Heterologous microarray experiments used to identify the early gene response to heat stress in a coral reef fish

Coral reef fishes are expected to experience rising sea surface temperatures due to climate change. How well tropical reef fishes will respond to these increased temperatures and which genes are important in the response to elevated temperatures is not known. Microarray technology provides a powerful tool for gene discovery studies, but the development of microarrays for individual species can be expensive and time-consuming. In this study, we tested the suitability of a Danio rerio oligonucleotide microarray for application in a species with few genomic resources, the coral reef fish Pomacentrus moluccensis. Results from a comparative genomic hybridization experiment and direct sequence comparisons indicate that for most genes there is considerable sequence similarity between the two species, suggesting that the D. rerio array is useful for genomic studies of P. moluccensis. We employed this heterologous microarray approach to characterize the early transcriptional response to heat stress in P. moluccensis. A total of 111 gene loci, many of which are involved in protein processing, transcription, and cell growth, showed significant changes in transcript abundance following exposure to elevated temperatures. Changes in transcript abundance were validated for a selection of candidate genes using quantitative real-time polymerase chain reaction. This study demonstrates that heterologous microarrays can be successfully employed to study species for which specific microarrays have not yet been developed, and so have the potential to greatly enhance the utility of microarray technology to the field of environmental and functional genomics.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

A survey of approaches and frameworks to carry out genomic data analysis on the cloud

High Performance Computing (HPC) clouds have started to change the way how research in science, in particular medicine and genomics (bioinformatics) is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. However, most HPC clouds are provided at an Infrastructure as a Service (IaaS) level, users are presented with a set of virtual servers which need to be put together to form HPC environments via time consuming resource management and software configuration tasks, which make them practically unusable by discipline, non-computing specialists. In response, there is a new trend to expose cloud applications as services to simplify access and execution on clouds. This paper firstly examines commonly used cloud-based genomic analysis services (Tuxedo Suite, Galaxy and Cloud Bio Linux). As a follow up, we propose two new solutions (HPCaaS and Uncinus), which aim to automate aspects of the service development and deployment process. By comparing and contrasting these five solutions, we identify key mechanisms of service creation, execution and access that are required to support genomic research on the SaaS cloud, in particular by discipline specialists. © 2014 IEEE.

Selected approaches and frameworks to carry out genomic data provision and analysis on the cloud

While High Performance Computing clouds allow researchers to process large amounts of genomic data, complex resource and software configuration tasks must be carried out beforehand. The current trend exposes applications and data as services, simplifying access to clouds. This paper examines commonly used cloud-based genomic analysis services, introduces the approach of exposing data as services and proposes two new solutions (HPCaaS and Uncinus) which aim to automate service development, deployment process and data provision. By comparing and contrasting these solutions, we identify key mechanisms of service creation, execution and data access required to support non-computing specialists employing clouds.

Paediatric leukaemia DNA methylation profiling using MBD enrichment and SOLiD sequencing on archival bone marrow smears

BACKGROUND: Acute Lymphoblastic Leukaemia (ALL) is the most common cancer in children. Over the past four decades, research has advanced the treatment of this cancer from a less than 60% chance of survival to over 85% today. The causal molecular mechanisms remain unclear. Here, we performed sequencing-based genomic DNA methylation profiling of eight paediatric ALL patients using archived bone marrow smear microscope slides. FINDINGS: SOLiD™ sequencing data was collected from Methyl-Binding Domain (MBD) enriched fractions of genomic DNA. The primary tumour and remission bone marrow sample was analysed from eight patients. Four patients relapsed and the relapsed tumour was analysed. Input and MBD-enriched DNA from each sample was sequenced, aligned to the hg19 reference genome and analysed for enrichment peaks using MACS (Model-based Analysis for ChIP-Seq) and HOMER (Hypergeometric Optimization of Motif EnRichment). In total, 3.67 gigabases (Gb) were sequenced, 2.74 Gb were aligned to the reference genome (average 74.66% alignment efficiency). This dataset enables the interrogation of differential DNA methylation associated with paediatric ALL. Preliminary results reveal concordant regions of enrichment indicative of a DNA methylation signature. CONCLUSION: Our dataset represents one of the first SOLiD™MBD-Seq studies performed on paediatric ALL and is the first to utilise archival bone marrow smears. Differential DNA methylation between cancer and equivalent disease-free tissue can be identified and correlated with existing and published genomic studies. Given the rarity of paediatric haematopoietic malignancies, relative to adult counterparts, our demonstration of the utility of archived bone marrow smear samples to high-throughput methylation sequencing approaches offers tremendous potential to explore the role of DNA methylation in the aetiology of cancer.

A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts

The computational approach for identifying promoters on increasingly large genomic sequences has led to many false positives. The biological significance of promoter identification lies in the ability to locate true promoters with and without prior sequence contextual knowledge. Prior approaches to promoter modelling have involved artificial neural networks (ANNs) or hidden Markov models (HMMs), each producing adequate results on small scale identification tasks, i.e. narrow upstream regions. In this work, we present an architecture to support prokaryote promoter identification on large scale genomic sequences, i.e. not limited to narrow upstream regions. The significant contribution involved the hybrid formed via aggregation of the profile HMM with the ANN, via Viterbi scoring optimizations. The benefit obtained using this architecture includes the modelling ability of the profile HMM with the ability of the ANN to associate elements composing the promoter. We present the high effectiveness of the hybrid approach in comparison to profile HMMs and ANNs when used separately. The contribution of Viterbi optimizations is also highlighted for supporting the hybrid architecture in which gains in sensitivity (+0.3), specificity (+0.65) and precision (+0.54) are achieved over existing approaches.

