Evidence map›Paper›PMID 33248408›Full record

ReviewCurrent opinion in biotechnology2020

Emerging computational tools and models for studying gut microbiota composition and function.

Seo-Young Park, Arinzechukwu Ufondu, Kyongbum Lee, Arul Jayaraman

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current opinion in biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Review
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  3. Article
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  7. Article
  8. Pathogens (Basel, Switzerland) · 2024
    Review
  9. Review
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  11. Article
  12. 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

4 authors.

Seo-Young ParkDepartment of Chemical and Biological Engineering, Tufts University, Medford, MA, USA.
Arinzechukwu UfonduArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA.
Kyongbum LeeDepartment of Chemical and Biological Engineering, Tufts University, Medford, MA, USA. Electronic address: kyongbum.lee@tufts.edu.
Arul JayaramanArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA; Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA. Electronic address: arulj@tamu.edu.

Funding

Microbial flavonoid metabolites as modulators of AD markers in a 3D human neural tissue modelR01AT010282 · NCCIH · TEXAS A&M AGRILIFE RESEARCH · PI CHAPKIN, ROBERT STEPHEN, JAYARAMAN, ARUL · 2018 to 2021
$1.9M
NCCIH NIH HHS R01 AT010282
6 · The paper itself

Abstract

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaHigh-Throughput Nucleotide SequencingHumansMass SpectrometryMetabolome

Identifiers

PMID33248408
PMCPMC7744364

What OpenQuestion holds

Textmetadata
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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.