Open Access Published by De Gruyter

Volume 3 Issue 2 - Special Issue: 3rd Integrative Bioinformatics Workshop, Harpenden, United Kingdom, 2006 / Guest Editors: Paul Kersey, Jacob Koehler, Chris Rawlings, Paul Verrier, Julio Collado-Vides, Ralf Hofestädt, Uwe Scholz

Issue of Journal of Integrative Bioinformatics

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Journal Overview

Open Access October 18, 2016

CoryneRegNet 2: An Integrative Bioinformatics Approach for Reconstruction and Comparison of Transcriptional Regulatory Networks in Prokaryotes

Jan Baumbach, Karina Brinkrolf, Tobias Wittkop, Andreas Tauch, Sven Rahmann

Page range: 1-13

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Abstract

CoryneRegNet is an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Now we integrated the genomes and transcriptional interactions of three other corynebacteria, C. diphtheriae, C. efficiens, and C. jeikeium into CoryneRegNet; providing comparative analysis and visualization with GraphVis. We also integrated the high-performance PSSM search tool PoSSuM search to detect potential transcription factor binding sites within and across species. As an application, we reconstruct in silico the regulatory network of the iron metabolism regulator DtxR in the four corynebacteria. CoryneRegNet is freely accessible at https://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/. The final slash (/) is mandatory. In order to use the GraphVis feature, Java (at least version 1.4.2) is required.

Open Access October 18, 2016

Analysing Microarray Data using the Multi-functional Immune Ontologiser

Sabah Khalid, Mohsin Khan, Ping Wang, Xiaohui Liu, Suling Li

Page range: 14-36

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Abstract

Gene expression microarrays are a prominent experimental tool in functional genomics allowing researchers to gain a deeper understanding of biological processes. To date, no such tool has been developed to allow researchers with a specialised biological research interest to distinctively identify those genes and gene functionalities associated more strongly with the research area. Based on this functional analysis capability we present a specialised multi-functional Immune Ontologiser – a software, specialised for immunologists to annotate multiple genes from microarray datasets within two new ontologies: a newly structured Immune Ontology focussed at immunology and haematology and a uniquely curated ImmunoArray-PubOntology. The Immune Ontology functionally annotates genes identifying immunology related functions enriched with upregulated or downregulated genes of interest. The ImmunoArray-PubOntology compares and contrasts gene functionality of microarray datasets, comparing genes of interest with the differential gene expression matrices published amongst immunologyrelated microarray literature. This aspect facilitates literature mining by extracting publications containing gene sets of interest in a well-structured immunological context where the literature has been categorised according to disease types. The software consists of a query-optimised database of two parts – the ImmunoGene-database and a unique Database of Immunological Microarray Publications (DIMP) to provide the user with a more detailed insight into other studies involving their genes and research groups investigating similar research areas. Using our Immune Ontologiser software to analyse tolerance array data we identify 70 interesting up-regulated genes in terms of their functionality within tolerance. Furthermore, from these 70 genes we identify 15 genes to have immunology-related functions. More interestingly, the remaining 55 genes were not previously known to be directly involved within the immunology related condition and hence we have identified target genes for future investigation. Among the 70 genes, 21 have been identified by our software to be studied within various immunology-related diseases via microarray experiments performed by other laboratories. The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchareas/mi/publications/supplementary. A detailed microarray protocol is available at http://www.ebi.ac.uk/arrayexpress under the Accession Number: E-MEXP-283.

Open Access October 18, 2016

BASIS: an internet resource for network modelling

Colin S. Gillespie, Darren J. Wilkinson, Daryl P. Shanley, Carole J. Proctor, Richard J. Boys, Thomas B.L. Kirkwood

Page range: 37-48

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Abstract

There is a growing realisation that complex biological processes cannot be understood through the application of ever more reductionist experimental programs alone. Recognising this, we have a constructed a flexible web-service based modelling system called BASIS (Biology of Ageing e-Science Integration and Simulation), which facilitates model construction and development. In particular it allows users to store, share and simulate their models. The system is accessed through web-services using any language (e.g. Python or Java) or under any operating system (e.g. Linux or Windows).

