Project: A whole new role for Unipept: a framework for comparative metaproteomics analyses
2019-10-01 – 2022-08-31
- Abstract
Metaproteomics is a maturing research discipline that can be used to look at the protein contents of samples taken from environments such as the human gut, soil, or sludge. Based on protein fragments (peptides) found in the samples, we can try to infer which proteins the peptides originate from, which (micro)organisms created them and what functions they perform. This way, we can gain insight into the processes that take place in ecosystems, find out about disruptions and discover potential solutions to resolve imbalances.
Unfortunately, the available software to analyse this wealth of data is clearly lagging behind the technical advances in the field. To remedy this, we developed Unipept, a user-friendly web application that enables researchers to easily explore their data using interactive data visualisations. Originally, we were primarily interested in sample biodiversity, but recently we have shifted our focus to the functional composition and the link between organisms and functions.
The analyses in Unipept are done on a per-sample basis, but to get the most out of the data, it is necessary to be able to compare multiple samples and detect shifts in composition. This research proposal aims to add such comparative analysis to Unipept in an easy to use, but statistically-correct way. This will make it possible to, for example, compare healthy with diseased patients or to track the effectiveness of an intervention.
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- Journal Article
- A1
- open access
UMGAP : the Unipept MetaGenomics analysis pipeline
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Pout2Prot : an efficient tool to create protein (sub)groups from Percolator Output Files
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- Journal Article
- A1
- open access
FragGeneScanRs : faster gene prediction for short reads
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- Journal Article
- A1
- open access
Unipept visualizations : an interactive visualization library for biological data
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- Journal Article
- A1
- open access
MegaGO : a fast yet powerful approach to assess functional gene ontology similarity across meta-omics data sets