Early breast cancer identification : Which way to go? Microarray or image based computer aided diagnosis!

The goal of this research is to develop a computer aided diagnostic (CAD) system that can detect breast cancer in the early stage by using microarray and image data. We verified the performance of six well known classification algorithms with various performance matrices. Although we do not suggest a unique classifier algorithm for a CAD system, we do identify a number of algorithms whose performance is very promising. The algorithms performance was validated by 3 images dataset; two have been used for the first time in this experiment. Multidimensional image filtering is adopted for the final data extraction. The image data classification performance is compared with microarray data. Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach.

An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.

Simple and signal-off electrochemical biosensor for mercury(II) based on thymine-mercury-thymine hybridization directly on graphene

Abstract A simple, signal-off and reusable electrochemical biosensor was developed for sensitive and selective detection of mercury(II) based on thymine-mercury(II)-thymine (T-Hg²⁺-T) complex and the remarkable difference in the affinity of graphene with double strand DNA (ds-DNA) and single strand DNA (ss-DNA). Our system was composed of ferrocene-tagged probe DNA and graphene. Due to the noncovalent assembly, the ferrocene-tagged probe ss-DNA was immobilized on the surface of graphene nanosheets directly and employed to amplify the electrochemical signal. In the presence of Hg²⁺, the ferrocene-labeled T-rich DNA probe hybridized with target probe to form ds-DNA via the Hg²⁺-mediated coordination of T-Hg²⁺-T base pairs. As a result, the duplex DNA complex kept away from the graphene surface due to the weak affinity of graphene and ds-DNA, and the redox current decreased substantially. Meanwhile, the graphene decorated GCE surface was released for the reusability. Under the optimal conditions, the proposed sensor showed a linear concentration range from 25 pM to 10 μM with a detection limit of 5 pM for Hg²⁺ detection. The strategy afforded exquisite selectivity for Hg²⁺ against other metal ions in real environmental samples.

Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene flourescent in situ by hybridization

Background Changes in the composition of gastrointestinal microbiota by dietary interventions using pro- and prebiotics provide opportunity for improving health and preventing disease. However, the capacity of lupin kernel fiber (LKFibre), a novel legume-derived food ingredient, to act as a prebiotic and modulate the colonic microbiota in humans needed investigation.

Aim of the study The present study aimed to determine the effect of LKFibre on human intestinal microbiota by quantitative fluorescent in situ hybridization (FISH) analysis.

Design A total of 18 free-living healthy males between the ages of 24 and 64 years consumed a control diet and a LKFibre diet (containing an additional 17–30 g/day fiber beyond that of the control—incorporated into daily food items) for 28 days with a 28-day washout period in a single-blind, randomized, crossover dietary intervention design.
Methods Fecal samples were collected for 3 days towards the end of each diet and microbial populations analyzed by FISH analysis using 16S rRNA gene-based oligonucleotide probes targeting total and predominant microbial populations.

Results Significantly higher levels of Bifidobacterium spp. (P = 0.001) and significantly lower levels of the clostridia group of C. ramosum, C. spiroforme and C. cocleatum (P = 0.039) were observed on the LKFibre diet compared with the control. No significant differences between the LKFibre and the control diet were observed for total bacteria, Lactobacillus spp., the Eubacterium spp., the C. histolyticum/C. lituseburense group and the Bacteroides–Prevotella group.
Conclusions Ingestion of LKFibre stimulated colonic bifidobacteria growth, which suggests that this dietary fiber may be considered as a prebiotic and may beneficially contribute to colon health.

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.

A genomic approach to understanding the cause and effect of annual ryegrass toxicity

Annual Ryegrass Toxicity (ARGT) is a potentially lethal disease affecting livestock grazing on pastures or consuming fodder that include annual ryegrass (Lolium rigidum) contaminated with corynetoxins. The corynetoxins (CTs), among the most lethal toxins produced in nature, are produced by the bacterium Rathayibacter toxicus that uses a nematode vector to attach to and infect the seedheads of L.rigidum. There is little known of the factors that control toxin production. Several studies have speculated that a bacteriophage specific to R.toxicus may be implicated in CT production. We have developed a PCR-based assay to test for both bacterium and phage in ryegrass material and results indicate that there is a correlation between phage and bacterial presence in all toxic ryegrass samples tested so far. This PCR-based technique may ultimately allow for a rapid, high-throughput screening assay to identify potentially toxic pastures and feed in the field. Currently, ~80% of the 45 Kb genome has been sequenced an investigation to further elucidate its potential role in toxin production.Furthermore, specific alterations in gene expression as a result of exposure to CTs or the closely related tunicamycins (TMs), which are commercially available and considered biologically indistinguishable from CTs, will be evaluated for use as biomarkers of exposure. The effects of both toxins will be analysed in vitro using a rat hepatocyte cell line and screened on a low-density DNA micro array “CT-Chip” that contains <100 selected rat hepatic genes. The results are expected to further define the bioequivalence of CTs and TMs and to identify levels of exposure that are related to specific toxic effects or have no adverse effect on livestock.