Evidence map›Paper›PMID 34178438›Full record

ArticlePeerJ2021

Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation.

Akbar Adjie Pratama, Benjamin Bolduc, Ahmed A Zayed, Zhi-Ping Zhong, Jiarong Guo, Dean R Vik, Maria Consuelo Gazitúa, James M Wainaina, Simon Roux, Matthew B Sullivan

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
15.6field-weighted citation impact, top 1% 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

66 citing papers in PubMed, 128 citations in OpenAlex.

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6 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

10 authors at 2 institutions in 1 country.

Akbar Adjie PratamaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Benjamin BolducDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Ahmed A ZayedDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Zhi-Ping ZhongDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Jiarong GuoDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Dean R VikDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Maria Consuelo GazitúaViromica Consulting, Santiago, Chile.
James M WainainaDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Simon RouxDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Matthew B SullivanDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
The Ohio State University · USJoint Genome Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundViruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs).

resultsThe in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while

conclusionTogether, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses 'hidden' in diverse sequence datasets.

Indexed as

BenchmarksEcologyStandard operating procedureViromicsViruses

Identifiers

PMID34178438
PMCPMC8210812
OpenAlexW3167996047

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.