第11届计算生物学与生物信息学实践应用国际会议论文集

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"第11届国际计算在实际应用中的研讨会论文集是一本汇集了智能系统和计算领域最新研究成果的英文电子书，由Florentino Fdez-Riverola、Mohd Saberi Mohammad、Miguel Rocha、Juan F. De Paz和Tiago Pinto共同担任编辑。该会议专注于计算生物学与生物信息学的实际应用，是'智能系统与计算进展'系列的一部分，由Janusz Kacprzyk（波兰科学院，华沙）担任系列编辑。 '智能系统与计算进展'系列旨在发布有关智能系统理论、应用和设计方法的学术出版物，涵盖了工程、自然科学、计算机及信息科学、信息技术（ICT）、经济、商业、电子商务、环境、健康医疗和生命科学等多个领域，展示了现代智能系统和计算的广泛课题。本书收录的内容主要为重要的会议论文和教材，反映了该领域基础研究和实用技术的最新进展。参加第11届国际会议的学者们分享了他们在计算生物学与生物信息学实践中的创新成果，这些成果不仅推动了理论的发展，还为实际问题的解决提供了强有力的工具和技术支持。读者可以借此了解前沿的研究动态，探索如何将计算方法应用于生物领域，以及如何通过智能化手段改进现有的业务流程和决策支持系统。通过阅读这本书，研究人员、工程师、学生和行业专业人士能够拓宽视野，掌握最新的算法、数据处理技术以及如何将它们转化为实际的解决方案，对于提升科学研究水平和推动产业进步具有重要意义。"

S2P: A Desktop Application for Fast and Easy

Processing of 2D-Gel and MALDI-Based Mass

Spectrometry Protein Data

Hugo López-Fernández

1,2,3

(

✉

)

, Jose E. Araújo

, Daniel Glez-Peña

1,2

Miguel Reboiro-Jato

1,2

, Florentino Fdez-Riverola

1,2

, and José L. Capelo-Martínez

ESEI - Escuela Superior de Ingeniería Informática, Ediﬁcio Politécnico, Universidad de Vigo,

Campus Universitario As Lagoas s/n, 32004 Ourense, Spain

{hlfernandez,dgpena,mrjato,riverola}@uvigo.es

CINBIO - Centro de Investigaciones Biomédicas, University of Vigo,

Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain

UCIBIO-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia,

Universidade Nova de Lisboa, 2829-516 Caparica, Portugal

{jeduardoaraujo,jlcapelom}@bioscopegroup.org

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

1 Introduction

2D-gel electrophoresis and mass spectrometry using matrix assisted laser desorption

ionization coupled to time of ﬂight analysers, 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 to identify the proteins separated in such spots.

F. Fdez-Riverola et al. (eds.), 11th International Conference on Practical

Applications of Computational Biology & Bioinformatics, Advances in Intelligent

Systems and Computing 616, DOI 10.1007/978-3-319-60816-7_1

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 diagnosis, prognosis and new targeted therapies development [3–5]. 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) separation of 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 ﬁnd out the potential

biomarkers. Such workﬂow generates a large amount of data, which need to be processed

before it can be properly analysed. A considerable part of the aforementioned data

processing is usually carried out manually by laboratory researchers (e.g. using text

editors and Excel). However, doing this repetitive process in a non-automated way

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 overcoming these tedious but necessary data processing steps.

S2P has been created with two main goals in mind: to improve reproducibility and

to save time. Nowadays, lack of reproducibility is a growing concern in science [6] and

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 it has been demonstrated that gene name errors are

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 get data ready for analysis. The usefulness of S2P is illustrated by a case

study experiment that aims to establish a biomarker-based method to allow better diag‐

nosis and monitoring of patients with bladder cancer.

The rest of the paper is structured as follows. Section 2 presents the case study and

the most relevant implementation details. Section 3 reviews the results, showing how

to use S2P to process the case study dataset. Finally, Sect. 4 concludes the paper and

outlines future research work.

2 Materials and Methods

2.1 Case Study

As a case study, a dataset composed by 14 patients plus 1 healthy group of 6 individuals

was used. Plasma samples from 7 anonymous patients diagnosed with bladder cancer,

7 anonymous patients diagnosed with lower urinary tract symptoms (LUTS) and 6

healthy individuals were collected following standard procedures. Both 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

developed as a proof of concept to diﬀerentiate bladder cancer from LUTS.

2 H. López-Fernández et al.

Once in the laboratory, the samples were centrifuged, and then the supernatant was

withdrawn, aliquoted and stored at –80 °C until analysis. Most abundant proteins

(MAPs) in plasma can mask or interfere with the detection of proteins belonging to the

low-abundance protein fraction [9]. To avoid this problem, protein equalization from

plasma samples was performed with dithiothreitol, DTT, according to the protocol

described by Warder et al. [10] with minor modiﬁcations as described by Fernández

et al. [11] and Araújo et al. [12–14]. This process was performed with ﬁve replicates for

each patient. Then, the total protein content was determined using a Bradford protein

assay [15].