Open Access October 18, 2016

InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET

Wolfgang Straßer, Doris Siegl, Kamil Önder, Johann Bauer

Page range: 49-60

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Abstract

In the last decades, biological databases became the major knowledge resource for researchers in the field of molecular biology. The distribution of information among these databases is one of the major problems. An overview about the subject area of data access and representation of protein and protein-protein interaction data within public biological databases is described. For a comprehensive and consistent way of searching and analysing integrated protein and protein-protein interaction data, the InSilico Proteomics (ISP) project has been initiated. Its three main objectives are (1) to provide an integrated knowledge pool for data investigation and global network analysis functions for a better understanding of a cell’s interactome, (2) employment of public data for plausibility analysis and validation of in-house experimental data and (3) testing the applicability of Microsoft’s .NET architecture for bioinformatics applications. Data integrated into the ISP database can be queried through the Web portal PRIMOS (PRotein Interaction and MOlecule Search) which is freely available at http://biomis.fh-hagenberg.at/isp/primos.

Open Access October 18, 2016

Metabolite profiles as a reflection of physiological status – a methodological validation

Matthias Steinfath, Dirk Repsilber, Manuela Hische, Nicolas Schauer, Alisdair R. Fernie, Joachim Selbig

Page range: 61-76

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Abstract

Biological ”omics” data comprise numerous variables (metabolites, gene expression, physiological quantities) and comparatively few samples. These samples represent either measurements for slightly different genotypes in identical environments, or for different environmental conditions affecting the same genotype. Given this kind of data, it is intriguing to ask for possible measurable associations between molecular variables and the phenotypical or physiological status. To evaluate such correlations we need a model for the functional dependency of the physiological state on given molecular variables. Supervised machine learning methods such as neural networks, decision trees, or support vector machines may be used to reveal such correlations. The simplest model is certainly a linear approach. To investigate the association between molecular and phenotypical variables, we ask if the correlation between predictor and response is statistically significant, and how much of the phenotypical variance of the response can be explained by a given set of predictors. When confronted with a set of molecular data not all of them are generally relevant for each physiological trait. Given this fact the problem of feature selection arises. Different regression methods have been developed to answer this question: Ordinary Least Squares (OLS) yields an unbiased solution, but normally has a high mean square error. In particular, there is no dimension reduction included in this method and, hence, overfitting is a critical problem. In contrast, Principle Component Regression (PCR) offers such a dimension reduction, however, the principle components are found without considering the response. Partial Least Squares Regression (PLSR) is utilised as an alternative method since it considers the variance within the predictors as well as between predictors and response, whilst Ridge Regression is a further alternative worthy of consideration. In our study we applied these methods to data resulting from a tomato metabolite experimental series. Comparison of the results for this dataset with experimentally relevant correlation structure between variables and samples allows us to test the relative merits of the regression methods with respect to the questions raised above. Given certain prerequisite knowledge it also allows us to conjecture the true biological correlation. Our results show that under most circumstances OLS is worst with respect to prediction. However, the ranking of methods seems to change considerably if the question of feature selection is considered. Understanding and discussing these differences is a relevant contribution to the task of choice of suitable approach of correlation analysis for “omics” datasets with respect to the biological interpretation in question.

Open Access October 18, 2016

Modeling Microarray Data: Interpreting and communicating the biological results

Y. E. Pittelkow, S. R. Wilson

Page range: 77-89

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Abstract

Various statistical models have been proposed for detecting differential gene expression in data from microarray experiments. Given such detection, we are usually interested in describing the differential expression patterns. Due to the large number of genes that are typically analysed in microarray experiments, possibly more than ten thousand, the tasks of interpretation and communication of all the corresponding statistical models pose a considerable challenge, except perhaps in the simplest experiment involving only two groups. A further challenge is to find methods to summarize the resulting models. These challenges increase with experimental complexity. Biologists often wish to sort genes into ‘classes’ with similar response profiles/patterns. So, in this paper we describe a likelihood approach for assigning genes to these different class patterns for data from a replicated experimental design. The number of potential patterns increases very quickly as the number of combinations in the experimental design increases. In a two group experimental design there are only three patterns required to describe the mean response: up, down and no difference. For a factorial design with three treatments there are 13 different patterns, and with four levels there are 75 potential patterns to be considered, and so on. The approach is applied to the identification of differential response patterns in gene expression from a microarray experiment using RNAextracted from the leaves of Arabidopsis thaliana plants. We compare patterns of response found using additive and multiplicative models. A multiplicative model is more commonly used in the statistical analysis of microarray data because of the variance stabilizing properties of the logarithmic function. Then the error structure of the model is taken to be log-Normal. On the other hand, for the additive model the gene expression value is modeled directly as being from a gamma distribution which successfully accounts for the constant coefficient of variation often observed. Appropriate visualization displays for microarray data are important as a way of communicating the patterns of response amongst the genes. Here we use graphical ‘icons’ to represent the patterns of up/down and no response and two alternative displays, the Gene-plot and a grid layout to provide rapid overall summaries of the gene expression patterns.

