Evidence map›Paper›PMID 27081850›Full record

ArticlePLoS computational biology2016

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

Brian R Granger, Yi-Chien Chang, Yan Wang, Charles DeLisi, Daniel Segrè, Zhenjun Hu

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

14 citing papers in PubMed, 33 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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  6. Article
  7. Metabolic modelling approaches for describing and engineering microbial communities.Computational and structural biotechnology journal · 2021
    Review
  8. Article
  9. Regularized S-Map Reveals Varying Bacterial Interactions.Applied and environmental microbiology · 2020
    Article
  10. Review
  11. Review
  12. Article
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  14. Review
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 2 institutions in 1 country.

Brian R GrangerBioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
Yi-Chien ChangBioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
Yan WangBioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
Charles DeLisiBioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
Daniel SegrèBioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
Zhenjun HuCenter for Advanced Genomic Technology, Boston University, Boston, Massachusetts, United States of America.
Boston University · USAdvanced Genomic Technology (United States) · US

Funding

SPECIAL ASSAY COREP30DK036836 · NIDDK · JOSLIN DIABETES CENTER · PI JEAN E. SCHAFFER · 1986 to 2026
$50.5M
Cultivation, Nature, Ecology and Pathogenicity of the Uncultivable Oral MicrobiomR01DE024468 · NIDCR · ADA FORSYTH INSTITUTE, INC. · PI ALM, ERIC JOHN, DEWHIRST, FLOYD E · 2014 to 2018
$7.3M
SciBay: A New Methodology for Scientific Collaboration and Gene Function DetermiRC2GM092602 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KASIF, SIMON, ROBERTS, RICHARD J · 2009 to 2010
$4.0M
Visant-Predictome: A System for Integration, Mining, Visualization and AnalysisR01GM103502 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI DELISI, CHARLES, HU, ZHENJUN · 2012 to 2015
$3.0M
Computation and functional significance of multi-phenotype genetic interaction maR01GM089978 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI DUDLEY, AIMEE M, SEGRE, DANIEL · 2010 to 2014
$2.3M
NIDCR NIH HHS 5R01DE024468NIDCR NIH HHS R01 DE024468NIDDK NIH HHS P30 DK036836NIGMS NIH HHS 1RC2GM092602-01NIGMS NIH HHS R01 GM089978NIGMS NIH HHS R01GM089978NIGMS NIH HHS R01 GM103502NIGMS NIH HHS R01GM103502-05NIGMS NIH HHS RC2 GM092602
6 · The paper itself

Abstract

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Indexed as

Metabolic Networks and PathwaysModels, BiologicalSoftwareComputational BiologyComputer GraphicsComputer SimulationHumansMicrobial ConsortiaMicrobiotaSystems Biology

Identifiers

PMID27081850
PMCPMC4833320
OpenAlexW2341446847

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.