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iSanXoT

This repository contains the source code and the distribution files for the iSanXoT application.

This application executes several kind of workflows for quantitative high-throughput proteomics, systems biology and the statistical analysis, integration and comparison of experiments.

iSanXoT was developed by the Cardiovascular Proteomics Lab/Proteomic Unit at The National Centre for Cardiovascular Research (CNIC, https://www.cnic.es).

This application is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) License. For further details, read the https://creativecommons.org/licenses/by-nd/4.0/.

Download

The multiple releases are available in the "release" section, located in the following link:

https://github.com/CNIC-Proteomics/iSanXoT/releases

Installation

Available operating systems

iSanXoT maintains the following operating systems and architectures and may add additional ones in the future:

  • Windows 10 Pro (x64)
  • MacOs High Sierra (10.13.6)
  • Ubuntu 20.04 (x64)

For more details, read the Installation section in the web documentation.

Help and Documentations

You can read the help and the user guide in the iSanXoT web documentation.

In addition, you can download the iSanXoT User Guides for each release in PDF format:

Publications

If you find iSanXoT useful, please consider citing our latest publication:

[1] Rodríguez, J. M., Jorge, I., Martinez-Val, A., Barrero-Rodríguez, R., Magni, R., Núñez, E., Laguillo, A., Devesa, C. A., López, J. A., Camafeita, E., & Vázquez, J. (2023). iSanXoT: A standalone application for the integrative analysis of mass spectrometry-based quantitative proteomics data. Computational and structural biotechnology journal, 23, 452–459. https://doi.org/10.1016/j.csbj.2023.12.034

[2] Trevisan-Herraz, M., Bagwan, N., García-Marqués, F., Rodriguez, J. M., Jorge, I., Ezkurdia, I., Bonzon-Kulichenko, E., & Vázquez, J. (2019). SanXoT: a modular and versatile package for the quantitative analysis of high-throughput proteomics experiments. Bioinformatics (Oxford, England), 35(9), 1594–1596. https://doi.org/10.1093/bioinformatics/bty815

[3] García-Marqués, F., Trevisan-Herraz, M., Martínez-Martínez, S., Camafeita, E., Jorge, I., Lopez, J. A., Méndez-Barbero, N., Méndez-Ferrer, S., Del Pozo, M. A., Ibáñez, B., Andrés, V., Sánchez-Madrid, F., Redondo, J. M., Bonzon-Kulichenko, E., & Vázquez, J. (2016). A Novel Systems-Biology Algorithm for the Analysis of Coordinated Protein Responses Using Quantitative Proteomics. Molecular & cellular proteomics : MCP, 15(5), 1740–1760. https://doi.org/10.1074/mcp.M115.055905

[4] Navarro, P., Trevisan-Herraz, M., Bonzon-Kulichenko, E., Núñez, E., Martínez-Acedo, P., Pérez-Hernández, D., Jorge, I., Mesa, R., Calvo, E., Carrascal, M., Hernáez, M. L., García, F., Bárcena, J. A., Ashman, K., Abian, J., Gil, C., Redondo, J. M., & Vázquez, J. (2014). General statistical framework for quantitative proteomics by stable isotope labeling. Journal of proteome research, 13(3), 1234–1247. https://doi.org/10.1021/pr4006958