Open Access Published by De Gruyter

Volume 4 Issue 3 - Special Issue: 4th Integrative Bioinformatics Workshop, Gent, Belgium, 2007 / Guest Editors: Martin Kuiper, Paul Verrier, Jacob Koehler

Issue of Journal of Integrative Bioinformatics

Contents
Journal Overview

Open Access October 18, 2016

A High-Level Petri Net Framework for Genetic Regulatory Networks

Richard Banks, L. Jason Steggles

Page range: 1-14

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Abstract

To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

Open Access October 18, 2016

From MIN model to ordinary differential equations

A. Yartseva, R. Devillers, H. Klaudel, F. Képès

Page range: 15-26

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Abstract

Biological interaction networks can be modeled using the Modular Interaction Network (MIN) formalism, which provides an intermediary modeling level between the biological and mathematical ones. MIN focuses on a simple but structured and versatile representation of biological knowledge, without targeting a particular analysis or simulation technique. In this paper, we propose a translation procedure which, starting from a MIN specification of a biological system, generates its representation in ordinary differential equations (ODEs) allowing to study the dynamics of the system. The translation is illustrated on a classical benchmark: the λ phage genetic switch.

Open Access October 18, 2016

The OXL format for the exchange of integrated datasets

Jan Taubert, Klaus Peter Sieren, Matthew Hindle, Berend Hoekman, Rainer Winnenburg, Stephan Philippi, Chris Rawlings, Jacob Köhler

Page range: 27-40

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Abstract

A prerequisite for systems biology is the integration and analysis of heterogeneous experimental data stored in hundreds of life-science databases and millions of scientific publications. Several standardised formats for the exchange of specific kinds of biological information exist. Such exchange languages facilitate the integration process; however they are not designed to transport integrated datasets. A format for exchanging integrated datasets needs to i) cover data from a broad range of application domains, ii) be flexible and extensible to combine many different complex data structures, iii) include metadata and semantic definitions, iv) include inferred information, v) identify the original data source for integrated entities and vi) transport large integrated datasets. Unfortunately, none of the exchange formats from the biological domain (e.g. BioPAX, MAGE-ML, PSI-MI, SBML) or the generic approaches (RDF, OWL) fulfil these requirements in a systematic way. We present OXL, a format for the exchange of integrated data sets, and detail how the aforementioned requirements are met within the OXL format. OXL is the native format within the data integration and text mining system ONDEX. Although OXL was developed with the ONDEX system in mind, it also has the potential to be used in several other biological and non-biological applications described in this paper. Availability: The OXL format is an integral part of the ONDEX system which is freely available under the GPL at http://ondex.sourceforge.net/. Sample files can be found at http://prdownloads.sourceforge.net/ondex/ and the XML Schema at http://ondex.svn.sf.net/viewvc/*checkout*/ondex/trunk/backend/data/xml/ondex.xsd.

Open Access October 18, 2016

VINEdb: a data warehouse for integration and interactive exploration of life science data

Sridhar Hariharaputran, Thoralf Töpel, Björn Brockschmidt, Ralf Hofestädt

Page range: 41-51

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Abstract

Control of cell proliferation, differentiation, activation and cell removal is crucial for the development and existence of multi-cellular organisms. Apoptosis, or programmed cell death, is a major control mechanism by which cells die and is also important in controlling cell number and proliferation as part of normal development. Molecular networks that regulate these processes are critical targets for drug development, gene therapy, and metabolic engineering. The molecular interactions involved in this and other processes are analyzed and annotated by experts and stored as data in different databases. The key task is to integrate, manage and visualize these data available from different sources and present them in a user-comprehensible manner. Here we present VINEdb, a data warehouse developed to interact with and to explore integrated life science data. Extendable open source data warehouse architecture enables platform-independent usability of the web application and the underlying infrastructure. A high degree of transparency and up-to-dateness is ensured by a monitor component to control and update the data from the sources. Furthermore, the system is supported by a visualization component to allow interactive graphical exploration of the integrated data. We will use apoptotic pathway and caspase-3 as a case study to show capability and usability of our approach. VINEdb is available at http://tunicata.techfak.unibielefeld.de/VINEdb/.