Open Access October 18, 2016

Statistical Embedding in Complex Biosystems

Enrico Capobianco

Page range: 90-108

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Abstract

Complex high-dimensional systems represent an important area of interdisciplinary research in systems biology. Gene expression values obtained by microarray data represent a good example, owing to their various features that depend on biological network dynamics. This work emphasizes the role of blind source separation for dealing with dimensionality reduction and feature selection, and their useful combination with fuzzy rules, embedding principles and entropic measures. In particular, entropy and embedding are useful tools for controlling the robustness and stability of the decomposition of a system with larger than intrinsic dimensionality. As a result, the convergence to a small intrinsic dimensionality occurs by the means of least dependent components, seen as a minimal number of salient features.

Open Access October 18, 2016

A First Step towards Learning which uORFs Regulate Gene Expression

Selpi, Christopher H. Bryant, Graham J.L. Kemp, Marija Cvijovic

Page range: 109-122

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Abstract

We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.

Open Access October 18, 2016

Multi-model inference of network properties from incomplete data

Michael P. H. Stumpf, Thomas Thorne

Page range: 123-136

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Abstract

It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

Open Access October 18, 2016

Inference of Gene Coexpression Networks by Integrative Analysis across Microarray Experiments

Laura L. Elo, Tero Aittokallio

Page range: 137-147

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Abstract

We improve the reliability of detecting coexpressed gene pairs from microarray data by introducing a novel probe-level quality-weighted similarity measure for combining data across different Affymetrix experiments. In construction of gene coexpression networks, the proposed procedure is less sensitive to noise than the corresponding single-experiment approaches or the conventional integrative approaches, even when a relatively small number of samples and conditions is available. The present results indicate how the accumulated microarray data can be effectively exploited to increase the quality of the inferred networks. In particular, we demonstrate its biological relevance in identifying coexpressions in mouse T helper cell differentiation.

Open Access October 18, 2016

BN++ – A Biological Information System

Jan Küntzer, Torsten Blum, Andreas Gerasch, Christina Backes, Andreas Hildebrandt, Michael Kaufmann, Oliver Kohlbacher, Hans-Peter Lenhof

Page range: 148-161

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Abstract

Recent years have seen an explosive growth in the amount of biochemical data available. Numerous databases have been established and are being used as an essential resource by biologists around the world. The sheer amount and heterogeneity of these data poses a major challenge: data integration and, based thereupon, the integrative analysis of these data. We present BN++, the biochemical network library, a powerful software package for integrating, analyzing, and visualizing biochemical data in the context of networks. BN++ is based on a comprehensive and extensible object model (BioCore), which has been implemented as a C++ framework, a Java class library, and a relational database. The C++ framework is used to efficiently import, integrate, and analyze the data, which is stored in a data warehouse. The Java-based viewer (BiNA) provides a powerful platform-independent visualization of the data using sophisticated graph layout algorithms. Currently, the data warehouse imports and integrates data from about a dozen important databases including, among others, sequence data, metabolic and regulatory networks, and protein interaction data. We illustrate the usefulness of BN++ with a few select example applications. Availability: BN++ is open source software available from our website at www.bnplusplus.org.

