Evidence map›Paper›PMID 25259891›Full record

ArticlePloS one2014

SDT: a virus classification tool based on pairwise sequence alignment and identity calculation.

Brejnev Muhizi Muhire, Arvind Varsani, Darren Patrick Martin

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 745 papers.

0numbers the graph read from it
0cells of the map it votes in
745citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

745 citing papers in PubMed.

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685 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

3 authors.

Brejnev Muhizi MuhireDepartment of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa.
Arvind VarsaniDepartment of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa; School of Biological Sciences and Biomolecular Interaction Centre, University of Canterbury, Christchurch, New Zealand; Department of Plant Pathology and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.
Darren Patrick MartinDepartment of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The perpetually increasing rate at which viral full-genome sequences are being determined is creating a pressing demand for computational tools that will aid the objective classification of these genome sequences. Taxonomic classification approaches that are based on pairwise genetic identity measures are potentially highly automatable and are progressively gaining favour with the International Committee on Taxonomy of Viruses (ICTV). There are, however, various issues with the calculation of such measures that could potentially undermine the accuracy and consistency with which they can be applied to virus classification. Firstly, pairwise sequence identities computed based on multiple sequence alignments rather than on multiple independent pairwise alignments can lead to the deflation of identity scores with increasing dataset sizes. Also, when gap-characters need to be introduced during sequence alignments to account for insertions and deletions, methodological variations in the way that these characters are introduced and handled during pairwise genetic identity calculations can cause high degrees of inconsistency in the way that different methods classify the same sets of sequences. Here we present Sequence Demarcation Tool (SDT), a free user-friendly computer program that aims to provide a robust and highly reproducible means of objectively using pairwise genetic identity calculations to classify any set of nucleotide or amino acid sequences. SDT can produce publication quality pairwise identity plots and colour-coded distance matrices to further aid the classification of sequences according to ICTV approved taxonomic demarcation criteria. Besides a graphical interface version of the program for Windows computers, command-line versions of the program are available for a variety of different operating systems (including a parallel version for cluster computing platforms).

Indexed as

SoftwareBase SequenceComputational BiologySequence AlignmentViruses

Identifiers

PMID25259891
PMCPMC4178126

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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.