Open Access October 18, 2016

Defining Mapping Mashups with BioXMash

Ela Hunt, Joanna Jakubowska, Caroline Bösinger, Moira C. Norrie

Page range: 52-63

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Abstract

We present a novel approach to XML data integration which allows a biologist to select data from a large XML file repository, add it to a genome map, and produce a mapping mashup showing integrated data in map context. This approach can be used to produce contextual views of arbitrary XML data which relates to objects shown on a map. A biologist using BioXMash searches in XML tags, and is guided by XML path data availability, shown as the number of values reachable via a path, in both global, genome-wide, and local, per-gene, context. Then she examines sample values in an area of interest on the map. If required, the resulting data is dumped to files, for subsequent analysis. This is a lightweight integration approach, and differs significantly from other known methods. It assumes that data integration can be performed on a lab computer with limited memory, with no database installation or programming knowledge. It is different from BioMarts which predefine possible data selections, in that arbitrary data sources related to map items can be used. BioXMash offers full textual search in XML paths, shows path statistics, and supports visual verification of data values. Repeated scanning of all XML files at query time is avoided by the use of a high level indexing technique. Our prototype demonstrates this new approach. It efficiently supports data browsing on 2 GB od data from GeneCards with an index size of 40 MB.

Open Access October 18, 2016

Developmental Anatomy Ontology of Zebrafish: an Integrative semantic framework

Mounia Belmamoune, Fons J. Verbeek

Page range: 64-76

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Abstract

Integration of information is quintessential to make use of the wealth of bioinformatics resources. One aspect of integration is to make databases interoperable through well annotated information. With new databases one strives to store complementary information and such results in collections of heterogeneous information systems. Concepts in these databases need to be connected and ontologies typically provide a common terminology to share information among different resources. Our focus of research is the zebrafish and we have developed several information systems in which ontologies are crucial. Pivot is an ontology describing the developmental anatomy, referred to as the Developmental Anatomy Ontolgoy of Zebrafish (DAOZ). The anatomical and temporal concepts are provided by the Zebrafish Information Network (ZFIN) and proven within the research community. We have constructed a 3D digital atlas of zebrafish development based on histology; the atlas is series of volumetric models; in each instance, every volume element is assigned to an anatomical term. Complementing the atlas we developed an information system with 3D patterns of gene expression in zebrafish development based on marker genes. The spatial and temporal annotations to these 3D images are drawn from the ontology that we have designed. In its design the DAOZ ontology is structured as a Directed Acyclic Graph (DAG). Such is required to find unique concept paths and prevent self referencing. As we need to address the ontology in a direct manner, the DAG structure is transferred to a database. The database is used in the integration of our databases that share concepts at different levels of aggregation. In order to make sure that sufficient levels of aggregation for applications in mind are present, the original vocabulary was enriched with more relations and concepts. Both databases can now be addressed with the same unique terms and co-occurrence and co-expression of genes can be readily extracted from the databases. Integration can be further extended to the ZFIN resource and also by including ontologies that relate to gene/gene expression (e.g. Gene Ontology). In this manner, interoperable information retrieval from heterogeneous databases can be realized. This greatly facilitates processing complex information and retrieving relations in the data through machine learning approaches.

Open Access October 18, 2016

Stochastic effects in a compartmental model for mitotic checkpoint regulation

Bashar Ibrahim, Peter Dittrich, Stephan Diekmann, Eberhard Schmitt

Page range: 77-88

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Abstract

The proper segregation of sister chromatids at onset of anaphase is surveyed by the mitotic spindle assembly checkpoint. The concentration dynamics of the complexes APC:Cdc20 and MCC:APC determine exit from metaphase to anaphase. We have developed a model based on 14 proteins and complexes to describe concentration dynamics by ordinary differential equations in three compartments coupled by diffusion. One kinetochore in each compartment determines the attachment status to the spindle pole. Here, we focus on the role of noise in the segregation surveillance process. The deterministic differential equations are enriched by a stochastic term adding white noise of different amplitudes. Obviously, for the known physiological parameter ranges, noise does not disturb the checkpoint function. On the other hand, there is a connection between diffusion and noise, that could become important when considering a larger number of chromosomes.

