Literature DB >> 19910409

Integrating multiple 'omics' analysis for microbial biology: application and methodologies.

Weiwen Zhang1, Feng Li2, Lei Nie3.   

Abstract

Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed.

Mesh:

Year:  2009        PMID: 19910409     DOI: 10.1099/mic.0.034793-0

Source DB:  PubMed          Journal:  Microbiology (Reading)        ISSN: 1350-0872            Impact factor:   2.777


  123 in total

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Journal:  Nucleic Acids Res       Date:  2014-11-11       Impact factor: 16.971

2.  Optimizing high dimensional gene expression studies for immune response following smallpox vaccination using Taqman® low density immune arrays.

Authors:  Ann L Oberg; Neelam Dhiman; Diane E Grill; Jenna E Ryan; Richard B Kennedy; Gregory A Poland
Journal:  J Immunol Methods       Date:  2011-01-28       Impact factor: 2.303

3.  Effect of antibiotic treatment on the intestinal metabolome.

Authors:  L Caetano M Antunes; Jun Han; Rosana B R Ferreira; Petra Lolić; Christoph H Borchers; B Brett Finlay
Journal:  Antimicrob Agents Chemother       Date:  2011-01-31       Impact factor: 5.191

Review 4.  Agrigenomics for microalgal biofuel production: an overview of various bioinformatics resources and recent studies to link OMICS to bioenergy and bioeconomy.

Authors:  Namrata Misra; Prasanna Kumar Panda; Bikram Kumar Parida
Journal:  OMICS       Date:  2013-09-17

Review 5.  Systems vaccinology: learning to compute the behavior of vaccine induced immunity.

Authors:  Helder I Nakaya; Shuzhao Li; Bali Pulendran
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2011-10-19

6.  Development of an ecophysiological model for Diplosphaera colotermitum TAV2, a termite hindgut Verrucomicrobium.

Authors:  Jantiya Isanapong; W Sealy Hambright; Austin G Willis; Atcha Boonmee; Stephen J Callister; Kristin E Burnum; Ljiljana Paša-Tolić; Carrie D Nicora; John T Wertz; Thomas M Schmidt; Jorge Lm Rodrigues
Journal:  ISME J       Date:  2013-05-09       Impact factor: 10.302

7.  Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.

Authors:  Oliver P Günther; Heesun Shin; Raymond T Ng; W Robert McMaster; Bruce M McManus; Paul A Keown; Scott J Tebbutt; Kim-Anh Lê Cao
Journal:  OMICS       Date:  2014-11

8.  Global relationships in fluctuation and response in adaptive evolution.

Authors:  Chikara Furusawa; Kunihiko Kaneko
Journal:  J R Soc Interface       Date:  2015-08-06       Impact factor: 4.118

Review 9.  Analysis of omics data with genome-scale models of metabolism.

Authors:  Daniel R Hyduke; Nathan E Lewis; Bernhard Ø Palsson
Journal:  Mol Biosyst       Date:  2012-12-18

10.  Refinement of the Listeria monocytogenes σB regulon through quantitative proteomic analysis.

Authors:  S Mujahid; R H Orsi; P Vangay; K J Boor; M Wiedmann
Journal:  Microbiology       Date:  2013-04-25       Impact factor: 2.777

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