Evidence map›Paper›PMID 40524188›Full record

ReviewGenome biology2025

A survey of computational approaches for characterizing microbial interactions in microbial mats.

Vanesa L Perillo, Michael Nute, Nicolae Sapoval, Kristen D Curry, Logan Golia, Yongze Yin, Huw A Ogilvie, Luay Nakhleh, Santiago Segarra, Devaki Bhaya and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Vanesa L Perillo *Instituto Argentino de Oceanografía (IADO-CONICET-UNS), Camino La Carrindanga Km 7 E1, Bahía Blanca, B8000CPB, Argentina. vperillo@criba.edu.ar.ORCID http://orcid.org/0000-0002-6031-3119
Michael Nute *Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA. mn56@rice.edu.
Nicolae SapovalDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Kristen D CurryDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Logan GoliaDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Yongze YinDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Huw A OgilvieDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Luay NakhlehDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Santiago SegarraDepartment of Electrical and Computer Engineering, Rice University, 6100 Main Street, MS 380, Houston, TX, 77005, USA.
Devaki BhayaDepartment of Plant Biology, Carnegie Institution for Science, 260 Panama St, Stanford, CA, 94305, USA.
Diana G CuadradoInstituto Argentino de Oceanografía (IADO-CONICET-UNS), Camino La Carrindanga Km 7 E1, Bahía Blanca, B8000CPB, Argentina.
Todd J TreangenDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.

Funding

Fondo para la Investigación Científica y Tecnológica PICT 2020-302Joint Genome Institute proposal: 10.46936/10.25585/60001132Joint Genome Institute Proposal 503441Ministerio de Ciencia, Tecnología e Innovación Desarrollo e Innovación en Ciencias del Mar"Ministerio de Ciencia, Tecnología e Innovación Pampa Azul A8 Programa "InvestigaciónNational Science Foundation BBSRC-NSF/BIO #1921429National Science Foundation EF-2126387National Science Foundation NSF#2125965
6 · The paper itself

Abstract

In this review, we use microbial mat communities as a general model system to highlight the strengths and limitations of current computational methods for analyzing interactions between members of microbial ecosystems. We describe the factors that make this environment have such a high degree of interaction, and we explore different categories of both laboratory and computational tools for studying these interactions. For each tool, we describe efforts to apply them to microbial mats in the past and, in the process, argue that genome-scale metabolic models have breakthrough potential for modeling microbial interactions in microbial mats.

Indexed as

Computational BiologyMicrobial InteractionsMicrobiotaBacteriaEcosystemComparative genomicsComputational modelsEnvironmental dataMetabolic modelsMeta-omicsMicrobial ecologyMicrobial evolutionMicrobial matsTransdisciplinary studies

Identifiers

PMID40524188
PMCPMC12168310

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.