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Journal of Integrative Bioinformatics

Journal of Integrative Bioinformatics

Volume 11 Issue 1

  • Contents
  • Journal Overview
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Integrated visualization of a multi-omics study of starvation in mouse intestine

Martijn P. van Iersel, Milka Sokolović, Kaatje Lenaerts, Martina Kutmon, Freek G. Bouwman, Wouter H. Lamers, Edwin C.M. Mariman, Chris T. Evelo October 18, 2016 Page range: 1-16
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Abstract

Our understanding of complex biological processes can be enhanced by combining different kinds of high-throughput experimental data, but the use of incompatible identifiers makes data integration a challenge. We aimed to improve methods for integrating and visualizing different types of omics data. To validate these methods, we applied them to two previous studies on starvation in mice, one using proteomics and the other using transcriptomics technology. We extended the PathVisio software with new plugins to link proteins, transcripts and pathways. A low overall correlation between proteome and transcriptome data was detected (Spearman rank correlation: 0.21). At the level of individual genes, correlation was highly variable. Many mRNA/protein pairs, such as fructose biphosphate aldolase B and ATP Synthase, show good correlation. For other pairs, such as ferritin and elongation factor 2, an interesting effect is observed, where mRNA and protein levels change in opposite directions, suggesting they are not primarily regulated at the transcriptional level. We used pathway diagrams to visualize the integrated datasets and found it encouraging that transcriptomics and proteomics data supported each other at the pathway level. Visualization of the integrated dataset on pathways led to new observations on gene-regulation in the response of the gut to starvation. Our methods are generic and can be applied to any multi-omics study. The PathVisio software can be obtained at http://www.pathvisio.org. Supplemental data are available at http://www.bigcat.unimaas.nl/data/jib-supplemental/, including instructions on reproducing the pathway visualizations of this manuscript.

LmTDRM Database: A Comprehensive Database on Thiol Metabolic Gene/Gene Products in Listeria monocytogenes EGDe

Vanishree Srinivas, Shubha Gopal October 18, 2016 Page range: 17-29
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Abstract

There are a number of databases on the Listeria species and about their genome. However, these databases do not specifically address a set of network that is important in defence mechanism of the bacteria. Listeria monocytogenes EGDe is a well-established intracellular model organism to study host pathogenicity because of its versatility in the host environment. Here, we have focused on thiol disulphide redox metabolic network proteins, specifically in L. monocytogenes EGDe. The thiol redox metabolism is involved in oxidative stress mechanism and is found in all living cells. It functions to maintain the thiol disulphide balance required for protein folding by providing reducing power. Nevertheless, they are involved in the reversible oxidation of thiol groups in biomolecules by creating disulphide bonds; therefore, the term thiol disulphide redox metabolism (TDRM). TDRM network genes play an important role in oxidative stress mechanism and during host-pathogen interaction. Therefore, it is essential to have detailed information on these proteins with regard to other bacteria and its genome analysis to understand the presence of tRNA, transposons, and insertion elements for horizontal gene transfer. LmTDRM database is a new comprehensive web-based database on thiol proteins and their functions. It includes: Description, Search, TDRM analysis, and genome viewer. The quality of these data has been evaluated before they were aggregated to produce a final representation. The web interface allows for various queries to understand the protein function and their annotation with respect to their relationship with other bacteria. LmTDRM is a major step towards the development of databases on thiol disulphide redox proteins; it would definitely help researchers to understand the mechanism of these proteins and their interaction. Database URL: www.lmtdrm.com

Profiling of Genetic Switches using Boolean Implications in Expression Data

M. Volkan Çakır, Hans Binder, Henry Wirth October 18, 2016 Page range: 30-54
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Abstract

Correlation analysis assuming coexpression of the genes is a widely used method for gene expression analysis in molecular biology. Yet growing extent, quality and dimensionality of the molecular biological data permits emerging, more sophisticated approaches like Boolean implications. We present an approach which is a combination of the SOM (self organizing maps) machine learning method and Boolean implication analysis to identify relations between genes, metagenes and similarly behaving metagene groups (spots). Our method provides a way to assign Boolean states to genes/metagenes/spots and offers a functional view over significantly variant elements of gene expression data on these three different levels. While being able to cover relations between weakly correlated entities Boolean implication method also decomposes these relations into six implication classes. Our method allows one to validate or identify potential relationships between genes and functional modules of interest and to assess their switching behaviour. Furthermore the output of the method renders it possible to construct and study the network of genes. By providing logical implications as updating rules for the network it can also serve to aid modelling approaches.

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).

> Information on submission process

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