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. MetaGOmics is freely available at https://www.yeastrc.org/metagomics/.
Highlights
IntroductionThese advancements have fueled the growth of meta-omics as a field for characterizing the metagenomes, metatranscriptomes, meta-metabolomes, and metaproteomes of environmental and microbiome samples comprising a taxonomically diverse (often uncharacterized) community of organisms [1,2,3]
Recent years have seen tremendous advancements in the availability of high-throughput “omics”technologies for characterizing complex biological samples
The user is presented with a simple interface for creating an initial context for the analysis of metaproteomics data (Figure 2a)
Summary
These advancements have fueled the growth of meta-omics as a field for characterizing the metagenomes, metatranscriptomes, meta-metabolomes, and metaproteomes of environmental and microbiome samples comprising a taxonomically diverse (often uncharacterized) community of organisms [1,2,3]. Metagenomics examines questions related to taxonomic composition and genomic architecture of organisms in the sample [4,5]. Meta-metabolomics examines which metabolites are being produced and how those change in response to environmental factors [6]. Meta-transcriptomics aims to use gene expression of mRNA transcripts to track taxonomic and functional abundance [7]. Transcript and actual protein levels can be poorly correlated because of codon bias, differing rates of protein turnover, and other factors [8,9]
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