Evidence map›Paper›PMID 36224545›Full record

ArticleBMC bioinformatics2022

VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes.

Josh L Espinoza, Chris L Dupont

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
4.2field-weighted citation impact, top 6% 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

20 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. 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

2 authors at 1 institution in 1 country.

Josh L EspinozaDepartment of Environment and Sustainability, J. Craig Venter Institute, 4120 Capricorn Ln, La Jolla, CA, 92037, USA. jespinoz@jcvi.org.
Chris L DupontDepartment of Environment and Sustainability, J. Craig Venter Institute, 4120 Capricorn Ln, La Jolla, CA, 92037, USA.
J. Craig Venter Institute · US

Funding

Uncultured bacteria for the discovery of novel antimicrobialsP01AI118687 · NIAID · NORTHEASTERN UNIVERSITY · PI LEWIS, KIM · 2016 to 2020
$8.9M
Identifying Influenza Virus Infection Severity and Outcome Signatures Through Artificial Intelligence-driven AnalysesR01AI170111 · NIAID · J. CRAIG VENTER INSTITUTE, INC. · PI Christopher L. Dupont · 2022 to 2026
$3.0M
NIAID NIH HHS P01 AI118687NIAID NIH HHS R01 AI170111NIH HHS 1R01AI170111-01
6 · The paper itself

Abstract

backgroundWith the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth's natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ, however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes.

resultsIn this study, we introduce the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge, VEBA is the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes. VEBA implements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone. VEBA includes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification. VEBA also provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally, VEBA is the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments. VEBA's capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives.

conclusionsThe VEBA software suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways. VEBA fully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions of VEBA to the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks. VEBA allows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.

Indexed as

ArchaeaMetagenomeBacteriaCluster AnalysisEcosystemEukaryotaGenome, ViralHumansMetagenomicsBinningMetagenome-assembled genomeMetagenomicsPipeline

Identifiers

PMID36224545
PMCPMC9554839
OpenAlexW4304690728

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.