Evidence map›Paper›PMID 31202277›Full record

ArticleBMC genomics2019

HumanMycobiomeScan: a new bioinformatics tool for the characterization of the fungal fraction in metagenomic samples.

Matteo Soverini, Silvia Turroni, Elena Biagi, Patrizia Brigidi, Marco Candela, Simone Rampelli

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 17% 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, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
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  5. Review
  6. Article
  7. Review
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  10. Article
  11. Review
  12. Analyzing the human gut mycobiome - A short guide for beginners.Computational and structural biotechnology journal · 2022
    Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. The role of gut mycobiome in health and diseases.Therapeutic advances in gastroenterology · 2021
    Review
  18. Article
  19. Article
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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

6 authors at 1 institution in 1 country.

Matteo SoveriniDepartment of Pharmacy and Biotechnology, Unit of Microbial Ecology of Health, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy. matteo.soverini5@unibo.it.ORCID http://orcid.org/0000-0002-3026-9460
Silvia TurroniDepartment of Pharmacy and Biotechnology, Unit of Microbial Ecology of Health, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy.
Elena BiagiDepartment of Pharmacy and Biotechnology, Unit of Microbial Ecology of Health, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy.
Patrizia BrigidiDepartment of Pharmacy and Biotechnology, Unit of Microbial Ecology of Health, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy.
Marco CandelaDepartment of Pharmacy and Biotechnology, Unit of Microbial Ecology of Health, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy.
Simone RampelliDepartment of Pharmacy and Biotechnology, Unit of Microbial Ecology of Health, University of Bologna, Via Belmeloro 6, 40126, Bologna, Italy.
University of Bologna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundModern metagenomic analysis of complex microbial communities produces large amounts of sequence data containing information on the microbiome in terms of bacterial, archaeal, viral and eukaryotic composition. The bioinformatics tools available are mainly devoted to profiling the bacterial and viral fractions and only a few software packages consider fungi. As the human fungal microbiome (human mycobiome) can play an important role in the onset and progression of diseases, a comprehensive description of host-microbiota interactions cannot ignore this component.

resultsHumanMycobiomeScan is a bioinformatics tool for the taxonomic profiling of the mycobiome directly from raw data of next-generation sequencing. The tool uses hierarchical databases of fungi in order to unambiguously assign reads to fungal species more accurately and > 10,000 times faster than other comparable approaches. HumanMycobiomeScan was validated using in silico generated synthetic communities and then applied to metagenomic data, to characterize the intestinal fungal components in subjects adhering to different subsistence strategies.

conclusionsAlthough blind to unknown species, HumanMycobiomeScan allows the characterization of the fungal fraction of complex microbial ecosystems with good performance in terms of sample denoising from reads belonging to other microorganisms. HumanMycobiomeScan is most appropriate for well-studied microbiomes, for which most of the fungal species have been fully sequenced. This released version is functionally implemented to work with human-associated microbiota samples. In combination with other microbial profiling tools, HumanMycobiomeScan is a frugal and efficient tool for comprehensive characterization of microbial ecosystems through shotgun metagenomics sequencing.

Indexed as

SoftwareAnimalsFungiHigh-Throughput Nucleotide SequencingHumansMetagenomicsMycobiomeMetagenomicsMicrobiomeMycobiome

Identifiers

PMID31202277
PMCPMC6570844
OpenAlexW2950027438

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.