Evidence map›Paper›PMID 25120265›Full record

ArticleNucleic acids research2014

COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification.

Daniel Struck, Glenn Lawyer, Anne-Marie Ternes, Jean-Claude Schmit, Danielle Perez Bercoff

Abstract read
In one paragraph

Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 264 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
264citing papers in PubMed, 1 pooled it
–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

264 citing papers in PubMed, 1 synthesis or guideline pooled it.

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204 more citing papers are in PubMed but not listed here.

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.

Daniel StruckLaboratory of Retrovirology, CRP-Santé, 84, Val Fleuri, L-1526, Luxembourg daniel.struck@crp-sante.lu.
Glenn LawyerDepartment of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1 4, 66123 Saarbrücken, Germany.ORCID http://orcid.org/0000-0001-9781-063X
Anne-Marie TernesLaboratory of Retrovirology, CRP-Santé, 84, Val Fleuri, L-1526, Luxembourg.
Jean-Claude SchmitLaboratory of Retrovirology, CRP-Santé, 84, Val Fleuri, L-1526, Luxembourg.
Danielle Perez BercoffLaboratory of Retrovirology, CRP-Santé, 84, Val Fleuri, L-1526, Luxembourg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Viral sequence classification has wide applications in clinical, epidemiological, structural and functional categorization studies. Most existing approaches rely on an initial alignment step followed by classification based on phylogenetic or statistical algorithms. Here we present an ultrafast alignment-free subtyping tool for human immunodeficiency virus type one (HIV-1) adapted from Prediction by Partial Matching compression. This tool, named COMET, was compared to the widely used phylogeny-based REGA and SCUEAL tools using synthetic and clinical HIV data sets (1,090,698 and 10,625 sequences, respectively). COMET's sensitivity and specificity were comparable to or higher than the two other subtyping tools on both data sets for known subtypes. COMET also excelled in detecting and identifying new recombinant forms, a frequent feature of the HIV epidemic. Runtime comparisons showed that COMET was almost as fast as USEARCH. This study demonstrates the advantages of alignment-free classification of viral sequences, which feature high rates of variation, recombination and insertions/deletions. COMET is free to use via an online interface.

Indexed as

AlgorithmsHigh-Throughput Nucleotide SequencingHIV-1HumansPhylogenySoftware

Identifiers

PMID25120265
PMCPMC4191385

What OpenQuestion holds

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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.