Evidence map›Paper›PMID 30717665›Full record

ArticleBMC bioinformatics2019

ENVirT: inference of ecological characteristics of viruses from metagenomic data.

Duleepa Jayasundara, Damayanthi Herath, Damith Senanayake, Isaam Saeed, Cheng-Yu Yang, Yuan Sun, Bill C Chang, Sen-Lin Tang, Saman K Halgamuge

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In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

9 authors.

Duleepa JayasundaraSchool of Public Health and Community Medicine, University of New South Wales, Randwick, NSW 2052, Australia. duleepalj@gmail.com.ORCID http://orcid.org/0000-0001-6937-3807
Damayanthi HerathOptimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.
Damith SenanayakeOptimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.
Isaam SaeedOptimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.
Cheng-Yu YangBiodiversity Research Center, Academia Sinica, Nan-Kang, Taipei 11529, Taiwan.
Yuan SunOptimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.
Bill C ChangYourgene Bioscience, No. 376-5, Fuxing Rd., Shu-Lin District, New Taipei City, Taiwan.
Sen-Lin TangBiodiversity Research Center, Academia Sinica, Nan-Kang, Taipei 11529, Taiwan. sltang@gate.sinica.edu.tw.
Saman K HalgamugeOptimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.

Funding

Australian Research Council DP150103512Australian Research Council LP140100670
6 · The paper itself

Abstract

backgroundEstimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible using metagenomic approaches. However, the best-performing existing methods require databases to first estimate an average genome length of a viral community before being able to estimate other parameters, such as viral richness. Although this approach has been widely used, it can adversely skew results since the majority of viruses are yet to be catalogued in databases.

resultsIn this paper, we present ENVirT, a method for estimating the richness of novel viral mixtures, and for the first time we also show that it is possible to simultaneously estimate the average genome length without a priori information. This is shown to be a significant improvement over database-dependent methods, since we can now robustly analyze samples that may include novel viral types under-represented in current databases. We demonstrate that the viral richness estimates produced by ENVirT are several orders of magnitude higher in accuracy than the estimates produced by existing methods named PHACCS and CatchAll when benchmarked against simulated data. We repeated the analysis of 20 metavirome samples using ENVirT, which produced results in close agreement with complementary in virto analyses.

conclusionsThese insights were previously not captured by existing computational methods. As such, ENVirT is shown to be an essential tool for enhancing our understanding of novel viral populations.

Indexed as

AlgorithmsEcological and Environmental PhenomenaMetagenomicsComputer SimulationFermented FoodsGastrointestinal MicrobiomeGenome, ViralHumansLakesTime FactorsVirusesAverage genome lengthRichness estimationViral metagenomics

Identifiers

PMID30717665
PMCPMC7394321

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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.