Abstract
SummaryMass spectrometry-based proteomics has had a formidable development in recent years, increasing the amount of data handled and the complexity of the statistical resources needed. Here we present SanXoT, an open-source, standalone software package for the statistical analysis of high-throughput, quantitative proteomics experiments. SanXoT is based on our previously developed weighted spectrum, peptide and protein statistical model and has been specifically designed to be modular, scalable and user-configurable. SanXoT allows limitless workflows that adapt to most experimental setups, including quantitative protein analysis in multiple experiments, systems biology, quantification of post-translational modifications and comparison and merging of experimental data from technical or biological replicates.Availability and implementationDownload links for the SanXoT Software Package, source code and documentation are available at https://wikis.cnic.es/proteomica/index.php/SSP.Contact jvazquez@cnic.es or ebonzon@cnic.esSupplementary information Supplementary information is available at Bioinformatics online.
Highlights
Current high-throughput quantitative proteomics presents many bioinformatic challenges, especially in the case of stable isotope-based techniques. Several of these problems have been highlighted in the literature, such as the problem of undersampling (Nilsson et al, 2010), the need for a null hypothesis (Arntzen et al, 2011; Karp et al, 2010; Lin et al, 2006), the proteome dynamic range (Zubarev, 2013), the non-normality of protein abundance change distributions (Karp et al, 2010) and the need for quality control measures
Most of these issues were addressed by the weighted spectrum, peptide and protein (WSPP) statistical model (Bonzon-Kulichenko et al, 2011a; Garcıa-Marques et al, 2016; Jorge et al, 2014; Navarro et al, 2014)
WSPP models the error structure of the data generated by the mass spectrometer and integrates the quantitative results into peptide values using weighted averages according to error propagation theory
Summary
Current high-throughput quantitative proteomics presents many bioinformatic challenges, especially in the case of stable isotope-based techniques Several of these problems have been highlighted in the literature, such as the problem of undersampling (Nilsson et al, 2010), the need for a null hypothesis (Arntzen et al, 2011; Karp et al, 2010; Lin et al, 2006), the proteome dynamic range (Zubarev, 2013), the non-normality of protein abundance change distributions (Karp et al, 2010) and the need for quality control measures. Most of these issues were addressed by the weighted spectrum, peptide and protein (WSPP) statistical model (Bonzon-Kulichenko et al, 2011a; Garcıa-Marques et al, 2016; Jorge et al, 2014; Navarro et al, 2014).
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