Open Access October 18, 2016

Monophyletic clustering and characterization of protein families

Jian Zhang, Zhiyuan Zhao, Jennifer Evershed, Guoying Li

Page range: 89-100

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Abstract

A protein family contains sequences that are evolutionarily related. Generally, this is reflected by sequence similarity. There have been many attempts to organize the set of protein families into evolutionarily homogenous clusters using certain clustering methods. How do we characterize these clusters? How can we cluster protein families using these characterizations? In this work, these questions were addressed by use of a concept called group-wide co-evolution, and was exemplified by some real and simulated protein family data. The results have shown that the trend of a group of monophyletic proteins might be characterized by a normal distribution, while the strength and variability of this trend can be described by the sample mean and variance of the observed correlation coefficients after a suitable transformation. To exploit this property, we have developed a monophyletic clustering method called monophyletic k−medoids clustering. A software package written in R has been made available at http://www.kent.ac.uk/ims/personal/jz .

Open Access October 18, 2016

Data Linkage Graph: computation, querying and knowledge discovery of life science database networks

Matthias Lange, Axel Himmelbach, Patrick Schweizer, Uwe Scholz

Page range: 101-111

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Abstract

To support the interpretation of measured molecular facts, like gene expression experiments or EST sequencing, the functional or the system biological context has to be considered. Doing so, the relationship to existing biological knowledge has to be discovered. In general, biological knowledge is worldwide represented in a network of databases. In this paper we present a method for knowledge extraction in life science databases, which prevents the scientists from screen scraping and web clicking approaches. We developed a method for extraction of knowledge networks from distributed, heterogeneous life science databases. To meet the requirement of the very large data volume, the method used is based on the concept of data linkage graphs (DLG).We present an efficient software which enables the joining of millions of data points over hundreds of databases. In order to motivate possible applications, we computed networks of protein knowledge, which interconnect metabolic, disease, enzyme and gene function data. The computed networks enabled a holistic relationship among measured experimental facts and the combined biological knowledge. This was successfully applied for a high throughput functional classification of barley EST and gene expression experiments with the perspective of an automated pipeline for the provisioning of controlled annotation of plant gene arrays and chips. Availability: The data linkage graphs (XML or TGF format), the schema integrated database schema (GML or GRAPH-ML) and the graph computation software may be downloaded from the following URL: http://pgrc.ipk-gatersleben.de/dlg/

Open Access October 18, 2016

A Methodology for Comparative Functional Genomics

Intikhab Alam, Mike Cornell, Darren M. Soanes, Cornelia Hedeler, Han Min Wong, Magnus Rattray, Simon J. Hubbard, Nicholas J. Talbot, Stephen G. Oliver, Norman W. Paton

Page range: 112-122

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Abstract

The continuing and rapid increase in the number of fully sequenced genomes is creating new opportunities for comparative studies. However, although many genomic databases store data from multiple organisms, for the most part they provide limited support for comparative genomics. We argue that refocusing genomic data management to provide more direct support for comparative studies enables systematic identification of important relationships between species, thereby increasing the value that can be obtained from sequenced genomes. The principal result of the paper is a methodology, in which comparative analyses are constructed over a foundation based on sequence clusters and evolutionary relationships. This methodology has been applied in a systematic study of the fungi, and we describe how comparative analyses have been implemented as an analysis library over the e-Fungi data warehouse.

Open Access October 18, 2016

Exploring PSI-MI XML Collections Using DescribeX

Reza Samavi, Mariano Consens, Shahan Khatchadourian, Thodoros Topaloglou

Page range: 123-134

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Abstract

PSI-MI has been endorsed by the protein informatics community as a standard XML data exchange format for protein-protein interaction datasets. While many public databases support the standard, there is a degree of heterogeneity in the way the proposed XML schema is interpreted and instantiated by different data providers. Analysis of schema instantiation in large collections of XML data is a challenging task that is unsupported by existing tools. In this study we use DescribeX, a novel visualization technique of (semi-)structured XML formats, to quantitatively and qualitatively analyze PSI-MI XML collections at the instance level with the goal of gaining insights about schema usage and to study specific questions such as: adequacy of controlled vocabularies, detection of common instance patterns, and evolution of different data collections. Our analysis shows DescribeX enhances understanding the instance-level structure of PSI-MI data sources and is a useful tool for standards designers, software developers, and PSI-MI data providers.

