Evidence map›Paper›PMID 28289558›Full record

ArticlePeerJ2017

Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets.

Alex D Washburne, Justin D Silverman, Jonathan W Leff, Dominic J Bennett, John L Darcy, Sayan Mukherjee, Noah Fierer, Lawrence A David

Abstract read
In one paragraph

Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 1 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

60 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Constructing phylogenetic trees for microbiome data analysis: A mini-review.Computational and structural biotechnology journal · 2024
    Review
  8. A test for microbiome-mediated rescue via host phenotypic plasticity inbioRxiv : the preprint server for biology · 2024
    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

8 authors.

Alex D WashburneNicholas School of the Environment, Duke University , Durham , NC , United States.
Justin D SilvermanProgram for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Medical Scientist Training Program, Duke University, Durham, NC, United States; Center for Genomic and Computational Biology, Duke University, Durham, NC, United States; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States.
Jonathan W LeffCooperative Institute for Research in Environmental Sciences, University of Colorado , Boulder , CO , United States.
Dominic J BennettDepartment of Earth Science and Engineering, Imperial College London, London, United Kingdom; Institute of Zoology, Zoological Society of London, London, United Kingdom.
John L DarcyDepartment of Ecology and Evolution, University of Colorado Boulder , Boulder , CO , United States.
Sayan MukherjeeProgram for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Department of Statistical Science, Mathematics, and Computer Science, Duke University, Durham, NC, United States.
Noah FiererCooperative Institute for Research in Environmental Sciences, University of Colorado , Boulder , CO , United States.
Lawrence A DavidProgram for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Center for Genomic and Computational Biology, Duke University, Durham, NC, United States; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007171 · NIGMS · DUKE UNIVERSITY · PI KONTOS, CHRISTOPHER D · 1985 to 2021
$31.2M
Medical Scientist Training Program Training GrantT32GM145449 · NIGMS · DUKE UNIVERSITY · PI Christopher D Kontos · 2022 to 2026
$6.6M
Computational Biology and Bioinformatics Training ProgramT32GM071340 · NIGMS · DUKE UNIVERSITY · PI MAGWENE, PAUL MITAARI · 2005 to 2020
$3.3M
NIGMS NIH HHS T32 GM007171NIGMS NIH HHS T32 GM071340NIGMS NIH HHS T32 GM145449
6 · The paper itself

Abstract

Marker gene sequencing of microbial communities has generated big datasets of microbial relative abundances varying across environmental conditions, sample sites and treatments. These data often come with putative phylogenies, providing unique opportunities to investigate how shared evolutionary history affects microbial abundance patterns. Here, we present a method to identify the phylogenetic factors driving patterns in microbial community composition. We use the method, "phylofactorization," to re-analyze datasets from the human body and soil microbial communities, demonstrating how phylofactorization is a dimensionality-reducing tool, an ordination-visualization tool, and an inferential tool for identifying edges in the phylogeny along which putative functional ecological traits may have arisen.

Indexed as

Community phylogeneticsCompositional dataFactor analysisMicrobial biogeographyMicrobiomePhylofactorizationSequence-count data

Identifiers

PMID28289558
PMCPMC5345826

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.