1. ProteoSign: an end-user online differential proteomics statistical analysis platform
- Author
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Efstathiou, Georgios, Antonakis, Andreas N, Pavlopoulos, Georgios A, Theodosiou, Theodosios, Divanach, Peter, Trudgian, David C, Thomas, Benjamin, Papanikolaou, Nikolas, Aivaliotis, Michalis, Acuto, Oreste, and Iliopoulos, Ioannis
- Subjects
Bioengineering ,Networking and Information Technology R&D (NITRD) ,Underpinning research ,1.5 Resources and infrastructure (underpinning) ,Generic health relevance ,Data Interpretation ,Statistical ,Internet ,Mass Spectrometry ,Proteomics ,Software ,Environmental Sciences ,Biological Sciences ,Information and Computing Sciences ,Developmental Biology - Abstract
Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a user-friendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ProteoSign.
- Published
- 2017