VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu     

Independence and reproducibility across microarray platforms

Microarrays have been widely used for the analysis of gene expression, but the issue of reproducibility across platforms has yet to be fully resolved. To address this apparent problem, we compared gene expression between two microarray platforms: the short oligonucleotide Affymetrix Mouse Genome 430 2.0 GeneChip and a spotted cDNA array using a mouse model of angiotensin II-induced hypertension. RNA extracted from treated mice was analyzed using Affymetrix and cDNA platforms and then by quantitative RT-PCR (qRT-PCR) for validation of specific genes. For the 11,710 genes present on both arrays, we assessed the relative impact of experimental treatment and platform on measured expression and found that biological treatment had a far greater impact on measured expression than did platform for more than 90% of genes, a result validated by qRT-PCR. In the small number of cases in which platforms yielded discrepant results, qRT-PCR generally did not confirm either set of data, suggesting that sequence-specific effects may make expression predictions difficult to make using any technique.     

A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics.     

Real-time detection of DNA hybridization on microarray using a CCD-based imaging system equipped with a rotated microlens array disk

This work describes a novel charge-coupled device (CCD)-based imaging system (MB Biochip Reader?) for real-time detection of DNA hybridization to DNA microarrays. The MB Biochip Reader? consisted of a laser light source (532 nm), a microlens array for generation of a multi-beam laser, and a CCD for 2-D signal imaging. The MB Biochip Reader? with a rotated microlens array, allowed large-field imaging (6.2 mm × 7.6 mm with 6.45 μm resolution) with fast time-resolution at 0.2 s without speckle noise. Furthermore, real-time detection of DNA hybridization, which is sufficient to obtain accurate data from tens of thousands of array element per field, was successfully performed without the need for laser scanning. The performance of the MB Biochip Reader? for DNA microarray imaging was similar to the commercially available photomultiplier tube (PMT)-based microarray scanner, ScanArray Lite. The system potentially could be applied toward real-time analysis in many other fluorescent techniques in addition to real-time DNA microarray analysis.     

A high-performance, small-scale microarray for expression profiling of many samples in Arabidopsis-pathogen studies

Studies of the behavior of biological systems often require monitoring of the expression of many genes in a large number of samples. While whole-genome arrays provide high-quality gene-expression profiles, their high cost generally limits the number of samples that can be studied. Although inexpensive small-scale arrays representing genes of interest could be used for many applications, it is challenging to obtain accurate measurements with conventional small-scale microarrays. We have developed a small-scale microarray system that yields highly accurate and reproducible expression measurements. This was achieved by implementing a stable gene-based quantile normalization method for array-to-array normalization, and a probe-printing design that allows use of a statistical model to correct for effects of print tips and uneven hybridization. The array measures expression values in a single sample, rather than ratios between two samples. This allows accurate comparisons among many samples. The array typically yielded correlation coefficients higher than 0.99 between technically duplicated samples. Accuracy was demonstrated by a correlation coefficient of 0.88 between expression ratios determined from this array and an Affymetrix GeneChip, by quantitative RT-PCR, and by spiking known amounts of specific RNAs into the RNA samples used for profiling. The array was used to compare the responses of wild-type, rps2 and ndr1 mutant plants to infection by a Pseudomonas syringae strain expressing avrRpt2. The results suggest that ndr1 affects a defense-signaling pathway(s) in addition to the RPS2-dependent pathway, and indicate that the microarray is a powerful tool for systems analyses of the Arabidopsis disease-signaling network.     

