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Updates on metaQuantome software for quantitative metaproteomics

Authors: Mehta, Subina; Pratik Jagtap;

Updates on metaQuantome software for quantitative metaproteomics

Abstract

metaQuantome is a software suite that enables quantitative analysis, statistical evaluation and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide; the ability to perform statistical analysis on samples from multiple conditions; and comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data point quantitative and statistical analysis so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on multi-omics analysis of microbial communities, we have also initiated use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements metagenomic and metaproteomic analysis already available. For this we have developed a tool named MT2MQ (‘metatranscriptomics to metaQuantome’), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms it so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics will enable multi-point data analysis. These improvements will take us a step towards integrative multi-omic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. Updated metaQuantome and MT2MQ are open source software and available via the Galaxy Toolshed and GitHub.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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