Evidence map›Paper›PMID 30153857›Full record

ArticleMicrobiome2018

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling.

Ari Ugarte, Riccardo Vicedomini, Juliana Bernardes, Alessandra Carbone

Abstract readEvaluation Study
In one paragraph

Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Approaches for the Identification of Intrinsically Disordered Protein Domains.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021
    Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. 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

4 authors.

Ari UgarteSorbonne Université, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 Place Jussieu, Paris, 75005, France.
Riccardo VicedominiSorbonne Université, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 Place Jussieu, Paris, 75005, France.
Juliana BernardesSorbonne Université, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 Place Jussieu, Paris, 75005, France.
Alessandra CarboneSorbonne Université, UPMC-Univ P6, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 Place Jussieu, Paris, 75005, France. Alessandra.Carbone@lip6.fr.ORCID 0000-0003-2098-5743

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBiochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.

resultsMetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.

conclusionsLearning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

Indexed as

AlgorithmsBacteriaBacterial ProteinsDatabases, GeneticEnvironmental MicrobiologyGastrointestinal MicrobiomeHumansMetagenomeMetagenomicsMolecular Sequence AnnotationProtein DomainsBacterial ProteinsDomain annotationEnvironmentFunctional annotationMetagenomicMetatranscriptomicMotifProbabilistic model

Identifiers

PMID30153857
PMCPMC6114274

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.