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Journal of Integrative Bioinformatics

Journal of Integrative Bioinformatics

Volume 5 Issue 1

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  • Journal Overview
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Conservation of the LexA repressor binding site in Deinococcus radiodurans

Feroz Khan, S. P. Singh, B. N. Mishra October 18, 2016 Page range: 1-56
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Abstract

The LexA protein is a transcriptional repressor of the bacterial SOS DNA repair system, which comprises a set of DNA repair and cellular survival genes that are induced in response to DNA damage. Its varied DNA binding motifs have been characterized and reported in the Escherichia coli, Bacillus subtilis, rhizobia family members, marine magnetotactic bacterium, Salmonella typhimurium and recently in Mycobacterium tuberculosis and this motifs information has been used in our theoretical analysis to detect its novel regulated genes in radio-resistant Deinococcus radiodurans genome. This bacterium showed presence of SOS-box like consensus sequence in the upstream sequences of 3166 genes with >60% motif score similarity percentage (MSSP) on both strands. Attempts to identify LexA-binding sites and the composition of the putative SOS regulon in D. radiodurans have been unsuccessful so far. To resolve the problem we performed theoretical analysis with modifications on reported data set of genes related to DNA repair (61 genes), stress response (145 genes) and some unusual predicted operons (21 clusters). Expression of some of the predicted SOS-box regulated operon members then was examined through the previously reported microarray data which confirm the expression of only single predicted operon i.e. DRB0143 (AAA superfamily NTPase related to 5-methylcytosine specific restriction enzyme subunit McrB) and DRB0144 (homolog of the McrC subunit of the McrBC restriction modification system). The methodology involved weight matrix construction through CONSENSUS algorithm using information of conserved upstream sequences of eight known genes including dinB, tagC, lexA, recA, uvrB, yneA of B. subtilis while lexA and recA of D. radiodurans through phylogenetic footprinting method and later detection of similar conserved SOS-box like LexA binding motifs through both RSAT & PoSSuMsearch programs. The resultant DNA consensus sequence had highly conserved 14 bp SOS-box like binding site.

Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

Nicola Segata, Enrico Blanzieri, Corrado Priami October 18, 2016 Page range: 57-71
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Abstract

The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

WebStruct and VisualStruct: web interfaces and visualization for Structure software implemented in a cluster environment

B. Jayashree, S. Rajgopal, D. Hoisington, V.P. Prasanth, S. Chandra October 18, 2016 Page range: 72-76
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Abstract

Structure, is a widely used software tool to investigate population genetic structure with multi-locus genotyping data. The software uses an iterative algorithm to group individuals into “K” clusters, representing possibly K genetically distinct subpopulations. The serial implementation of this programme is processor-intensive even with small datasets. We describe an implementation of the program within a parallel framework. Speedup was achieved by running different replicates and values of K on each node of the cluster. A web-based user-oriented GUI has been implemented in PHP, through which the user can specify input parameters for the programme. The number of processors to be used can be specified in the background command. A web-based visualization tool “Visualstruct”, written in PHP (HTML and Java script embedded), allows for the graphical display of population clusters output from Structure, where each individual may be visualized as a line segment with K colors defining its possible genomic composition with respect to the K genetic sub-populations. The advantage over available programs is in the increased number of individuals that can be visualized. The analyses of real datasets indicate a speedup of up to four, when comparing the speed of execution on clusters of eight processors with the speed of execution on one desktop. The software package is freely available to interested users upon request.

2.5D Visualisation of Overlapping Biological Networks

David C.Y. Fung, Seok-Hee Hong, Dirk Koschützki, Falk Schreiber, Kai Xu October 18, 2016 Page range: 77-93
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Abstract

Biological data is often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. In this paper, we investigate a new problem of visualising such overlapping biological networks. Two networks overlap if they share some nodes and edges. We present an approach for constructing visualisations of two overlapping networks, based on a restricted three dimensional representation. More specifically, we use three parallel two dimensional planes placed in three dimensions to represent overlapping networks: one for each network (the top and the bottom planes) and one for the overlapping part (in the middle plane). Our method aims to achieve both drawing aesthetics (or conventions) for each individual network, and highlighting the intersection part by them. Using three biological datasets, we evaluate our visualisation design with the aim to test whether overlapping networks can support the visual analysis of heterogeneous and yet interconnected networks.

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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