Evidence map›Paper›PMID 25876137›Full record

ArticlePloS one2015

SNPer: an R library for quantitative variant analysis on single nucleotide polymorphisms among influenza virus populations.

Unitsa Sangket, Sukanya Vijasika, Hasnee Noh, Wasun Chantratita, Chonticha Klungthong, In Kyu Yoon, Stefan Fernandez, Wiriya Rutvisuttinunt

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. 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
0.2field-weighted citation impact, top 40% 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, 3 citations in OpenAlex.

  1. Article
  2. Article
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 3 institutions in 1 country.

Unitsa SangketDepartment of Molecular Biotechnology and Bioinformatics, Faculty of Science, Prince of Songkla University, Songkhla, Thailand.
Sukanya VijasikaDepartment of Molecular Biotechnology and Bioinformatics, Faculty of Science, Prince of Songkla University, Songkhla, Thailand.
Hasnee NohDepartment of Molecular Biotechnology and Bioinformatics, Faculty of Science, Prince of Songkla University, Songkhla, Thailand.
Wasun ChantratitaDepartment of Pathology, Faculty of Medicine, Ramathibodhi Hospital, Mahidol University, Bangkok, Thailand.
Chonticha KlungthongDepartment of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
In Kyu YoonDepartment of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Stefan FernandezDepartment of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Wiriya RutvisuttinuntDepartment of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
Armed Forces Research Institute of Medical Science · THPrince of Songkla University · THMahidol University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza virus (IFV) can evolve rapidly leading to genetic drifts and shifts resulting in human and animal influenza epidemics and pandemics. The genetic shift that gave rise to the 2009 influenza A/H1N1 pandemic originated from a triple gene reassortment of avian, swine and human IFVs. More minor genetic alterations in genetic drift can lead to influenza drug resistance such as the H274Y mutation associated with oseltamivir resistance. Hence, a rapid tool to detect IFV mutations and the potential emergence of new virulent strains can better prepare us for seasonal influenza outbreaks as well as potential pandemics. Furthermore, identification of specific mutations by closely examining single nucleotide polymorphisms (SNPs) in IFV sequences is essential to classify potential genetic markers associated with potentially dangerous IFV phenotypes. In this study, we developed a novel R library called "SNPer" to analyze quantitative variants in SNPs among IFV subpopulations. The computational SNPer program was applied to three different subpopulations of published IFV genomic information. SNPer queried SNPs data and grouped the SNPs into (1) universal SNPs, (2) likely common SNPs, and (3) unique SNPs. SNPer outperformed manual visualization in terms of time and labor. SNPer took only three seconds with no errors in SNP comparison events compared with 40 hours with errors using manual visualization. The SNPer tool can accelerate the capacity to capture new and potentially dangerous IFV strains to mitigate future influenza outbreaks.

Indexed as

Polymorphism, Single NucleotideAlgorithmsAllelesComputational BiologyGene LibraryGenetic DriftGenomicsHumansInfluenza, HumanOrthomyxoviridaeProgramming LanguagesSoftware

Identifiers

PMID25876137
PMCPMC4395159
OpenAlexW1977522491

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

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.