Literature DB >> 27183115

The information science of microbial ecology.

Aria S Hahn1, Kishori M Konwar2, Stilianos Louca3, Niels W Hanson4, Steven J Hallam5.   

Abstract

A revolution is unfolding in microbial ecology where petabytes of 'multi-omics' data are produced using next generation sequencing and mass spectrometry platforms. This cornucopia of biological information has enormous potential to reveal the hidden metabolic powers of microbial communities in natural and engineered ecosystems. However, to realize this potential, the development of new technologies and interpretative frameworks grounded in ecological design principles are needed to overcome computational and analytical bottlenecks. Here we explore the relationship between microbial ecology and information science in the era of cloud-based computation. We consider microorganisms as individual information processing units implementing a distributed metabolic algorithm and describe developments in ecoinformatics and ubiquitous computing with the potential to eliminate bottlenecks and empower knowledge creation and translation.
Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mesh:

Year:  2016        PMID: 27183115     DOI: 10.1016/j.mib.2016.04.014

Source DB:  PubMed          Journal:  Curr Opin Microbiol        ISSN: 1369-5274            Impact factor:   7.934


  8 in total

1.  Draft Genome Sequences of Novel Pseudomonas, Flavobacterium, and Sediminibacterium [corrected] Strains from a Freshwater Ecosystem.

Authors:  Federica Pinto; Adrian Tett; Federica Armanini; Francesco Asnicar; Adriano Boscaini; Edoardo Pasolli; Moreno Zolfo; Claudio Donati; Nico Salmaso; Nicola Segata
Journal:  Genome Announc       Date:  2018-02-01

2.  A compendium of multi-omic sequence information from the Saanich Inlet water column.

Authors:  Alyse K Hawley; Mónica Torres-Beltrán; Elena Zaikova; David A Walsh; Andreas Mueller; Melanie Scofield; Sam Kheirandish; Chris Payne; Larysa Pakhomova; Maya Bhatia; Olena Shevchuk; Esther A Gies; Diane Fairley; Stephanie A Malfatti; Angela D Norbeck; Heather M Brewer; Ljiljana Pasa-Tolic; Tijana Glavina Del Rio; Curtis A Suttle; Susannah Tringe; Steven J Hallam
Journal:  Sci Data       Date:  2017-10-31       Impact factor: 6.444

Review 3.  Model Microbial Consortia as Tools for Understanding Complex Microbial Communities.

Authors:  Shin Haruta; Kyosuke Yamamoto
Journal:  Curr Genomics       Date:  2018-12       Impact factor: 2.236

4.  An integrated, modular approach to data science education in microbiology.

Authors:  Kimberly A Dill-McFarland; Stephan G König; Florent Mazel; David C Oliver; Lisa M McEwen; Kris Y Hong; Steven J Hallam
Journal:  PLoS Comput Biol       Date:  2021-02-25       Impact factor: 4.475

Review 5.  Inferring microbiota functions from taxonomic genes: a review.

Authors:  Christophe Djemiel; Pierre-Alain Maron; Sébastien Terrat; Samuel Dequiedt; Aurélien Cottin; Lionel Ranjard
Journal:  Gigascience       Date:  2022-01-12       Impact factor: 6.524

6.  Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia.

Authors:  James J Winkle; Bhargav R Karamched; Matthew R Bennett; William Ott; Krešimir Josić
Journal:  PLoS Comput Biol       Date:  2021-09-22       Impact factor: 4.475

7.  Molecular ecological network analysis of the response of soil microbial communities to depth gradients in farmland soils.

Authors:  Hang Yu; Dongmei Xue; Yidong Wang; Wei Zheng; Guilong Zhang; Zhong-Liang Wang
Journal:  Microbiologyopen       Date:  2020-01-05       Impact factor: 3.139

8.  Metabolic pathway inference using multi-label classification with rich pathway features.

Authors:  Abdur Rahman M A Basher; Ryan J McLaughlin; Steven J Hallam
Journal:  PLoS Comput Biol       Date:  2020-10-01       Impact factor: 4.475

  8 in total

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