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.     

Signal deconvolution based expression-detection and background adjustment for microarray data.

Background adjustment is an essential stage in analyzing DNA microarrays. Discriminating expressed genes from unexpressed ones (expression detection), and estimating the expression levels of weakly expressed genes, critically depend on accurate treatment of the background intensity. Current methods for background adjustment either do not deal with nonspecific hybridization or strongly depend on the reliability of control probes. Existing model-based methods have limited accuracy. A new platform-independent background adjustment algorithm is presented. The algorithm relies on the deconvoluted experimental signal distribution for evaluating the expression probability and adjusting the background of each probe. Considering expression detection, it is shown, for two-channels cDNA arrays and for the Affymetrix GeneChip platform, that the algorithm performs at least as good or better than control-probes-based algorithms. For the Affymetrix GeneChip arrays, it is further shown that the algorithm outperforms the robust multiarray (RMA) expression measure in estimating genomewide expression levels.     

Using expression arrays for copy number detection: an example from E. coli

Background  

The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable.     

An introduction to DNA chips: principles, technology, applications and analysis

This review describes the recently developed GeneChip technology that provides efficient access to genetic information using miniaturised, high-density arrays of DNA or oligonucleotide probes. Such microarrays are powerful tools to study the molecular basis of interactions on a scale that would be impossible using conventional analysis. The recent development of the microarray technology has greatly accelerated the investigation of gene regulation. Arrays are mostly used to identify which genes are turned on or off in a cell or tissue, and also to evaluate the extent of a gene's expression under various conditions. Indeed, this technology has been successfully applied to investigate simultaneous expression of many thousands of genes and to the detection of mutations or polymorphisms, as well as for their mapping and sequencing.     

Affymetrix GeneChip microarray preprocessing for multivariate analyses

Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.     

Gene expression analysis by DNA microarray in papillary thyroid cancer

In our study we present chosen elements of microarray analysis of gene expression profile in papillary thyroid cancer. The study group included 16 papillary thyroid cancer tissues and 16 corresponding normal tissues. Samples were analyzed on high density oligonucleotide microarrays (GeneChip HG-U133A) which contain 22.000 genes. 110 genes, which had significant changed expression, were selected by MAS 5.0 program. 3 genes were chosen to the deeper analysis: dipeptidylpeptidase 4 (DPP4), fibronectin 1 (FN1), tissue inhibitor of metalloproteinase 1 (TIMP1). DPP4-RNA were absent in normal tissue while in cancer tissue it was detected in large amount. FN1 and TIMP1 expression were detected in normal tissue but markedly increased in papillary thyroid cancer. Among these 3 genes DPP4 seems to be the best molecular marker for papillary thyroid cancer.     

Development and application of a 6.5 million feature affymetrix genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.)

ABSTRACT: BACKGROUND: High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection of nucleotide polymorphisms, which limits utility in species with low rates of polymorphism such as lettuce (Lactuca sativa). RESULTS: We developed a 6.5 million feature Affymetrix GeneChip? for efficient polymorphism discovery and genotyping, as well as for analysis of gene expression in lettuce. Probes on the microarray were designed from 26,809 unigenes from cultivated lettuce and an additional 8,819 unigenes from four related species (L. serriola, L. saligna, L. virosa and L. perennis). Where possible, probes were tiled with a 2 bp stagger, alternating on each DNA strand; providing an average of 187 probes covering approximately 600 bp for each of over 35,000 unigenes; resulting in up to 13 fold redundancy in coverage per nucleotide. We developed protocols for hybridization of genomic DNA to the GeneChip? and refined custom algorithms that utilized coverage from multiple, high quality probes to detect single position polymorphisms in 2 bp sliding windows across each unigene. This allowed us to detect greater than 18,000 polymorphisms between the parental lines of our core mapping population, as well as numerous polymorphisms between cultivated lettuce and wild species in the lettuce genepool. Using marker data from our diversity panel comprised of 52 accessions from the five species listed above, we were able to separate accessions by species using both phylogenetic and principal component analyses. Additionally, we estimated the diversity between different types of cultivated lettuce and distinguished morphological types. CONCLUSION: By hybridizing genomic DNA to a custom oligonucleotide array designed for maximum gene coverage, we were able to identify polymorphisms using two approaches for pair-wise.     

Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays

Background

Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor (SF), for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values.

Results

Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.

Conclusions

Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.

     

A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms.

We have conducted a study to compare the variability in measured gene expression levels associated with three types of microarray platforms. Total RNA samples were obtained from liver tissue of four male mice, two each from inbred strains A/J and C57BL/6J. The same four samples were assayed on Affymetrix Mouse Genome Expression Set 430 GeneChips (MOE430A and MOE430B), spotted cDNA microarrays, and spotted oligonucleotide microarrays using eight arrays of each type. Variances associated with measurement error were observed to be comparable across all microarray platforms. The MOE430A GeneChips and cDNA arrays had higher precision across technical replicates than the MOE430B GeneChips and oligonucleotide arrays. The Affymetrix platform showed the greatest range in the magnitude of expression levels followed by the oligonucleotide arrays. We observed good concordance in both estimated expression level and statistical significance of common genes between the Affymetrix MOE430A GeneChip and the oligonucleotide arrays. Despite their apparently high precision, cDNA arrays showed poor concordance with other platforms.     

Libaffy: software for processing Affymetrix GeneChip data

SUMMARY: Affymetrix GeneChip microarrays are increasingly used in gene expression studies and in greater number. A software library was developed that supports Affymetrix file formats and implements two popular summary algorithms (MAS5.0 and RMA). The library is modular in design for integration into larger systems and processing pipelines. Additionally, a graphical interface (GENE) was developed to allow end-user access to the functionality within the library. AVAILABILITY: libaffy is free to use under the GNU GPL license. The source code and Windows binaries can be freely accessed from the website http://src.moffitt.usf.edu/libaffy. Additional API documentation and user manual are available.