Open Access Published by De Gruyter

Volume 7 Issue 2 - Special Issue: Selected Papers of the International Workshop on Computational and Integrative Biology, Hangzhou, China, 2009 / Guest Editors: Ming Chen, Ralf Hofestädt, Matthias Lange, Falk Schreiber, Andrew Harrison, Paul Verrier, Hiroshi Matsuno

Issue of Journal of Integrative Bioinformatics

Contents
Journal Overview

Open Access October 18, 2016

The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

Matthias Lange, Karl Spies, Joachim Bargsten, Gregor Haberhauer, Matthias Klapperstück, Michael Leps, Christian Weinel, Röbbe Wünschiers, Mandy Weißbach, Jens Stein, Uwe Scholz

Page range: 1-11

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Abstract

Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented. In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

Open Access October 18, 2016

Identifying the impact of G-Quadruplexes on Affymetrix 3′ Arrays using Cloud Computing

Farhat N. Memon, Anne M. Owen, Olivia Sanchez-Graillet, Graham J.G. Upton, Andrew P. Harrison

Page range: 12-20

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Abstract

A tetramer quadruplex structure is formed by four parallel strands of DNA/ RNA containing runs of guanine. These quadruplexes are able to form because guanine can Hoogsteen hydrogen bond to other guanines, and a tetrad of guanines can form a stable arrangement. Recently we have discovered that probes on Affymetrix GeneChips that contain runs of guanine do not measure gene expression reliably. We associate this finding with the likelihood that quadruplexes are forming on the surface of GeneChips. In order to cope with the rapidly expanding size of GeneChip array datasets in the public domain, we are exploring the use of cloud computing to replicate our experiments on 3′ arrays to look at the effect of the location of G-spots (runs of guanines). Cloud computing is a recently introduced high-performance solution that takes advantage of the computational infrastructure of large organisations such as Amazon and Google. We expect that cloud computing will become widely adopted because it enables bioinformaticians to avoid capital expenditure on expensive computing resources and to only pay a cloud computing provider for what is used. Moreover, as well as financial efficiency, cloud computing is an ecologically-friendly technology, it enables efficient data-sharing and we expect it to be faster for development purposes. Here we propose the advantageous use of cloud computing to perform a large data-mining analysis of public domain 3′ arrays.

Open Access October 18, 2016

Integration of -omics data and networks for biomedical research with VANTED

Christian Klukas, Falk Schreiber

Page range: 21-26

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Abstract

Increasingly, research focus in the fields of biology and medicine moves from the investigation of single phenomena to the analysis of complex cause and effect relationships. The clarification of complicated relationships requires the consideration of different domains, such as gene expression, protein, and metabolite data. Furthermore, it is often sensible not to analyze the collected data in isolation, but to consider the context of relevant biological networks. In this paper newly developed functionalities of the VANTED system are presented. They allow users from medicine and biology to interactively structure extensive experimental data, to filter, to evaluate, and to visualize the data and the analysis results in the context of biological networks and classification hierarchies.

Open Access October 18, 2016

Using surveys of Affymetrix GeneChips to study antisense expression

Olivia Sanchez-Graillet, Maria A. Stalteri, Joanna Rowsell, Graham J.G. Upton, Andrew P. Harrison

Page range: 27-48

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Abstract

We have used large surveys of Affymetrix GeneChip HG-U133_Plus_2 data in the public domain to conduct a study of antisense expression across diverse conditions. We derive correlations between groups of probes which map uniquely to the same exon in the antisense direction. When there are no probes assigned to an exon in the sense direction we find that many of the antisense groups fail to detect a coherent block of transcription. We find that only a minority of these groups contain coherent blocks of antisense expression suggesting transcription. We also derive correlations between groups of probes which map uniquely to the same exon in both sense and antisense direction. In some of these cases the locations of sense probes overlap with the antisense probes, and the sense and antisense probe intensities are correlated with each other. This configuration suggests the existence of a Natural Antisense Transcript (NAT) pair. We find the majority of such NAT pairs detected by GeneChips are formed by a transcript of an established gene and either an EST or an mRNA. In order to determine the exact antisense regulatory mechanism indicated by the correlation of sense probes with antisense probes, a further investigation is necessary for every particular case of interest. However, the analysis of microarray data has proved to be a good method to reconfirm known NATs, discover new ones, as well as to notice possible problems in the annotation of antisense transcripts.

Open Access October 18, 2016

Reconstruction of biological networks based on life science data integration

Benjamin Kormeier, Klaus Hippe, Patrizio Arrigo, Thoralf Töpel, Sebastian Janowski, Ralf Hofestädt

Page range: 49-60

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Abstract

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

Open Access October 18, 2016

Towards Prediction and Prioritization of disease genes by the modularity of human phenome-genome assembled network

Jeffrey Q. Jiang, Andreas W. M. Dress, Ming Chen

Page range: 61-84

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Abstract

Empirical clinical studies on the human interactome and phenome not only illustrates prevalent phenotypic overlap and genetic overlap between diseases, but also reveals a modular organization of the genetic landscape of human disease, provding new opportunities to reduce the complexity in dissecting the phenotype-genotype association. We here introduce a network-module based method towards phenotype-genotype association inference and disease gene identification. This approach incorporates protein-protein interaction network, phenotype similarity network and known phenotype-genotype associations into an assembled network. We then decomposes the resulted network into modules (or communities) wherein we identified and prioritized the disease genes from the candidates within the loci associated with the query disease using a linear regression model and concordance score. For the known phenotype-gene associations in the OMIM database, we used the leave-one-out validation to evaluate the feasibility of our method, and successfully ranked known disease genes at top 1 in 887 out of 1807 cases. Moreover, applying this approach on 850 OMIMloci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

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).