Two dimensional gel electrophoresis separation was carried out by duplicate for each

patient and for the healthy pool. Then, 2D-gels obtained for each patient and the pool

of healthy volunteers were compared using the Progenesis SameSpots software v4.0

(NonLinear Dynamics) to ﬁnd out the diﬀerentially expressed proteins. All spots of

interest were excised and subjected to in-gel protein(s) digestion and then to protein

ﬁngerprint identiﬁcation by mass spectrometry using MALDI-TOF-MS [16]. Finally,

S2P was used to process the spots data (i.e. diﬀerentially expressed spots) obtained with

the SameSpots software as well as to analyze them along with the protein identiﬁcations

obtained from Mascot.

2.2 Implementation

S2P v1.0.0 is implemented in Java and it was constructed using the AIBench framework

[17], which has been demonstrated to be suitable for rapid development of scientiﬁc

applications [18, 19]. The Graphical User Interface (GUI) was constructed in Java Swing

using freely available extensions such as SwingX or GC4S. S2P also makes use of several

well-established open-source libraries such as JFreeChart, charts4j, iText and the

Apache Commons Mathematics library.

The source code of the project is freely available at https://github.com/sing-

group/S2P under a GNU GPL 3.0 License (http://www.gnu.org/copyleft/gpl.html). It is

divided into three modules: (i) core, which contains the default implementation API, (ii)

gui, which contains several reusable GUI components, and (iii) aibench, which contains

a GUI application based on the AIBench framework.

3 Results and Discussion

With the goal of showing the main features of S2P as well as its usefulness to analyse

real data, this section shows how it has been used to process and analyse the case study

data presented.

Figure 1 illustrates the ﬁve main steps where S2P was used through the experiments:

(1) to merge the SameSpots report into a single table where all samples can be compared;

(2) to design the MALDI plate; (3) to load and ﬁlter the Mascot identiﬁcations; (4) to

link the Mascot identiﬁcations with their corresponding spots using the MALDI plate;

and (5) to examine and analyse spots data along with Mascot identiﬁcations. All data

S2P: A Desktop Application for Fast and Easy Processing 3

to recover this information. For these two reasons, S2P incorporates a MALDI plate

editor that allows the storage of digital copies of experiments’ plates as well as print

them into PDF ﬁles (Step 2 of Fig. 1). S2P also allows to automatically ﬁlling the plate

using a set of previously loaded spots (Step 1 of Fig. 1), allowing the user to deﬁne

parameters such as matrix dimensions (i.e. number of rows and columns) or the number

of replicates of each spot. In our case study, S2P was used to create the MALDI plate

and to obtain a printed copy of it that is used to guide the experimental work.

Once the MALDI-TOF MS analysis was done, the MALDI-based spectra of the

digested protein(s) were submitted to Mascot in order to identify the proteins. Then,

they were exported into a HTML ﬁle that was loaded into S2P in order to remove dupli‐

cated entries and exclude identiﬁcations with a Mascot score under 56 (Step 3 of Fig. 1).

This processed list of Mascot identiﬁcations was exported into a CSV ﬁle so that it can

be directly loaded into S2P later or used in other applications (e.g. Excel). Then, these

Mascot identiﬁcations integrated with the spots data using the MALDI plate (Step 4 of

Fig. 1) to know which identiﬁcations are associated with each spot.

Finally (Step 5 of Fig. 1), S2P allows an integrated analysis of the spots data and the

Mascot identifications (Fig. 2). In the context of our case study, this option was firstly

used to try to identify potential biomarkers of the two conditions of interest. When the

healthy pool was compared with the bladder cancer patients, four differentially expressed

spots present in at least 5 of 7 bladder cancer patients (Fig. 3A) were found. The corre‐

sponding proteins were: (i) Serum albumin (Spot Number [SN] = 137), (ii) Gelsolin

(SN = 137), (iii) Fibrinogen gamma chain (SN = 337), (iv) Ig alpha-1 chain C region

(SN = 360), (v) Ig alpha-2 chain C region (SN = 360) and (vi) Haptoglobin (SN = 266).

When the healthy pool was compared with the LUTS patients, we found five differen‐

tially expressed spots that were present in at least 4 of 7 LUTS patients (Fig. 3B). The

associated proteins were the following: (i) CD5 antigen-like (SN = 244), (ii) Heparin

cofactor 2 (SN = 175 and SN = 190), (iii) Hemopexin (SN = 175), (iv) Serum albumin

(SN = 192 and SN = 190) and (v) Inter-alpha-trypsin inhibitor heavy chain H4 (SN = 88).

Fig. 2. Screenshot of the S2P integrated analysis window.

S2P: A Desktop Application for Fast and Easy Processing 5

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