Surface free energy and microarray deposition technology

Microarray techniques use a combinatorial approach to assess complex biochemical interactions. The fundamental goal is simultaneous, large-scale experimentation analogous to the automation achieved in the semiconductor industry. However, microarray deposition inherently involves liquids contacting solid substrates. Liquid droplet shapes are determined by surface and interfacial tension forces, and flows during drying. This article looks at how surface free energy and wetting considerations may influence the accuracy and reliability of spotted microarray experiments

MAPPI-DAT : data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments

Microarray detection of human parainfluenzavirus 4 infection associated with respiratory failure in an immunocompetent adult.

A pan-viral DNA microarray, the Virochip (University of California, San Francisco), was used to detect human parainfluenzavirus 4 (HPIV-4) infection in an immunocompetent adult presenting with a life-threatening acute respiratory illness. The virus was identified in an endotracheal aspirate specimen, and the microarray results were confirmed by specific polymerase chain reaction and serological analysis for HPIV-4. Conventional clinical laboratory testing using an extensive panel of microbiological tests failed to yield a diagnosis. This case suggests that the potential severity of disease caused by HPIV-4 in adults may be greater than previously appreciated and illustrates the clinical utility of a microarray for broad-based viral pathogen screening

Comparability of Microarray Data between Amplified and Non Amplified RNA in Colorectal Carcinoma

Microarray analysis reaches increasing popularity during the investigation of prognostic gene clusters in oncology. The standardisation of technical procedures will be essential to compare various datasets produced by different research groups. In several projects the amount of available tissue is limited. In such cases the preamplification of RNA might be necessary prior to microarray hybridisation. To evaluate the comparability of microarray results generated either by amplified or non amplified RNA we isolated RNA from colorectal cancer samples (stage UICC IV) following tumour tissue enrichment by macroscopic manual dissection (CMD). One part of the RNA was directly labelled and hybridised to GeneChips (HG-U133A, Affymetrix), the other part of the RNA was amplified according to the ?Eberwine? protocol and was then hybridised to the microarrays. During unsupervised hierarchical clustering the samples were divided in groups regarding the RNA pre-treatment and 5.726 differentially expressed genes were identified. Using independent microarray data of 31 amplified vs. 24 non amplified RNA samples from colon carcinomas (stage UICC III) in a set of 50 predictive genes we validated the amplification bias. In conclusion microarray data resulting from different pre-processing regarding RNA pre-amplification can not be compared within one analysis

Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

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

Diverse correlation structures in gene expression data and their utility in improving statistical inference

It is well known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source of useful information. We discuss distinct correlation substructures revealed in microarray gene expression data by an appropriate ordering of genes. These substructures include stochastic proportionality of expression signals in a large percentage of all gene pairs, negative correlations hidden in ordered gene triples, and a long sequence of weakly dependent random variables associated with ordered pairs of genes. The reported striking regularities are of general biological interest and they also have far-reaching implications for theory and practice of statistical methods of microarray data analysis. We illustrate the latter point with a method for testing differential expression of nonoverlapping gene pairs. While designed for testing a different null hypothesis, this method provides an order of magnitude more accurate control of type 1 error rate compared to conventional methods of individual gene expression profiling. In addition, this method is robust to the technical noise. Quantitative inference of the correlation structure has the potential to extend the analysis of microarray data far beyond currently practiced methods.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS120 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Nucleic acid - protein fingerprints. Novel protein classification based on nucleic acid - protein recognition

Protein chemistry uses protein description and classification based on molecular mass and isoelectric point as general features. Enzymes are also compared by enzymatic reaction constants, namely Km and kcat values. Proteins are also studied by binding to different oligonucleotides. Here we suggest a simple experimental method for such a comparison of DNA binding proteins, which we call "nucleic acid-protein fingerprints". The experimental design of the method is based on an use of short oligonucleotides immobilized inside microarray of hydrogel cells - biochip. As a first stage, we solved a simple experimental task: what is the shortest single strand oligonucleotide to be recognized by protein? We tested binding of oligonucleotides from 2 to 12 bases, and we have obtained unexpected result that tetranucleotide one is long enough for specific protein binding. This 4-mer can contain two universal bases - 5-nitroindole nucleoside analogue (Ni) and only two meaningful bases, like A, G, T and C. The result obtained opens a way for constructing the simplest protein binding microarray. This microarray consists of 16 meaningful dinucleotides, like AA, AG, CT, GG etc. Physical sequences of all the nucleotides were NiNiAA, etc, where Ni is bound to gel through the amino linker. We prepared such an array and tested it for specific binding of several DNA/RNA binding proteins, labeled with fluorescent dyes like Texas Red of Bodipy. We tested RNase A and Binase for binding on the simplest microarray. It contains only 16 units, and there is a significant difference in the binding patterns. The microarray based on 3-mers must contains 64 units and must have much more specificity. The new principle of protein classification based on nucleic acid-protein recognition has been proposed and experimentally proved. Such an experimental approach must lead to a universal classification of specific DNA/RNA binding proteins