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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
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ProteoSign v2: A faster and evolved user-friendly online tool for statistical analyses of differential proteomics

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Author
Theodorakis E., Antonakis A.N., Baltsavia I., Pavlopoulos G.A., Samiotaki M., Amoutzias G.D., Theodosiou T., Acuto O., Efstathiou G., Iliopoulos I.
Date
2021
Language
en
DOI
10.1093/nar/gkab329
Keyword
article
bioinformatics
DNA microarray
experimental design
human
proteomics
shipyard worker
velocity
genetics
Internet
mass spectrometry
metabolism
procedures
proteomics
software
statistical analysis
protein
Data Interpretation, Statistical
Internet
Mass Spectrometry
Proteins
Proteomics
Software
Oxford University Press
Metadata display
Abstract
Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign. © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.
URI
http://hdl.handle.net/11615/79672
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