Evidence map›Paper›PMID 29187204›Full record

ReviewGenome biology2017

Experimental design and quantitative analysis of microbial community multiomics.

Himel Mallick, Siyuan Ma, Eric A Franzosa, Tommi Vatanen, Xochitl C Morgan, Curtis Huttenhower

Abstract readReview
In one paragraph

Review in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
99citing papers in PubMed, 2 pooled it
–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

99 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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39 more citing papers are in PubMed but not listed here.

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.

Himel MallickDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Siyuan MaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Eric A FranzosaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Tommi VatanenBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Xochitl C MorganDepartment of Microbiology and Immunology, The University of Otago, Dunedin, New Zealand.
Curtis HuttenhowerDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. chuttenh@hsph.harvard.edu.ORCID 0000-0002-1110-0096

Funding

Characterizing the gut microbial ecosystem for diagnosis and therapy in IBDU54DK102557 · NIDDK · BROAD INSTITUTE, INC. · PI HUTTENHOWER, CURTIS, XAVIER, RAMNIK J · 2014 to 2015
$7.7M
Overcoming bias and unwanted variability in next generation sequencingR01HG005220 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2010 to 2018
$3.6M
Characterizing the gut microbial ecosystem for diagnosis and therapy in IBDU54DE023798 · NIDCR · BROAD INSTITUTE, INC. · PI HUTTENHOWER, CURTIS, XAVIER, RAMNIK J · 2013 to 2014
$3.0M
NHGRI NIH HHS R01 HG005220NIDCR NIH HHS U54 DE023798NIDDK NIH HHS U54 DK102557
6 · The paper itself

Abstract

Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.

Indexed as

MetabolomicsMetagenomicsMicrobiotaProteomicsResearch DesignAnimalsComputational BiologyDNA Barcoding, TaxonomicGene Expression ProfilingHumans

Identifiers

PMID29187204
PMCPMC5708111

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.