Evidence map›Paper›PMID 35689184›Full record

ArticleBMC genomics2022

UMGAP: the Unipept MetaGenomics Analysis Pipeline.

Felix Van der Jeugt, Rien Maertens, Aranka Steyaert, Pieter Verschaffelt, Caroline De Tender, Peter Dawyndt, Bart Mesuere

Abstract read
In one paragraph

Article in BMC genomics, 2022. 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
–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

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.

  1. Review
  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

7 authors.

Felix Van der JeugtDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium. unipept@ugent.be.ORCID http://orcid.org/0000-0003-3168-927X
Rien MaertensDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Aranka SteyaertDepartment of Information Technology, IDLab, imec, Ghent, Belgium.
Pieter VerschaffeltDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Caroline De TenderDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Peter DawyndtDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Bart MesuereDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Funding

Agentschap voor Innovatie door Wetenschap en Technologie 1164420NAgentschap voor Innovatie door Wetenschap en Technologie 1174621NAgentschap voor Innovatie door Wetenschap en Technologie 12I5220NAgentschap voor Innovatie door Wetenschap en Technologie 1512619N
6 · The paper itself

Abstract

backgroundShotgun metagenomics yields ever richer and larger data volumes on the complex communities living in diverse environments. Extracting deep insights from the raw reads heavily depends on the availability of fast, accurate and user-friendly biodiversity analysis tools.

resultsBecause environmental samples may contain strains and species that are not covered in reference databases and because protein sequences are more conserved than the genes encoding them, we explore the alternative route of taxonomic profiling based on protein coding regions translated from the shotgun metagenomics reads, instead of directly processing the DNA reads. We therefore developed the Unipept MetaGenomics Analysis Pipeline (UMGAP), a highly versatile suite of open source tools that are implemented in Rust and support parallelization to achieve optimal performance. Six preconfigured pipelines with different performance trade-offs were carefully selected, and benchmarked against a selection of state-of-the-art shotgun metagenomics taxonomic profiling tools.

conclusionsUMGAP's protein space detour for taxonomic profiling makes it competitive with state-of-the-art shotgun metagenomics tools. Despite our design choices of an extra protein translation step, a broad spectrum index that can identify both archaea, bacteria, eukaryotes and viruses, and a highly configurable non-monolithic design, UMGAP achieves low runtime, manageable memory footprint and high accuracy. Its interactive visualizations allow for easy exploration and comparison of complex communities.

Indexed as

MetagenomicsVirusesAlgorithmsBacteriaSequence Analysis, DNASoftwareBiodiversity analysisShotgun metagenomicsTaxonomic profiling

Identifiers

PMID35689184
PMCPMC9188040

What OpenQuestion holds

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LicenceCC BY
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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.