Open Access Published by De Gruyter

Volume 9 Issue 2 - Special Issue: 8th International Symposium on Integrative Bioinformatics, Hangzhou, China, 2012 / Guest Editors: Ming Chen, Ping Wu, Jun Zhu, Andrew Harrison, Ralf Hofestädt, Falk Schreiber, Matthias Lange, Sean May

Issue of Journal of Integrative Bioinformatics

Contents
Journal Overview

Open Access October 18, 2016

IDPredictor: predict database links in biomedical database

Hendrik Mehlhorn, Matthias Lange, Uwe Scholz, Falk Schreiber

Page range: 1-15

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Abstract

Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments, an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit of supporting the concept of an integrated data view is to acquire insights into cross-references among database entities. This issue is being hampered by the fact, that only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predicts and extracts cross-references from multiple life science databases and possible referenced data targets. We study the retrieval quality of our method and report on first, promising results. The method is implemented as the tool IDPredictor, which is published under the DOI 10.5447/IPK/2012/4 and is freely available using the URL: http://dx.doi.org/10.5447/IPK/2012/4.

Open Access October 18, 2016

Analysis of high-throughput plant image data with the information system IAP

Christian Klukas, Jean-Michel Pape, Alexander Entzian

Page range: 16-18

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Abstract

This work presents a sophisticated information system, the Integrated Analysis Platform (IAP), an approach supporting large-scale image analysis for different species and imaging systems. In its current form, IAP supports the investigation of Maize, Barley and Arabidopsis plants based on images obtained in different spectra. Several components of the IAP system, which are described in this work, cover the complete end-to-end pipeline, starting with the image transfer from the imaging infrastructure, (grid distributed) image analysis, data management for raw data and analysis results, to the automated generation of experiment reports.

Open Access October 18, 2016

Oncology In Vivo Data Integration for Hypothesis Generation

Jia Wei, Ming Chen

Page range: 19-27

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Abstract

AstraZeneca’s Oncology in vivo data integration platform brings multidimensional data from animal model efficacy, pharmacokinetic and pharmacodynamic data to animal model profiling data and public in vivo studies. Using this platform, scientists can cluster model efficacy and model profiling data together, quickly identify responder profiles and correlate molecular characteristics to pharmacological response. Through meta-analysis, scientists can compare pharmacology between single and combination treatments, between different drug scheduling and administration routes.

Open Access October 18, 2016

Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights

Xiu-Jun Gong, Hua Yu, Chun-Bai Yang, Yuan-Fang Li

Page range: 28-39

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Abstract

The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS) and housekeeping (HK) genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA) and Hypergeometric Analysis (HA) against three biological ontologies: Gene Ontology (GO), KEGG pathways and Disease Ontology (DO) respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease) are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM)” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC)” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO) for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.

Open Access October 18, 2016

Detection of Cardiac Geometry via Difference Intensity of Echocardiogram Images

Mohd Azrul Hisham Mohd Adib, Mohd Fadhlan Yusof, Ahmad Zulkifli, Mohd Hasni Nur Hazreen

Page range: 40-45

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Abstract

Echocardiogram is an ultrasound image of the heart that demonstrates the size, motion and composition of cardiac structures and is also used to diagnose various abnormalities of the heart including abnormal chamber size, shape and congenital heart disease. Echocardiography provides important morphological and functional details of the heart. Most of the presented automatic cardiac disease recognition systems that use echocardiograms based on defective anatomical region detection. In this paper we present a simple technique for cardiac geometry detection via echocardiogram images which conquer these borders and exploits cues from cardiac structure. To demonstrate the effectiveness of this technique, we present results for cardiac geometry detection through difference intensity of echocardiogram images. We have developed a simple program code for the prediction of cardiac geometry using difference intensity of echocardiogram images. With this code, users can generate node or point for detection of cardiac geometry as ventricle and atrium in size, shape and location.