Open Access October 18, 2016

Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes

Emilie Guérin, Gwenaëlle Marquet, Julie Chabalier, Marie-Bérengère Troadec, Christiane Guguen-Guillouzo, Olivier Loréal, Anita Burgun, Fouzia Moussouni

Page range: 162-176

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Abstract

In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics, as well as restitution and analysis methods adapted to huge amounts of data. We present a combined approach that relies on two components: BioMeKE annotates sets of genes using biomedical GO and UMLS concepts, and GEDAW, a Gene Expression Data Warehouse, uses BioMeKE to enrich experimental results with biomedical concepts, thus performing complex analyses of expression measurements through analysis workflows. The strength of our approach has been demonstrated within the framework of analysis of data resulting from the liver transcriptome study. It allowed new genes potentially associated with liver diseases to be highlighted.

Open Access October 18, 2016

A Graphical Notation to Describe the Logical Interactions of Biological Pathways

Stuart L. Moodie, Anatoly Sorokin, Igor Goryanin, Peter Ghazal

Page range: 177-187

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Abstract

The modelling of biological intra-cellular pathways requires the systematic capture and representation of interactions between components that are biologically correct and computationally rigorous. The challenge is two fold, to verify and extend our understanding of such pathways by comparing in silico models to experiments; and to ensure that such models are understandable by biologists and for checking biological validity. In this report we present a graphical notation, the Edinburgh Pathway Notation (EPN), which satisfies the central biologically driven requirements while providing a strict formal framework for analysis. The EPN emphasises the use of a logical representation of pathways, which is particularly suited to pathways were some mechanisms are not known in detail.

Open Access October 18, 2016

Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments

Ricardo Martinez, Nicolas Pasquier, Martine Collard, Claude Pasquier, Lucero Lopez-Perez

Page range: 188-198

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Abstract

Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to wellknown microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.

Open Access October 18, 2016

Exact and Approximate Stochastic Simulations of the MAPK Pathway and Comparisons of Simulations’ Results

Vilda Purutçuoǧlu, Ernst Wit

Page range: 199-211

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Abstract

The MAPK (mitogen-activated protein kinase) or its synonymous ERK (extracellular signal regulated kinase) pathway whose components are Ras, Raf, and MEK proteins with many biochemical links, is one of the major signalling systems involved in cellular growth control of eukaryotes including cell proliferation, transformation, differentiation, and apoptosis. In this study we describe the MAPK/ERK pathway via (quasi) biochemical reactions and then implement the pathway by a stochastic Markov process. A novelty of our approach is to use multiple parametrizations in order to deal with molecules for which localization in the cell is an intricate part of the dynamic process and to describe the protein using different binding sites and various phosphorylations. We simulate the system by exact and different approximate simulations, e.g. via the Poisson τ-leap, the Binomial τ-leap and the diffusion methods, in which we introduce a new updating plan for dependent columns of the diffusion matrix. Finally we compare the results of different algorithms by the current biological knowledge and find out new relations about this complex system.

Open Access October 18, 2016

The implications for Bioinformatics of integration across physical scales

T.C. Hodgman, Y. Ugartechea-Chirino, G. Tansley, I. Dryden

Page range: 212-218

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Abstract

Bioinformatics blossomed with research developments in molecular biology. But as the focus of research moves back up the physical scale to the biology of whole multicellular organisms, there are new integration challenges. Data integration is a perennial theme that will not be explored here. Rather, we outline a survey of the types of data generated in whole organisms, putting particular emphasis on complex image capture, management and analysis, bio-systems modelling techniques at this higher scale, and approaches to integrating models across these physical scales. This change in biological focus raises certain challenges, one of which may be the need to retrain bioinformaticians in sciences pertaining to whole plants and animals.

Open Access October 18, 2016

Data Cleaning and Semantic Improvement in Biological Databases

Daniele Apiletti, Giulia Bruno, Elisa Ficarra, Elena Baralis

Page range: 219-229

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Abstract

Public genomic and proteomic databases can be affected by a variety of errors. These errors may involve either the description or the meaning of data (namely, syntactic or semantic errors). We focus our analysis on the detection of semantic errors, in order to verify the accuracy of the stored information. In particular, we address the issue of data constraints and functional dependencies among attributes in a given relational database. Constraints and dependencies show semantics among attributes in a database schema and their knowledge may be exploited to improve data quality and integration in database design, and to perform query optimization and dimensional reduction. We propose a method to discover data constraints and functional dependencies by means of association rule mining. Association rules are extracted among attribute values and allow us to find causality relationships among them. Then, by analyzing the support and confidence of each rule, (probabilistic) data constraints and functional dependencies may be detected. With our method we can both show the presence of erroneous data and highlight novel semantic information. Moreover, our method is database-independent because it infers rules from data. In this paper, we report the application of our techniques to the SCOP (Structural Classification of Proteins) and CATH Protein Structure Classification databases.