Open Access October 18, 2016

Community-based Linking of Biological Network Resources: Databases, Formats and Tools

Michael Telgkamp, Dirk Koschützki, Henning Schwöbbermeyer, Falk Schreiber

Page range: 135-146

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Abstract

The analysis of biological networks is increasingly important in the life sciences and in particular in systems biology. Computer-based analysis tools are exploited for the investigation of these networks. However, to find relevant data sources can be a time-consuming task, access to information changes, frequently it is not obvious to a user which tools can be used in combination with specific data sources, and network data is often not available in a format directly usable by analysis tools. To assist in collecting information about biological network data sources, and to help in investigating possible combinations of data sources and analysis software, we created BiNCo-wiki. BiNCo-wiki is a wiki system that stores information related to biological networks and allows all users to add or modify this information in an easy way. The collaborative character of a wiki system supports easy sharing of information and allows the community-based extension of the information already stored in BiNCo-wiki. We describe the general structure and functionality of the system and its initial content. BiNCo-wiki is accessible, free of charge, at http://pgrc.ipk-gatersleben.de/BiNCO-wiki.

Open Access October 18, 2016

Analysis of integrated biomolecular networks using a generic network analysis suite

Matthias Oesterheld, Hans Werner Mewes, Volker Stümpflen

Page range: 147-157

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Abstract

The informative value of biomolecular networks has shifted from being solely information resources for possible cellular partners (whether these embody proteins, (ribo)nucleic acids or small molecules) towards becoming models for the functional connectivity within a cell. These models are increasingly exploited to make quantitative predictions about the cell’s functional organization as well as about the functionality of individual elements in the network. A large number of concepts and methods have been proposed in order to interpret experimental data mapped to cellular networks these systems and to make use of the rich source of information they represent. We will present a system for the Comprehensive Analysis of Biomolecular Networks (CABiNet), capable of integrating available network analysis methods. Integration is done by classifying each method into one of four separate categories using standardized interfaces that encapsulate the functionality of the method in a distinct component with standardized in- and output. These components can be accessed individually or in an integrated form using a processing pipeline for semi-automatic analyses. Additionally, the system can be used to query both biomolecular networks as well as the derived results of network analysis methods, such as clustering algorithms, in order to provide a service for researchers who are focused towards the functional context of any particular cellular entity. CABiNet is designed in an easy-to-use and easy-to-extend software framework that allows a straightforward integration of novel components. We will demonstrate the capabilities of the system by introducing several use cases. The CABiNet suite can be accessed at http://mips.gsf.de/genre/proj/CABiNet. Source code including additional components that can be accessed using the API is available upon request.

Open Access October 18, 2016

Mining for Single Nucleotide Polymorphisms in Expressed Sequence Tags of European Sea Bass

E. L. Souche, B. Hellemans, J. K. J. Van Houdt, A. Canario, S. Klages, R. Reinhardt, F. A. M. Volckaert

Page range: 158-167

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Abstract

As a multitude of sequence data are published, discovering polymorphisms bioinformatically becomes a valid option. In silico Single Nucleotide Polymorphism (SNP) detection is based on the analysis of multiple alignments. Each column of an alignment is considered a slice containing one base of every sequence aligned. If a mismatch is detected, the slice is further analysed and the mismatch may be reported as a candidate SNP. About 30,000 Expressed Sequence Tags (ESTs) of the fish European sea bass have been sequenced and processed. Since ESTs are redundant, they provide a resource for in silico SNP discovery. To prevent the detection of sequencing errors, a redundancy of two is chosen in order for a mismatch to be considered a candidate SNP. Among the various tools available to detect candidate SNPs, three software packages were tested: SNPServer, PolyBayes and PolyFreq. Candidate SNPs were validated in the laboratory by cloning and sequencing. From preliminary results PolyFreq outperforms both PolyBayes and SNPServer in terms of positive predictive value and SNPServer is the most sensitive tool. PolyFreq and SNPServer non-default identify respectively the fewest and highest number of candidates. Considering candidates detected by several tools seems to enhance both positive predictive value and sensitivity. Out of the 69 loci sequenced, only four were monomorphic, leading to a total of 91.3% polymorphic loci. Randomly chosen contigs will be sequenced to know whether SNP discovery tools tend to predict polymorphic fragments. Polymorphisms will be mapped, used for selection in aquaculture and the study of adaptation in natural populations.

