S2P: A Desktop Application for Fast and Easy Processing of 2D-Gel and MALDI-Based Mass Spectrometry Protein Data

  • Hugo López-Fernández
  • Jose E. Araújo
  • Daniel Glez-Peña
  • Miguel Reboiro-Jato
  • Florentino Fdez-Riverola
  • José L. Capelo-Martínez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 616)

Abstract

2D-gel electrophoresis is widely used in combination with MALDI-TOF mass spectrometry in order to analyse the proteome of biological samples. It can be used to discover proteins that are differentially expressed between two groups (e.g. two disease conditions) obtaining thus a set of potential biomarkers. Biomarker discovery requires a lot of data processing in order to prepare data for analysis or in order to merge data from different sources. This kind of work is usually done manually, being highly time consuming and distracting the operator or researcher from other important tasks. Moreover, doing this repetitive process in a non-automated, handling-based manner is error-prone, affecting reliability and reproducibility. To overcome these drawbacks, the S2P, an AIBench based desktop multiplatform application, has been specifically created to process 2D-gel and MALDI-mass spectrometry protein identification-based data in a computer-aided manner. S2P is open source and free to all users at http://www.sing-group.org/s2p.

Keywords

Protein identification Data processing Bioinformatics tools Open source 2D-gel MALDI-TOF-MS Protein data Mascot identifications 

Notes

Acknowledgements

This work has been partially funded by (i) the “Platform of integration of intelligent techniques for analysis of biomedical information” project (TIN2013-47153-C3-3-R) from Spanish Ministry of Economy and Competitiveness, (ii) the “Discovery of biomarkers for bladder carcinoma diagnosis” project from Nova Medical School, (iii) Unidade de Ciências Biomoleculares Aplicadas-UCIBIO, which is financed by national funds from FCT/MEC/Portugal (UID/Multi/04378/2013), and (iv) Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia) and FEDER (European Union). H. López-Fernández is supported by a post-doctoral fellowship from Xunta de Galicia. J. L. Capelo acknowledges Associação Cientifica ProteoMass for financial support. J. E. Araújo acknowledges the financial support given by the Portuguese Foundation for Science and Technology under doctoral grant number SFRH/BD/109201/2015. SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hugo López-Fernández
    • 1
    • 2
    • 3
  • Jose E. Araújo
    • 3
  • Daniel Glez-Peña
    • 1
    • 2
  • Miguel Reboiro-Jato
    • 1
    • 2
  • Florentino Fdez-Riverola
    • 1
    • 2
  • José L. Capelo-Martínez
    • 3
  1. 1.ESEI - Escuela Superior de Ingeniería InformáticaEdificio Politécnico, Universidad de VigoOurenseSpain
  2. 2.CINBIO - Centro de Investigaciones BiomédicasUniversity of VigoVigoSpain
  3. 3.UCIBIO-REQUIMTE, Departamento de Química, Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

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