Document Type

Article

Publication Date

2017

DOI

10.3390/proteomes6010002

Publication Title

Proteomes

Volume

6

Issue

1

Pages

1-17

Abstract

Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed "MetaGOmics") that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis-including comparing the functional makeup between samples.

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Original Publication Citation

Riffle, M., May, D. H., Timmins-Schiffman, E., Mikan, M. P., Jaschob, D., Noble, W. S., & Nunn, B. L. (2017). MetaGOmics: A web-based tool for peptide-centric functional and taxonomic analysis of metaproteomics data. Proteomes, 6(1), 1-17. doi:10.3390/proteomes6010002

ORCID

0000-0002-2478-6541 (Emma Timmins-Schiffman), 0000-0002-7361-4359 (Brook Nunn)

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