Open Access October 18, 2016

Nutrilyzer: A Tool for Deciphering Atomic Stoichiometry of Differentially Expressed Paralogous Proteins

Katrin Lotz, Falk Schreiber, Röbbe Wünschiers

Page range: 46-52

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Abstract

Organisms try to maintain homeostasis by balanced uptake of nutrients from their environment. From an atomic perspective this means that, for example, carbon:nitrogen:sulfur ratios are kept within given limits. Upon limitation of, for example, sulfur, its acquisition is triggered. For yeast it was shown that transporters and enzymes involved in sulfur up- take are encoded as paralogous genes that express different isoforms. Sulfur deprivation leads to up-regulation of isoforms that are poor in sulfur-containing amino acids, that is, methinone and cysteine. Accordingly, sulfur-rich isoforms are down-regulated. We developed a web-based software, doped Nutrilyzer, that extracts paralogous protein coding sequences from an annotated genome sequence and evaluates their atomic compo- sition. When fed with gene-expression data for nutrient limited and normal conditions, Nutrilyzer provides a list of genes that are significantly differently expressed and simul- taneously contain significantly different amounts of the limited nutrient in their atomic composition. Its intended use is in the field of ecological stoichiometry. Nutrilyzer is available at http://nutrilyzer.hs-mittweida.de. Here we describe the work flow and results with an example from a whole-genome Ara- bidopsis thaliana gene-expression analysis upon oxygen deprivation. 43 paralogs distributed over 37 homology clusters were found to be significantly differently expressed while containing significantly different amounts of oxygen.

Open Access October 18, 2016

Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing

Peng Sun, Jiong Guo, Jan Baumbach

Page range: 53-67

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Abstract

The explosion of biological data has largely influenced the focus of today’s biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data from different types, such as phenotypes and genotypes. This data is modelled as bi-partite graphs where nodes correspond to the different data points, mutations and diseases for instance, and weighted edges relate to associations between them. Bi-clustering is a special case of clustering designed for partitioning two different types of data simultaneously. We present a bi-clustering approach that solves the NP-hard weighted bi-cluster editing problem by transforming a given bi-partite graph into a disjoint union of bi-cliques. Here we contribute with an exact algorithm that is based on fixed-parameter tractability. We evaluated its performance on artificial graphs first. Afterwards we exemplarily applied our Java implementation to data of genome-wide association studies (GWAS) data aiming for discovering new, previously unobserved geno-to-pheno associations. We believe that our results will serve as guidelines for further wet lab investigations. Generally our software can be applied to any kind of data that can be modelled as bi-partite graphs. To our knowledge it is the fastest exact method for weighted bi-cluster editing problem.

Open Access October 18, 2016

Transcription Network Analysis by A Sparse Binary Factor Analysis Algorithm

Shikui Tu, Runsheng Chen, Lei Xu

Page range: 68-79

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Abstract

Transcription factor activities (TFAs), rather than expression levels, control gene expres- sion and provide valuable information for investigating TF-gene regulations. The underly- ing bimodal or switch-like patterns of TFAs may play important roles in gene regulation. Network Component Analysis (NCA) is a popular method to deduce TFAs and TF-gene control strengths from microarray data. However, it does not directly examine the bimodal- ity of TFAs and it needs the TF-gene connection topology to be a priori known. In this paper, we modify NCA to model gene expression regulation by Binary Factor Analysis (BFA), which directly captures switch-like patterns of TFAs. Moreover, sparse technique is employed on the mixing matrix of BFA, and thus the proposed sparse BYY-BFA al- gorithm, developed under Bayesian Ying-Yang (BYY) learning framework, can not only uncover the latent TFA profile’s switch-like patterns, but also be capable of automatically shutting off the unnecessary connections. Simulation study demonstrates the effectiveness of BYY-BFA, and a preliminary application to Saccharomyces cerevisiae cell cycle data and Escherichia coli carbon source transition data shows that the reconstructed binary pat- terns of TFAs by BYY-BFA are consistent with the ups and downs of TFAs by NCA, and that BYY-BFA also works well when the network topology is unknown.

