robust against among-site rate heterogeneity, compositional biases, genetic rearrangements and insertions/deletions, but are more sensitive to recent sequence divergence and sequence truncation. Across diverse empirical datasets, the alignment-free methods perform well for sequences sharing low divergence, at greater computation speed. Our findings provide strong evidence for the scalability and the potential use of alignment-free methods in large-scale phylogenomics.

Indexed as

Evolution, MolecularPhylogenyAmino Acid SequenceBase SequenceComputational BiologySequence AlignmentSequence Analysis, DNASequence Analysis, ProteinSoftware

Identifiers

PMID25266120
PMCPMC4179140

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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.