Open Access Published by De Gruyter

Volume 14 Issue 3 - Special Issue: Biological Big Bytes: Integrative analysis of large biological datasets / Guest Editors: Ming Chen, Andrew Harrison, Hugh Shanahan, Yuriy Orlov

September 2017

Issue of Journal of Integrative Bioinformatics

Contents
Journal Overview

Editorial

Open Access September 13, 2017

Biological Big Bytes: Integrative Analysis of Large Biological Datasets

Ming Chen, Andrew Harrison, Hugh Shanahan, Yuriy Orlov

Article number: 20170052

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Review

Open Access September 23, 2017

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

Graeme Benstead-Hume, Sarah K. Wooller, Frances M.G. Pearl

Article number: 20170027

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Abstract

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use ‘omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Research Articles

Open Access September 23, 2017

A Novel Method to Detect Bias in Short Read NGS Data

Jamie Alnasir, Hugh P. Shanahan

Article number: 20170025

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Abstract

Detecting sources of bias in transcriptomic data is essential to determine signals of Biological significance. We outline a novel method to detect sequence specific bias in short read Next Generation Sequencing data. This is based on determining intra-exon correlations between specific motifs. This requires a mild assumption that short reads sampled from specific regions from the same exon will be correlated with each other. This has been implemented on Apache Spark and used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set in drosophila indicates a variation due to motif GC content that is more significant than that found due to exon GC content. The software is available online and could be applied for cross-experiment transcriptome data analysis in eukaryotes.

Open Access August 10, 2017

MUSTv2: An Improved De Novo Detection Program for Recently Active Miniature Inverted Repeat Transposable Elements (MITEs)

Ruiquan Ge, Guoqin Mai, Ruochi Zhang, Xundong Wu, Qing Wu, Fengfeng Zhou

Article number: 20170029

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Abstract

Background Miniature inverted repeat transposable element (MITE) is a short transposable element, carrying no protein-coding regions. However, its high proliferation rate and sequence-specific insertion preference renders it as a good genetic tool for both natural evolution and experimental insertion mutagenesis. Recently active MITE copies are those with clear signals of Terminal Inverted Repeats (TIRs) and Direct Repeats (DRs), and are recently translocated into their current sites. Their proliferation ability renders them good candidates for the investigation of genomic evolution. Results This study optimizes the C++ code and running pipeline of the MITE Uncovering SysTem (MUST) by assuming no prior knowledge of MITEs required from the users, and the current version, MUSTv2, shows significantly increased detection accuracy for recently active MITEs, compared with similar programs. The running speed is also significantly increased compared with MUSTv1. We prepared a benchmark dataset, the simulated genome with 150 MITE copies for researchers who may be of interest. Conclusions MUSTv2 represents an accurate detection program of recently active MITE copies, which is complementary to the existing template-based MITE mapping programs. We believe that the release of MUSTv2 will greatly facilitate the genome annotation and structural analysis of the bioOMIC big data researchers.

Open Access September 18, 2017

Computer Analysis of Glioma Transcriptome Profiling: Alternative Splicing Events

Vladimir N. Babenko, Natalya V. Gubanova, Anatoly O. Bragin, Irina V. Chadaeva, Gennady V. Vasiliev, Irina V. Medvedeva, Alexey S. Gaytan, Alexey L. Krivoshapkin, Yuriy L. Orlov

Article number: 20170022

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Abstract

Here we present the analysis of alternative splicing events on an example of glioblastoma cell culture samples using a set of computer tools in combination with database integration. The gene expression profiles of glioblastoma were obtained from cell culture samples of primary glioblastoma which were isolated and processed for RNA extraction. Transcriptome profiling of normal brain samples and glioblastoma were done by Illumina sequencing. The significant differentially expressed exon-level probes and their corresponding genes were identified using a combination of the splicing index method. Previous studies indicated that tumor-specific alternative splicing is important in the regulation of gene expression and corresponding protein functions during cancer development. Multiple alternative splicing transcripts have been identified as progression markers, including generalized splicing abnormalities and tumor- and stage-specific events. We used a set of computer tools which were recently applied to analysis of gene expression in laboratory animals to study differential splicing events. We found 69 transcripts that are differentially alternatively spliced. Three cancer-associated genes were considered in detail, in particular: APP (amyloid beta precursor protein), CASC4 (cancer susceptibility candidate 4) and TP53. Such alternative splicing opens new perspectives for cancer research.

Open Access September 13, 2017

RNA-Seq Mouse Brain Regions Expression Data Analysis: Focus on ApoE Functional Network

Vladimir N. Babenko, Dmitry A. Smagin, Natalia N. Kudryavtseva

Article number: 20170024

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Abstract

ApoE expression status was proved to be a highly specific marker of energy metabolism rate in the brain. Along with its neighbor, Translocase of Outer Mitochondrial Membrane 40 kDa (TOMM40) which is involved in mitochondrial metabolism, the corresponding genomic region constitutes the neuroenergetic hotspot. Using RNA-Seq data from a murine model of chronic stress a significant positive expression coordination of seven neighboring genes in ApoE locus in five brain regions was observed. ApoE maintains one of the highest absolute expression values genome-wide, implying that ApoE can be the driver of the neighboring gene expression alteration observed under stressful loads. Notably, we revealed the highly statistically significant increase of ApoE expression in the hypothalamus of chronically aggressive (FDR < 0.007) and defeated (FDR < 0.001) mice compared to the control. Correlation analysis revealed a close association of ApoE and proopiomelanocortin ( Pomc ) gene expression profiles implying the putative neuroendocrine stress response background of ApoE expression elevation therein.

Open Access July 21, 2017

A Generalized Approach for Measuring Relationships Among Genes

Lijun Wang, Md. Asif Ahsan, Ming Chen

Article number: 20170026

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Abstract

Several methods for identifying relationships among pairs of genes have been developed. In this article, we present a generalized approach for measuring relationships between any pairs of genes, which is based on statistical prediction. We derive two particular versions of the generalized approach, least squares estimation (LSE) and nearest neighbors prediction (NNP). According to mathematical proof, LSE is equivalent to the methods based on correlation; and NNP is approximate to one popular method called the maximal information coefficient (MIC) according to the performances in simulations and real dataset. Moreover, the approach based on statistical prediction can be extended from two-genes relationships to multi-genes relationships. This application would help to identify relationships among multi-genes.

Open Access September 1, 2017

Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis

Md. Matiur Rahaman, Md. Asif Ahsan, Zeeshan Gillani, Ming Chen

Article number: 20170028

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Abstract

Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.

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).