Open Access October 18, 2016

An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions

Fiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje

Page range: 230-246

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Abstract

Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an existing multi-source dataset from S. cerevisiae by constructing a new set of putative interactions extracted from Gene Ontology (GO)- driven annotations in the Saccharomyces Genome Database. Different classification techniques: Simple Naive Bayesian (SNB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. Relatively simple classification methods (i.e. less computing intensive and mathematically complex), such as SNB, have been proven to be proficient at predicting PPI. SNB produced the “highest” predictive quality obtaining an area under Receiver Operating Characteristic (ROC) curve (AUC) value of 0.99. The lowest AUC value of 0.90 was obtained by the KNN classifier. This assessment also demonstrates the strong predictive power of GO-driven models, which offered predictive performance above 0.90 using the different machine learning and statistical techniques. As the predictive power of single-source datasets became weaker MLP and SNB performed better than KNN. Moreover, predictive performance saturation may be reached independently of the classification models applied, which may be explained by predictive bias and incompleteness of existing “Gold Standards”. More comprehensive and accurate PPI maps will be produced for S. cerevisiae and beyond with the emergence of largescale datasets of better predictive quality and the integration of intelligent classification methods.

Open Access October 18, 2016

Prediction of transcription factor binding to DNA using rule induction methods

Mikael Huss, Karin Nordström

Page range: 247-263

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Abstract

In this study, we seek to develop a predictive model for finding the strength of binding between a particular transcription factor (TF) variant and a particular DNA target variant. The DNA binding paired domains of the Pax transcription factors, which are our main focus, show seemingly fuzzy and degenerate binding to various DNA targets, and paired domain-DNA binding is not a problem well suited for previously proposed algorithms. Here, we introduce a simple way to use rule induction for predicting the strength of TFDNA binding. We have created a dataset consisting of 597 example cases for paired domain-DNA binding by collecting information about all published and quantified interactions between TF and DNA sequence variants. Application of the rule induction based method on this dataset yields a high, although far from ideal accuracy of 69.7% (based on cross-validation), but perhaps more importantly, several useful rules for predicting the binding strength have been found. Although the primary motivation for introducing the rule induction based methods is the lack of efficient algorithms for paired domain-DNA binding prediction, we also show that the method can be applied with some success to a more well-studied TF-DNA binding prediction task involving the early growth response (EGR) TF family.

Open Access October 18, 2016

Targeted Projection Pursuit Tool for Gene Expression Visualisation

Joe Faith, Michael Brockway

Page range: 264-273

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Abstract

A tool is introduced that uses a novel technique to enable users to explore two-dimensional views of high dimensional gene expression data sets. Unlike other such tools, the interface is intuitive and efficient, allowing the user to easily select views that meet their requirements. The tool is tested on publicly available gene expression data sets and demonstrated to find views that show the seperation of gene expression data sets into classes more effectively than standard dimension-reduction methods.

Open Access October 18, 2016

The BioRS^TM Integration and Retrieval System: An open system for distributed data integration

Andreas Kaps, Konstantin Dyshlevoi, Klaus Heumann, Ralf Jost, Ioannis Kontodinas, Martin Wolff, Jean Hani

Page range: 274-281

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Abstract

Modern academic and industrial research in life sciences generates huge amounts of data and information. To extract knowledge from this information space, optimized integration and retrieval software tools are essential. In the last years, a number of academic as well as commercial systems have been developed to solve this problem. However, as scientific projects are distributed at different locations (e.g., subsidiaries of companies, academic partnerships), data exchange and availability must be realized in a way that avoids data replication. In this article, we describe a global solution for integrating distributed information by applying the BioRS TM Integration and Retrieval System and its inter-BioRS communication capability that goes beyond the standard issue of local data integration. Each site integrates and maintains locally generated data using a local copy of the BioRS software. Applying the inter-BioRS communication, all available BioRS instances can communicate with each other realizing a global network of integrated databanks. All databanks integrated in this network can be accessed from any site without any data replication. This open system allows the addition of new information and sites dynamically. However, access privileges for certain databanks can be maintained on a per user and databank level ensuring data security when required.