Open Access October 18, 2016

Predicting breast cancer chemotherapeutic response using a novel tool for microarray data analysis

Jie Cheng, Joel Greshock, Jeffery Painter, Xiwu Lin, Kwan Lee, Shu Zheng, Alan Menius

Page range: 80-87

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Abstract

We developed a novel tool for microarray data analysis that can parsimoniously discover highly predictive genes by finding the optimal trade off between fold change and t-test p value through rigorous cross validation. In addition to find a small set of highly predictive genes, the tool also has a procedure that recursively discovers and removes predictive genes from the dataset until no such genes can be found. We applied our tool to a public breast cancer dataset with the goal to discover genes that can predict patient’s response to a preoperative chemotherapy. The results show that estrogen receptor (ER) gene is the most important gene to predict chemotherapeutic response and no gene signatures can add much clinical benefit for the whole patient population. We further identified a clinically homogenous subgroup of patients (ER-negative, PR-negative and HER2-negative) whose response to the chemotherapy can be reasonably predicted. Many of the discovered predictive markers for this subgroup of patients were successfully validated using a blinded validation set.

Open Access October 18, 2016

Computer and Statistical Analysis of Transcription Factor Binding and Chromatin Modifications by ChIP-seq data in Embryonic Stem Cell

Yuriy Orlov, Han Xu, Dmitri Afonnikov, Bing Lim, Jian-Chien Heng, Ping Yuan, Ming Chen, Junli Yan, Neil Clarke, Nina Orlova, Mikael Huss, Konstantin Gunbin, Nikolay Podkolodnyy, Huck-Hui Ng

Page range: 88-100

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Abstract

Advances in high throughput sequencing technology have enabled the identification of transcription factor (TF) binding sites in genome scale. TF binding studies are important for medical applications and stem cell research. Somatic cells can be reprogrammed to a pluripotent state by the combined introduction of factors such as Oct4, Sox2, c-Myc, Klf4. These reprogrammed cells share many characteristics with embryonic stem cells (ESCs) and are known as induced pluripotent stem cells (iPSCs). The signaling requirements for maintenance of human and murine embryonic stem cells (ESCs) differ considerably. Genome wide ChIP-seq TF binding maps in mouse stem cells include Oct4, Sox2, Nanog, Tbx3, Smad2 as well as group of other factors. ChIP-seq allows study of new candidate transcription factors for reprogramming. It was shown that Nr5a2 could replace Oct4 for reprogramming. Epigenetic modifications play important role in regulation of gene expression adding additional complexity to transcription network functioning. We have studied associations between different histone modification using published data together with RNA Pol II sites. We found strong associations between activation marks and TF binding sites and present it qualitatively. To meet issues of statistical analysis of genome ChIP-sequencing maps we developed computer program to filter out noise signals and find significant association between binding site affinity and number of sequence reads. The data provide new insights into the function of chromatin organization and regulation in stem cells.

Open Access October 18, 2016

Microbase2.0: A Generic Framework for Computationally Intensive Bioinformatics Workflows in the Cloud

Keith Flanagan, Sirintra Nakjang, Jennifer Hallinan, Colin Harwood, Robert P. Hirt, Matthew R. Pocock, Anil Wipat

Page range: 101-112

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Abstract

As bioinformatics datasets grow ever larger, and analyses become increasingly complex, there is a need for data handling infrastructures to keep pace with developing technology. One solution is to apply Grid and Cloud technologies to address the computational requirements of analysing high throughput datasets. We present an approach for writing new, or wrapping existing applications, and a reference implementation of a framework, Microbase2.0, for executing those applications using Grid and Cloud technologies. We used Microbase2.0 to develop an automated Cloud-based bioinformatics workflow executing simultaneously on two different Amazon EC2 data centres and the Newcastle University Condor Grid. Several CPU years’ worth of computational work was performed by this system in less than two months. The workflow produced a detailed dataset characterising the cellular localisation of 3,021,490 proteins from 867 taxa, including bacteria, archaea and unicellular eukaryotes. Microbase2.0 is freely available from http://www.microbase.org.uk/.

Open Access October 18, 2016

Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

Xiaogang Wu, Christoph Reinhard, Shuyu D. Li, Hui Huang, Tao Wei, Ragini Pandey, Jake Y. Chen

Page range: 113-125

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Abstract

In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

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).