Open Access October 18, 2016

Supervised classification of combined copy number and gene expression data

S. Riccadonna, G. Jurman, S. Merler, S. Paoli, A. Quattrone, C. Furlanello

Page range: 168-185

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Abstract

In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

Open Access October 18, 2016

IMS² – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

Jan Baumbach, Alexander Bunkowski, Sita Lange, Timm Oberwahrenbrock, Nils Kleinbölting, Sven Rahmann, Jörg Ingo Baumbach

Page range: 186-197

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Abstract

IMS 2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS 2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world. After reviewing the IMS technology, we first describe the software architecture of IMS 2 and then the integrated classification module, including necessary pre-processing steps and different classification methods. The Lung Hospital Hemer (Germany) provided IMS data of 35 patients suffering from lung cancer and 72 samples of healthy persons. IMS 2 correctly classifies 99% of the samples, evaluated using 10-fold cross-validation.

Open Access October 18, 2016

Functional and Transcriptional Coherency of Modules in the Human Protein Interaction Network

Matthias E. Futschik, Gautam Chaurasia, Anna Tschaut, Jenny Russ, M. Madan Babu, Hanspeter Herzel

Page range: 198-207

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Abstract

Modularity is a major design principle in interaction networks. Various studies have shown that protein interaction networks in prokaryotes and eukaryotes display a modular structure. A majority of the studies have been performed for the yeast interaction network, for which data have become abundant. The systematic examination of the human protein interaction network, however, is still in an early phase. To assess whether the human interaction network similarly displays a modular structure, we assembled a large protein network consisting of over 30,000 interactions. More than 670 modules were subsequently identified based on the detection of cliques. Inspection showed that these modules included numerous known protein complexes. The extracted modules were scrutinized for their coherency with respect to function, localization and expression, thereby allowing us to distinguish between stable and dynamic modules. Finally, the examination of the overlap between modules identified key proteins linking distinct molecular processes.

Open Access October 18, 2016

Prediction of protein-protein interactions using one-class classification methods and integrating diverse biological data

José A. Reyes, David Gilbert

Page range: 208-223

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Abstract

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results. Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature.

Open Access October 18, 2016

CIDA: An integrated software for the design, characterisation and global comparison of microarrays

Sabah Khalid, Mohsin Khan, Alistair Symonds, Karl Fraser, Ping Wang, Xiaohui Liu, Suling Li

Page range: 224-242

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Abstract

Microarray technology has had a significant impact in the field of systems biology involving the investigation into the biological systems that regulate human life. Identifying genes of significant interest within any given disease on an individual basis is no doubt time consuming and inefficient when considering the complexity of the human genome. Thus, the genetic profiling of the entire human genome in a single experiment has resulted in microarray technology becoming a widely used experimental tool. However, without the use of tools for several aspects of microarray data analysis the technology is limited. To date, no such tool has been developed that allows the integration of numerous microarray results from different research laboratories as well as the design of customised gene chips in a cost-effective manner. In light of this, we have designed the first integrated and automated software called Chip Integration, Design and Annotation (CIDA) for the cross comparison, design and functional annotation of microarray gene chips. The software provides molecular biologists with the control to cross compare the biological signatures generated from multiple microarray studies, design custom microarray gene chips based on their research requirements and lastly characterise microarray data in the context of immunogenomics. Through the relative comparison of related microarray experiments we have identified 258 genes with common gene expression profiles that are not only upregulated in anergic T cells, but also in cells over-expressing the transcription factor Egr2, that has been identified to play a role in T cell anergy. Using the gene chip design aspect of CIDA we have designed and subsequently fabricate immuno-tolerance gene chips consisting of 1758 genes for further research. The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk/CIDA/. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchgroups/mi/publications/supplementary/cida