Novel micro‐bioreactor high throughput technology for cell culture process development: Reproducibility and scalability assessment of fed‐batch CHO cultures

With increasing timeline pressures to get therapeutic and vaccine candidates into the clinic, resource intensive approaches such as the use of shake flasks and bench‐top bioreactors may limit the design space for experimentation to yield highly productive processes. The need to conduct large numbers of experiments has resulted in the use of miniaturized high‐throughput (HT) technology for process development. One such high‐throughput system is the SimCell? platform, a robotically driven, cell culture bioreactor system developed by BioProcessors Corp. This study describes the use of the SimCell? micro‐bioreactor technology for fed‐batch cultivation of a GS‐CHO transfectant expressing a model IgG4 monoclonal antibody. Cultivations were conducted in gas‐permeable chambers based on a micro‐fluidic design, with six micro‐bioreactors (MBs) per micro‐bioreactor array (MBA). Online, non‐invasive measurement of total cell density, pH and dissolved oxygen (DO) was performed. One hundred fourteen parallel MBs (19 MBAs) were employed to examine process reproducibility and scalability at shake flask, 3‐ and 100‐L bioreactor scales. The results of the study demonstrate that the SimCell? platform operated under fed‐batch conditions could support viable cell concentrations up to least 12 × 10⁶ cells/mL. In addition, both intra‐MB (MB to MB) as well as intra‐MBA (MBA to MBA) culture performance was found to be highly reproducible. The intra‐MB and ‐MBA variability was calculated for each measurement as the coefficient of variation defined as CV (%) = (standard deviation/mean) × 100. The % CV values for most intra‐MB and intra‐MBA measurements were generally under 10% and the intra‐MBA values were slightly lower than those for intra‐MB. Cell growth, process parameters, metabolic and protein titer profiles were also compared to those from shake flask, bench‐top, and pilot scale bioreactor cultivations and found to be within ±20% of the historical averages. Biotechnol. Bioeng. 2010; 106: 57–67. © 2010 Wiley Periodicals, Inc.     

Quantitative comparison of cDNA-AFLP, microarrays, and GeneChip expression data in Saccharomyces cerevisiae

cDNA-AFLP is a genome-wide expression analysis technology that does not require any prior knowledge of gene sequences. This PCR-based technique combines a high sensitivity with a high specificity, allowing detection of rarely expressed genes and distinguishing between homologous genes. In this report, we validated quantitative expression data of 110 cDNA-AFLP fragments in yeast with DNA microarrays and GeneChip data. The best correlation was found between cDNA-AFLP and GeneChip data. The cDNA-AFLP data revealed a low number of inconsistent profiles that could be explained by gel artifact, overexposure, or mismatch amplification. In addition, 18 cDNA-AFLP fragments displayed homology to genomic yeast DNA, but could not be linked unambiguously to any known ORF. These fragments were most probably derived from 5' or 3' noncoding sequences or might represent previously unidentified ORFs. Genes liable to cross hybridization showed identical results in cDNA-AFLP and GeneChip analysis. Three genes, which were readily detected with cDNA-AFLP, showed no significant expression in GeneChip experiments. We show that cDNA-AFLP is a very good alternative to microarrays and since no preexisting biological or sequence information is required, it is applicable to any species.     

Receiver operating characteristic analysis: a general tool for DNA array data filtration and performance estimation

A critical step for DNA array analysis is data filtration, which can reduce thousands of detected signals to limited sets of genes. Commonly accepted rules for such filtration are still absent. We present a rational approach, based on thresholding of intensities with cutoff levels that are estimated by receiver operating characteristic (ROC) analysis. The technique compares test results with known distributions of positive and negative signals. We apply the method to Atlas cDNA arrays, GeneFilters, and Affymetrix GeneChip. ROC analysis demonstrates similarities in the distribution of false and true positive data for these different systems. We illustrate the estimation of an optimal cutoff level for intensity-based filtration, providing the highest ratio of true to false signals. For GeneChip arrays, we derived filtration thresholds consistent with the reported data based on replicate hybridizations. Intensity-based filtration optimized with ROC combined with other types of filtration (for example, based on significances of differences and/or ratios), should improve DNA array analysis. ROC methodology is also demonstrated for comparison of the performance of different types of arrays, imagers, and analysis software.     

Normalization of microarray data: single-labeled and dual-labeled arrays

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.