Evidence map›Paper›PMID 39574575›Full record

ArticlebioRxiv : the preprint server for biology2024

Evaluating metagenomic analyses for undercharacterized environments: what's needed to light up the microbial dark matter?

William A Nickols, Lauren J McIver, Aaron Walsh, Yancong Zhang, Jacob T Nearing, Francesco Asnicar, Michal Punčochář, Nicola Segata, Long H Nguyen, Erica M Hartmann and 3 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

William A NickolsDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.ORCID 0000-0001-8214-9746
Lauren J McIverDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Aaron WalshDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Yancong ZhangDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Jacob T NearingDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Francesco AsnicarDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Michal PunčochářDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Nicola SegataDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Long H NguyenDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.ORCID 0000-0002-5436-4219
Erica M HartmannDepartment of Civil and Environmental Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.ORCID 0000-0002-0966-2014
Eric A FranzosaDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Curtis HuttenhowerDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.ORCID 0000-0002-1110-0096
Kelsey N ThompsonDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.

Funding

Technology CoreU19AI110820 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI RASKO, DAVID A · 2014 to 2023
$36.5M
A comprehensive platform for novel therapy development from the microbiomeR24DK110499 · NIDDK · BROAD INSTITUTE, INC. · PI HUTTENHOWER, CURTIS · 2017 to 2021
$7.7M
NIAID NIH HHS U19 AI110820NIDDK NIH HHS R24 DK110499
6 · The paper itself

Abstract

Non-human-associated microbial communities play important biological roles, but they remain less understood than human-associated communities. Here, we assess the impact of key environmental sample properties on a variety of state-of-the-art metagenomic analysis methods. In simulated datasets, all methods performed similarly at high taxonomic ranks, but newer marker-based methods incorporating metagenomic assembled genomes outperformed others at lower taxonomic levels. In real environmental data, taxonomic profiles assigned to the same sample by different methods showed little agreement at lower taxonomic levels, but the methods agreed better on community diversity estimates and estimates of the relationships between environmental parameters and microbial profiles.

Indexed as

AssemblyBioinformaticsEvaluationMetagenomicsMicrobiomeOceanSoilSyntheticTaxonomic profiling

Identifiers

PMID39574575
PMCPMC11580994

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.