Open Access October 18, 2016

MILAN – A medical information-system linking agents to metabolic networks

B. Prins, R. Hofestädt

Page range: 282-294

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Abstract

The genome, the protein-biosynthesis and the metabolism constitute a complex, strongly linked system. The complete functionality of this system is not entirely understood yet. Especially pharmacologists and medical doctors are interested to know how the system works regarding the influence of medications (Drug-Pointing). In the last thirty years biochemical research delivered a huge amount of relevant data for this field. The problem is, that all this data is highly distributed in thousands of databases which are just partly interconnected. Gathering and interconnecting this large amount of distributed data by hand significantly slows down the research process. Hence it is vitally important to create tools in terms of information-systems, which support the process of understanding through automatic data-integration and analysis. In this paper we would like to introduce our approach of a corresponding information-system. MILAN is an extensible datawarehouse framework, which has already integrated about 7.7 million medical publication-abstracts as well as metabolic pathways and information about enzymes, ligands, agents and pharmaceuticals from a collection of major biochemical databases. The system provides a set of functionalities to show where the metabolism is affected by a certain drug and instantly displays the most relevant publications for the particular case. Future development will focus on the integration of more databases and the development of additional functions like detection of side-effects and drug-interactions.

Open Access October 18, 2016

Functional diversity within protein superfamilies

James Casbon, Mansoor Saqi

Page range: 295-304

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Abstract

Structural genomics projects are leading to the discovery of relationships between proteins that would not have been anticipated from consideration of sequence alone. However the assignment of function via structure remains difficult as some structures are compatible with a variety of functions. In this study we explore the relationships between structural diversity and functional diversity within distantly related members of SCOP superfamilies. We use the Gene Ontology functional classification scheme and Greens path entropy to measure functional diversity. We observe a negative correlation between the functional entropy of a superfamily and the size of the conserved core.

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 3 Issue 2 - Special Issue: 3rd Integrative Bioinformatics Workshop, Harpenden, United Kingdom, 2006 / Guest Editors: Paul Kersey, Jacob Koehler, Chris Rawlings, Paul Verrier, Julio Collado-Vides, Ralf Hofestädt, Uwe Scholz

Issue of Journal of Integrative Bioinformatics

CoryneRegNet 2: An Integrative Bioinformatics Approach for Reconstruction and Comparison of Transcriptional Regulatory Networks in Prokaryotes

Abstract

Analysing Microarray Data using the Multi-functional Immune Ontologiser

Abstract

BASIS: an internet resource for network modelling

Abstract

InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET

Abstract

Metabolite profiles as a reflection of physiological status – a methodological validation

Abstract

Modeling Microarray Data: Interpreting and communicating the biological results

Abstract

Statistical Embedding in Complex Biosystems

Abstract

A First Step towards Learning which uORFs Regulate Gene Expression

Abstract

Multi-model inference of network properties from incomplete data

Abstract

Inference of Gene Coexpression Networks by Integrative Analysis across Microarray Experiments

Abstract

BN++ – A Biological Information System

Abstract

Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes

Abstract

A Graphical Notation to Describe the Logical Interactions of Biological Pathways

Abstract

Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments

Abstract

Exact and Approximate Stochastic Simulations of the MAPK Pathway and Comparisons of Simulations’ Results

Abstract

The implications for Bioinformatics of integration across physical scales

Abstract

Data Cleaning and Semantic Improvement in Biological Databases

Abstract

An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions

Abstract

Prediction of transcription factor binding to DNA using rule induction methods

Abstract

Targeted Projection Pursuit Tool for Gene Expression Visualisation

Abstract

The BioRSTM Integration and Retrieval System: An open system for distributed data integration

Abstract

MILAN – A medical information-system linking agents to metabolic networks

Abstract

Functional diversity within protein superfamilies

Abstract

The BioRS^TM Integration and Retrieval System: An open system for distributed data integration