Open Access October 18, 2016

Mapping protein information to disease terminologies

Anaïs Mottaz, Yum L. Yip, Patrick Ruch, Anne-Lise Veuthey

Page range: 243-251

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Abstract

In order to improve the accessibility of genomic and proteomic information to medical researchers, we have developed a procedure to link biological information on proteins involved in diseases to the MeSH and ICD-10 disease terminologies. For this purpose, we took advantage of the manually curated disease annotations in more than 2,000 human protein entries of the UniProt KnowledgeBase. We mapped disease names extracted from the entry comment lines or from the corresponding OMIM entry to the MeSH. The method was assessed on a benchmark set of 200 manually mapped disease comment lines. We obtained a recall of 54% for 91% precision. The same procedure was used to map the more than 3,000 diseases in Swiss-Prot to MeSH with comparable efficiency. Tested on ICD-10, the coverage of the mapped terms was lower, which could be explained by the coarse-grained structure of this terminology for hereditary disease description. The mapping is provided as supplementary material at http://research.isbsib.ch/unimed.

Open Access October 18, 2016

Integration of constraints documented in SBML, SBO, and the SBML Manual facilitates validation of biological models

Allyson L. Lister, Matthew Pocock, Anil Wipat

Page range: 252-263

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Abstract

The creation of quantitative, simulatable, Systems Biology Markup Language (SBML) models that accurately simulate the system under study is a time-intensive manual process that requires careful checking. Currently, the rules and constraints of model creation, curation, and annotation are distributed over at least three separate documents: the SBML schema document (XSD), the Systems Biology Ontology (SBO), and the “Structures and Facilities for Model Definition” document. The latter document contains the richest set of constraints on models, and yet it is not amenable to computational processing. We have developed a Web Ontology Language (OWL) knowledge base that integrates these three structure documents, and that contains a representative sample of the information contained within them. This Model Format OWL (MFO) performs both structural and constraint integration and can be reasoned over and validated. SBML Models are represented as individuals of OWL classes, resulting in a single computationally amenable resource for model checking. Knowledge that was only accessible to humans is now explicitly and directly available for computational approaches. The integration of all structural knowledge for SBML models into a single resource creates a new style of model development and checking.

Open Access October 18, 2016

GeneBrowser: an approach for integration and functional classification of genomic data

Joel Arrais, Bruno Santos, João Fernandes, Laura Carreto, Manuel A.S. Santos, José Luis Oliveira

Page range: 264-273

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Abstract

The achievements coming from genome analysis depend greatly on the quality of computational and processing methods. Tools for functional mRNA profiling and for gene information integration have become essential to this task. We have developed GeneBrowser as a novel approach that combines the advantages of mRNA profiling tools, at genome-scale experiments, with the features provided by data integration systems. For a given set of genes, GeneBrowser integrates bibliography information with functional annotations, using Gene Ontology, Entrez Gene, KEGG Orthology and KEGG Pathways. The result is a comprehensive and easy to use web application that helps researchers to extract knowledge from large data sets and to speed up the discovery process. Availability: GeneBrowser is freely available at http://bioinformatics.ua.pt/genebrowser

Open Access October 18, 2016

A Tool for Evaluating Strategies for Grouping of Biological Data

Vaida Jakonienė, Patrick Lambrix

Page range: 274-285

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Abstract

During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the grouping results: the data sources, the grouping attributes and the algorithms implementing the grouping procedure. Many methods exist, but it is often not clear which methods perform best for which grouping tasks. The study of the properties, and the evaluation and the comparison of the different aspects that influence the quality of the grouping results, would give us valuable insight in how the grouping procedures could be used in the best way. It would also lead to recommendations on how to improve the current procedures and develop new procedures. To be able to perform such studies and evaluations we need environments that allow us to compare and evaluate different grouping strategies. In this paper we present a framework, KitEGA 1 , for such an environment, and present its current prototype implementation. We illustrate its use by comparing grouping strategies for classifying proteins regarding biological function and isozymes.

Open Access October 18, 2016

Reactome: An integrated expert model of human molecular processes and access toolkit

Bernard de Bono, Imre Vastrik, Peter D’Eustachio, Esther Schmidt, Gopal Gopinath, David Croft, Marc Gillespie, Bijay Jassal, Suzanna Lewis, Lisa Matthews, Guanming Wu, Ewan Birney, Lincoln Stein

Page range: 286-296

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Abstract

The behaviour of pervasive molecular processes in human biology can be studied through the large-scale modeling of the molecular events that define them. Constructing detailed models of such extent and scope is a considerable undertaking well beyond the reach and capability of individual efforts, due to the range of expertise required. Reactome (http://www.reactome.org) is an open-access project that collaborates with field experts to integrate their pathway knowledge into a single quality-checked human model. This resource dataset is systematically cross-referenced to major molecular and literature databases, and is accessible to the community in a number of well-established formats. Various tools have been developed to facilitate querying and interaction with this content. The salient features of the annotation strategy are discussed here, and examples of pathway and genomic data integration using flexible interfacing methods from the associated toolkit are also presented.

