Differential gene expression graphs: A data structure for classification in DNA microarrays

This paper proposes an innovative data structure to be used as a backbone in designing microarray phenotype sample classifiers. The data structure is based on graphs and it is built from a differential analysis of the expression levels of healthy and diseased tissue samples in a microarray dataset. The proposed data structure is built in such a way that, by construction, it shows a number of properties that are perfectly suited to address several problems like feature extraction, clustering, and classificatio

Systems Biology and the Development of Vaccines and Drugs for Malaria Treatments

The sequencing race has ended and the functional race has already begun. Microarray technology enables simultaneous gene expression analysis of thousands of genes, enabling a snapshot of an organisms’ transcriptome at an unprecedented resolution. The close correlation between gene transcription and function, allow the inference of biological processes from the assessed transcriptome profile. Among the sophisticated analytical problems in microarray technology at the front and back ends respectively, are the selection of optimal DNA oligos and computational analysis of the genes expression. In this review paper, we analyse important methods in use today in customized oligos design. In the course of executing this, we discovered that the oligos designer algorithm hanged on gene PFA0135w of chromosome 1, while designing oligos for the gene sequences of Plasmodium falciparum. We do not know the reason for this yet, as the algorithm runs on other sequences like the yeast (Saccharomyces cervisiae) and Neurospora crassa. We conclude the paper highlighting the procedures encompassing the back end phase and discuss their application to the development of vaccines and drugs for malaria treatment. Note that, malaria is the cause of significant global morbidity and mortality with 300-500 million cases annually. Our aims are not ends, but a means to achieve the following: Iterate the need for experimental biologists to (i) know how to design their customized oligos and (ii) have some idea about gene expression analysis and the need for cooperation between experimental biologists and their counterpart, the computational biologists. These will help experimental biologists to coordinate very well the front and the back ends of the system biology analysis of the whole genome effectively

Experimental and computational applications of microarray technology for malaria eradication in Africa

Various mutation assisted drug resistance evolved in Plasmodium falciparum strains and insecticide resistance to female Anopheles mosquito account for major biomedical catastrophes standing against all efforts to eradicate malaria in Sub-Saharan Africa. Malaria is endemic in more than 100 countries and by far the most costly disease in terms of human health causing major losses among many African nations including Nigeria. The fight against malaria is failing and DNA microarray analysis need to keep up the pace in order to unravel the evolving parasite’s gene expression profile which is a pointer to monitoring the genes involved in malaria’s infective metabolic pathway. Huge data is generated and biologists have the challenge of extracting useful information from volumes of microarray data. Expression levels for tens of thousands of genes can be simultaneously measured in a single hybridization experiment and are collectively called a “gene expression profile”. Gene expression profiles can also be used in studying various state of malaria development in which expression profiles of different disease states at different time points are collected and compared to each other to establish a classifying scheme for purposes such as diagnosis and treatments with adequate drugs. This paper examines microarray technology and its application as supported by appropriate software tools from experimental set-up to the level of data analysis. An assessment of the level of microarray technology in Africa, its availability and techniques required for malaria eradication and effective healthcare in Nigeria and Africa in general were also underscored

Physico-chemical foundations underpinning microarray and next-generation sequencing experiments

Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithm

Effect of pooling samples on the efficiency of comparative studies using microarrays

Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample-pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and exact results can be large. In this study, we have characterized quantitatively the effect of pooling samples on the efficiency of microarray experiments for the detection of differential gene expression between two classes. We present exact formulas for calculating the power of microarray experimental designs involving sample pooling and technical replications. The formulas can be used to determine the total numbers of arrays and biological subjects required in an experiment to achieve the desired power at a given significance level. The conditions under which pooled design becomes preferable to non-pooled design can then be derived given the unit cost associated with a microarray and that with a biological subject. This paper thus serves to provide guidance on sample pooling and cost effectiveness. The formulation in this paper is outlined in the context of performing microarray comparative studies, but its applicability is not limited to microarray experiments. It is also applicable to a wide range of biomedical comparative studies where sample pooling may be involved.Comment: 8 pages, 1 figure, 2 tables; to appear in Bioinformatic

A comparative analysis of existing oligonucleotides selection algorithms for microarray technology

In system biology, DNA microarray technology is an indispensable tool for the biological analysis involved at the level of the whole genome. Among the sophisticated analytical problems in microarray technology at the front and back ends, respectively, are the selection of optimal DNA oligonucleotides (henceforth oligos) and computational analysis of the genes expression data. A computational comparative analysis of the methods used to select oligos is important since the design and quality of the microarray probes are of critical importance for the hybridization experiments as well as subsequent analysis of the data. In an attempt to enhance efficient and effective design at the front end, a computational comparative analysis was performed on oligos selection tools using the barley ESTs, as well as the Saccharomyces cerevisiae, Encephalitozoon cuniculi and human genomes. The analysis also shows that a large number of the existing tools are difficult to install and configure. For cross hybridization test, most rely on BLAST and therefore design ill specific oligonucleotides. Furthermore, most are non-intuitive to use and lack important oligo design and software features