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ProteoMill: Efficient network-based functional analysis portal for proteomics data

View ORCID ProfileM Rydén, View ORCID ProfileM Englund, View ORCID ProfileN Ali
doi: https://doi.org/10.1101/2020.11.09.374579
M Rydén
Lund University
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  • For correspondence: martin.ryden@med.lu.se
M Englund
Lund University
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N Ali
Lund University
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Abstract

Functional analysis has become a common approach to incorporate biological knowledge into the analysis of omics data, and to explore molecular events that govern a disease state. It is though only one step in a wider analytical pipeline that typically requires use of multiple individual analysis software. There is currently a need for a well-integrated omics analysis tool that performs all the steps. The ProteoMill portal is developed as an R Shiny application and integrates all necessary steps from data-upload, converting identifiers, to quality control, differential expression and network-based functional analysis into a single fast, interactive easy to use workflow. Further, it maintains annotation data sources up to date, overcoming a common problem with use of outdated information, and seamlessly integrates multiple R-packages for an improved user-experience. The functionality provided in this software can benefit researchers by facilitating the exploratory analysis of proteomics data.

ProteoMill is available for free at https://proteomill.com.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* Shared senior authors

  • https://proteomill.com/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 10, 2020.
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ProteoMill: Efficient network-based functional analysis portal for proteomics data
M Rydén, M Englund, N Ali
bioRxiv 2020.11.09.374579; doi: https://doi.org/10.1101/2020.11.09.374579
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ProteoMill: Efficient network-based functional analysis portal for proteomics data
M Rydén, M Englund, N Ali
bioRxiv 2020.11.09.374579; doi: https://doi.org/10.1101/2020.11.09.374579

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