S2P: A software tool to quickly carry out reproducible biomedical research projects involving 2D-gel and MALDI-TOF MS protein data
Introduction
2D-gel electrophoresis and mass spectrometry using matrix assisted laser desorption ionization coupled to time of flight analyzers (MALDI-TOF-MS), are widely used in conjunction in order to perform proteome analysis [1], [2]. In brief, while the comparison of 2D-gels allows obtaining a set of differentially expressed spots, MALDI-TOF-MS allows identifying the proteins separated in such spots.
The scientific community is particularly interested in the challenging task of finding proteins that can be used to differentiate different conditions of health with the aim to aid in the diagnosis, prognosis and development of new targeted therapies [3], [4], [5]. In order to find such proteins, known as biomarkers, a typical experimental workflow combining 2D-gel and MALDI-TOF-MS can involve the following steps: (i) separating the proteins present in a complex proteome; (ii) comparing the 2D-gels across samples to obtain the spots that were found expressed differentially; (iii) excising such spots and treating them for protein identification; (iv) linking the protein identifications to the 2D-gel spots; and (v) performing different types of data analysis to discover the potential biomarkers and extract meaningful biological knowledge. Such workflow generates a large amount of data, which need to be processed before they can be properly analyzed. A considerable part of the aforementioned data processing is usually carried out manually by laboratory researchers (e.g. using text editors and spreadsheet software). However, such a repetitive and non-automated process presents important drawbacks: it is time consuming, it is error-prone, and it tends to lack reliability and reproducibility.
To overcome the aforementioned drawbacks we have developed the S2P software application (http://www.sing-group.org/s2p/), a free software that aims to help researchers overcome these tedious but necessary data processing steps.
S2P was created with a twofold purpose: to improve reproducibility and to save time. Currently, lack of reproducibility is a growing concern in science [6]. The S2P software aims to improve reproducibility by avoiding human errors due to manual data processing. For instance, this issue has been particularly important in recent genomics bioinformatics, where gene name errors have been shown to be widespread in the scientific literature due to the use of Excel [7], [8]. Through its user-friendly GUI interface, S2P dramatically reduces the time that researchers need to invest in order to prepare data for analysis. To the best of our knowledge there is currently no other application offering similar functionalities.
The usefulness of S2P is illustrated by three case studies. The first is a case study experiment that aims to establish a biomarker-based method to allow better diagnosis and monitoring of patients with bladder cancer. The second aims to develop a longitudinal study to unravel the evolution of proteome of the peritoneal dialysate with time, so that biomarkers and molecular profiles for diagnosis and prognosis can be obtained. Finally, the third case study, which demonstrates how S2P has been extended to support new types of data, shows how it can be used to determine the relative abundance of serum protein using Mascot emPAI quantification data.
Section snippets
Case study 1 dataset
The first case study uses a dataset composed of 14 patients plus 1 healthy group of 6 individuals. Plasma samples were collected from 7 anonymous patients diagnosed with bladder cancer, 7 anonymous patients diagnosed with lower urinary tract symptoms (LUTS) and 6 healthy individuals, following standard procedures. All patients and healthy volunteers were informed about the project and their consent was obtained in written form. The local ethics committee approved the study. This experiment was
Results
With the goal of showing the main features of S2P as well as its usefulness to analyze real data, this section shows how it has been used to process and analyze the three case study datasets presented.
Discussion
We provided an overview of the S2P v1.2 software and presented three real-world use cases to illustrate is capabilities. The two main advantages of S2P, when compared to alternative approaches to achieve the same data analysis workflow are time saving and reproducibility. To our knowledge, the unique alternative approach to such workflow is that laboratory researchers do the processing by themselves using text editors and spreadsheet software.
Regarding the time saving, it is important to
Conclusions
S2P (http://www.sing-group.org/s2p/) is freely distributed under license GPLv3, providing a friendly graphical user interface designed to allow researchers to save time in data processing tasks related to 2D-gel electrophoresis and MALDI mass spectrometry protein identification-based data. The usefulness of S2P has been demonstrated by its application to real experiments, where it notably speeds up data processing as well as improves experiment reproducibility and reliability. S2P is open to
Mode of availability
The S2P software is licensed under a GNU GPL 3.0 License (http://www.gnu.org/copyleft/gpl.html). The S2P software along with full documentation and training tutorials are free and publicly available at http://www.sing-group.org/s2p.
Authors’ contributions
HL-F, JEA, HMS, and JLC conceived, coordinated and designed S2P. HL-F, DG-P, and MR-J developed S2P. HL-F, JEA, SJ, and HMS tested the application. HL-F created the S2P website. JEA, SJ, and HMS performed the laboratory experiments. HL-F, JEA, HMS, and FF-R wrote the manuscript. FF-R, DG-P, MR-J, and JLC drafted the manuscript critically. All authors read and approved the final version of the manuscript.
Acknowledgements
We are thankful to Doctors Pedro M. Baltazar, Luís Campos Pinheiro, and Fernando Calais da Silva from the Urology Service at Centro Hospital de Lisboa Central (Lisboa, Portugal) for providing case study 1 samples. We are also thankful to Doctors Fernando Teixeira e Costa and Aura Ramos from the Nephrology Department at Hospital Garcia de Orta (Almada, Portugal) for providing case study 2 samples. SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of
Funding
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 (Nova Health Project Pilot 01); (iii) the “Biomarkers for rheumatic inflammatory diseases diagnosis” project from Nova Medical School (Nova Health Project Pilot 02); (iv) the
Conflict of interest statement
The authors have no conflicts of interest to declare.
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These authors contributed equally to this work.