Computational and Structural Biotechnology JournalBioinformaticsPeer-reviewed journal article
iSanXoT: a Standalone Application for the Integrative Analysis of Mass Spectrometry-Based Quantitative Proteomics Data
Jose Manuel Rodríguez, Inmaculada JorgeiD, Ana Martínez‐ValiD, Rafael Barrero-RodrígueziD, Ricardo Magni, Estefanía NúñeziD, Andrea Laguillo-GómeziD, Cristina A. DevesaiD, Juan Antonio LópeziD, Emilio CamafeitaiD, Jesús VázqueziD
Abstract
Numerous bioinformatics tools currently exist to perform quantitative analysis of proteomics experiments. The majority of these tools apply diverse statistical models to assign a quantitative protein value from the mass-spectrometry information. Here we present iSanXoT, a standalone application that allows integrative analysis of quantitative proteomics data. iSanXoT processes relative abundances between MS signals and integrates them sequentially to upper levels using our previously published Generic Integration Algorithm (GIA). iSanXoT offers unique capabilities that complement conventional quantitative softwares, including statistical weighting and independent modeling of error distributions in each integration, aggregation of technical or biological replicates, quantification of posttranslational modifications or analysis of coordinated protein behavior. iSanXoT is a standalone, user-friendly application which accepts output from widespread proteomics pipelines and enables free construction of quantification workflows and fully customizable reports than can be reused across different projects or shared among users. Diverse integrative workflows constructed using GIA for the analysis of high-throughput quantitative proteomics experiments have been successfully applied in numerous publications. iSanXoT has been tested with the main operating systems. Download links for the corresponding distributions are available at https://github.com/CNIC-Proteomics/iSanXoT/releases.
- assay: proteomics
- research focus: software / tool
- research focus: benchmarking
- research focus: dimensionality reduction
- approach: statistical model
- Citations
- 36
- FWCI
- 3.32
- Field percentile
- top 5%
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