About this journal

Objective
Journal of Integrative Bioinformatics (JIB) is an international open access journal publishing original peer-reviewed research articles in all aspects of integrative bioinformatics.

Molecular biology produces huge amounts of data in the post-genomic era. This includes data describing metabolic mechanisms and pathways, structural genomic organization, patterns of regulatory regions; proteomics, transcriptomics, and metabolomics. On the one hand, analysis of this data uses essentially the methods and concepts of computer science; on the other hand, the range of biological tasks solved by researchers determines the range and scope of the data. Currently, there are about 1,000 database systems and various analytical tools available via the Internet which are directed at solving various biological tasks.

The challenge we have is to integrate these list-parts and relationships from genomics and proteomics at novel levels of understanding. Integrative Bioinformatics is a new area of research using the tools of computer science and electronic infrastructure applied to Biotechnology. These tools will also represent the backbone of the concept of a virtual cell.

Topics

Software applications/tools and databases covering the following topics:

Molecular Databases, Information Systems and Data Warehouses
Integration of Data, Methods and Tools
Metabolic and Regulatory Network Modeling and Simulation
Signal Pathways and Cell Control
Network Analysis
Medical Informatics, Biomedicine and Biotechnology
Integrative Approaches for Drug Design
Integrative Data and Text Mining Approaches
Integrative, whole cell and molecular modeling
Visualization and animation

Review papers are also welcome with regard to JIB.tools.

Article formats
Research articles, Review papers, Workshop contributions (if peer-reviewed)

Article processing charges (APCs)
Each unsolicited article, which is accepted for publication in the Journal of Integrative Bioinformatics is subject to an Article Processing Charge of 1,000€.
The Open Access publication of invited articles for Special Issues is sponsored by the editors.

Inquiries concerning APCs should be addressed to the Editorial Office at De Gruyter (see contact details below).

Volume 4 Issue 3 - Special Issue: 4th Integrative Bioinformatics Workshop, Gent, Belgium, 2007 / Guest Editors: Martin Kuiper, Paul Verrier, Jacob Koehler

Issue of Journal of Integrative Bioinformatics

A High-Level Petri Net Framework for Genetic Regulatory Networks

Abstract

From MIN model to ordinary differential equations

Abstract

The OXL format for the exchange of integrated datasets

Abstract

VINEdb: a data warehouse for integration and interactive exploration of life science data

Abstract

Defining Mapping Mashups with BioXMash

Abstract

Developmental Anatomy Ontology of Zebrafish: an Integrative semantic framework

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Stochastic effects in a compartmental model for mitotic checkpoint regulation

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Monophyletic clustering and characterization of protein families

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Data Linkage Graph: computation, querying and knowledge discovery of life science database networks

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A Methodology for Comparative Functional Genomics

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Exploring PSI-MI XML Collections Using DescribeX

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Community-based Linking of Biological Network Resources: Databases, Formats and Tools

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Analysis of integrated biomolecular networks using a generic network analysis suite

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Mining for Single Nucleotide Polymorphisms in Expressed Sequence Tags of European Sea Bass

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Supervised classification of combined copy number and gene expression data

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IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

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Functional and Transcriptional Coherency of Modules in the Human Protein Interaction Network

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Prediction of protein-protein interactions using one-class classification methods and integrating diverse biological data

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CIDA: An integrated software for the design, characterisation and global comparison of microarrays

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Mapping protein information to disease terminologies

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Integration of constraints documented in SBML, SBO, and the SBML Manual facilitates validation of biological models

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GeneBrowser: an approach for integration and functional classification of genomic data

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A Tool for Evaluating Strategies for Grouping of Biological Data

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Reactome: An integrated expert model of human molecular processes and access toolkit

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IMS² – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath