A method for cross-species gene expression analysis with high-density oligonucleotide arrays

DNA microarrays have been widely used in gene expression analysis of biological processes. Due to a lack of sequence information, the applications have been largely restricted to humans and a few model organisms. Presented within this study are results of the cross-species hybridization with Affymetrix human high-density oligonucleotide arrays or GeneChip® using distantly related mammalian species; cattle, pig and dog. Based on the unique feature of the Affymetrix GeneChip® where every gene is represented by multiple probes, we hypothesized that sequence conservation within mammals is high enough to generate sufficient signals from some of the probes for expression analysis. We demonstrated that while overall hybridization signals are low for cross-species hybridization, a few probes of most genes still generated signals equivalent to the same-species hybridization. By masking the poorly hybridized probes electronically, the remaining probes provided reliable data for gene expression analysis. We developed an algorithm to select the reliable probes for analysis utilizing the match/mismatch feature of GeneChip®. When comparing gene expression between two tissues using the selected probes, we found a linear correlation between the cross-species and same-species hybridization. In addition, we validated cross-species hybridization results by quantitative PCR using randomly selected genes. The method shown herein could be applied to both plant and animal research.     

Photolithographic synthesis of high-density oligonucleotide probe arrays

High-density DNA probe arrays provide a massively parallel approach to nucleic acid sequence analysis that is transforming gene-based biomedical research and diagnostics. Light-directed combinatorial oligonucleotide synthesis has enabled the large-scale production of GeneChip probe arrays which contain several hundred of thousand oligonucleotide sequences on glass "chips" about one cm2 in size. Due to their very high information content, GeneChip probe arrays are finding widespread use in the hybridization-based detection and analysis of mutations and polymorphisms ("genotyping"), and in a wide range of gene expression studies. The manufacturing process integrates solid-phase photochemical oligonucleotide synthesis with lithographic techniques adapted from the microelectronics industry. The present-generation methodology employs MeNPOC photo-activatable nucleoside monomers with proximity photolithography, and is currently capable of printing individual 10 microns 2 probe features at a density of 10(6) probes/cm2.     

Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples.     

High-throughput polymorphism screening and genotyping with high-density oligonucleotide arrays

A highly reliable and efficient technology has been developed for high-throughput DNA polymorphism screening and large-scale genotyping. Photolithographic synthesis has been used to generate miniaturized, high-density oligonucleotide arrays. Dedicated instrumentation and software have been developed for array hybridization, fluorescent detection, and data acquisition and analysis. Specific oligonucleotide probe arrays have been designed to rapidly screen human STSs, known genes and full-length cDNAs. This has led to the identification of several thousand biallelic single-nucleotide polymorphisms (SNPs). Meanwhile, a rapid and robust method has been developed for genotyping these SNPs using oligonucleotide arrays. Each allele of an SNP marker is represented on the array by a set of perfect match and mismatch probes. Prototype genotyping chips have been produced to detect 400, 600 and 3000 of these SNPs. Based on the preliminary results, using oligonucleotide arrays to genotype several thousand polymorphic loci simultaneously appears feasible.     

Joint estimation of calibration and expression for high-density oligonucleotide arrays

MOTIVATION: The need for normalization in microarray experiments has been well documented in the literature. Currently, many analysis methods treat normalization and analysis as a series of steps, with summarized data carried forward to the next step. RESULTS: We present a unified algorithm which incorporates normalization and class comparison in one analysis using probe level perfect match and mismatch data. The algorithm is based on calibration models common to most biological assays, and the resulting chip-specific parameters have a natural interpretation. We show that the algorithm fits into the statistical generalized linear models framework, describe a practical fitting strategy and present results of the algorithm applied to an example dataset as well as based on metrics used in affycomp. The algorithm ranks amongst the top third of the affycomp competitors, performing best in measures of bias. AVAILABILITY: R functions are available on request from the authors.     

Characterization of the expression ratio noise structure in high-density oligonucleotide arrays

Background

High-density oligonucleotide microarrays provide a powerful tool for assessing differential mRNA expression levels. Characterizing the noise resulting from the enzymatic and hybridization steps, called type I noise, is essential for attributing significance measures to the differential expression scores. We introduce scoring functions for expression ratios, and associated quality measures. Both the PM (Perfect Match) probes and PM-MM differentials (MM is the single MisMatch) are considered as raw intensities. We then characterize the log-ratio noise structure using robust estimates of their intensity dependent variance.

Results

We show the relationships between the obtained ratios and their quality measures. The complementarity of PM and PM-MM methods is emphasized by the probe sets signal to noise measures. Using a large set of replicate experiments, we demonstrate that the noise structure in the log-ratios very closely follows a local log-normal distribution for both the PM and PM-MM cases. Therefore, significance relative to the type I noise can be quantified reliably using the local STD. We discuss the intensity dependence of the STD and show that ratio scores >1.25 are significant in the mid- to high-intensity range.

Conclusions

The ratio noise structure inherent to high-density oligonucleotide arrays can be well described in terms of local log-normal ratio distributions with characteristic intensity dependence. Therefore, robust estimates of the local STD of these distributions provide a simple and powerful way for assessing significance (relative to type I noise) in differential gene expression. This approach will be helpful for improving the reliability of predictions from hybridization experiments in general.     

Empirical characterization of the expression ratio noise structure in high-density oligonucleotide arrays

Background  

High-density oligonucleotide arrays (HDONAs) are a powerful tool for assessing differential mRNA expression levels. To establish the statistical significance of an observed change in expression, one must take into account the noise introduced by the enzymatic and hybridization steps, called type I noise. We undertake an empirical characterization of the experimental repeatability of results by carrying out statistical analysis of a large number of duplicate HDONA experiments.     

Retriever and CompareTable, two informatics tools for data analysis of high-density oligonucleotide arrays.

High-density oligonucleotide arrays are widely employed for detecting global changes in gene expression profiles of cells or tissues exposed to specific stimuli. Presented with large amounts of data, investigators can spend significant amounts of time analyzing and interpreting this array data. In our application of GeneChip arrays to analyze changes in gene expression in viral-infected epithelium, we have needed to develop additional computational tools that may be of utility to other investigators using this methodology. Here, I describe two executable programs to facilitate data extraction and multiple data point analysis. These programs run in a virtual DOS environment on Microsoft Windows 95/98/2K operating systems on a desktop PC. Both programs can be freely downloaded from the BioTechniques Software Library (www.BioTechniques.com). The first program, Retriever, extracts primary data from an array experiment contained in an Affymetrix textfile using user-inputted individual identification strings (e.g., the probe set identification numbers). With specific data retrieved for individual genes, hybridization profiles can be examined and data normalized. The second program, CompareTable, is used to facilitate comparison analysis of two experimental replicates. CompareTable compares two lists of genes, identifies common entries, extracts their data, and writes an output text file containing only those genes present in both of the experiments. The output files generated by these two programs can be opened and manipulated by any software application recognizing tab-delimited text files (e.g., Microsoft NotePad or Excel).     

Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

Background  

To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.     

Assessing DNA sequence variations in human ESTs in a phylogenetic context using high-density oligonucleotide arrays

We have analyzed human genomic diversity in 32 individuals representing four continental populations of Homo sapiens in the context of four ape species. We used DNA resequencing chips covering 898 expressed sequence tags (ESTs), corresponding to 109 kb of sequence. Based on the intra-species data, the neutral hypothesis could not be rejected. However, the mutation rate was two times lower than typically observed in functionally unconstrained genomic segments, suggesting a certain level of selection. The worldwide diversity (297 segregating sites and nucleotide diversity of 0.054%) was partitioned among continents, with the greatest amount of variation observed in the African sample. The long-term effective population size of the human population was estimated at 13,000; a similar figure was obtained for the African sample and a 20% lower estimate was obtained for the other continents. Africans also differed in having a higher number of continental-specific polymorphisms contributing to the higher average nucleotide diversity. These results are consistent with the existence of two distinct lineages of modern humans: amalgamation of these lineages in Africa led to the higher present-day diversity on that continent, whereas colonization of other continents by one of them gave the effect of a population bottleneck.     

Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology

In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis. Toward this goal, we propose a methodology for the analysis of high-density oligonucleotide arrays. The gene expression measures are modeled as censored data to account for the quantification limits of the technology, and two gene selection criteria based on contrasts from an analysis of covariance (ANCOVA) model are presented. The model is formulated in a hierarchical Bayesian framework, which in addition to making the fit of the model straightforward and computationally efficient, allows us to borrow strength across genes. The elicitation of hierarchical priors, as well as issues related to parameter identifiability and posterior propriety, are discussed in detail. We examine the performance of our proposed method on simulated data, then present a detailed case study of an endometrial cancer dataset.     

MARA: a novel approach for highly multiplexed locus-specific SNP genotyping using high-density DNA oligonucleotide arrays

We have developed a locus-specific DNA target preparation method for highly multiplexed single nucleotide polymorphism (SNP) genotyping called MARA (Multiplexed Anchored Runoff Amplification). The approach uses a single primer per SNP in conjunction with restriction enzyme digested, adapter-ligated human genomic DNA. Each primer is composed of common sequence at the 5′ end followed by locus-specific sequence at the 3′ end. Following a primary reaction in which locus-specific products are generated, a secondary universal amplification is carried out using a generic primer pair corresponding to the oligonucleotide and genomic DNA adapter sequences. Allele discrimination is achieved by hybridization to high-density DNA oligonucleotide arrays. Initial multiplex reactions containing either 250 primers or 750 primers across nine DNA samples demonstrated an average sample call rate of ~95% for 250- and 750-plex MARA. We have also evaluated >1000- and 4000-primer plex MARA to genotype SNPs from human chromosome 21. We have identified a subset of SNPs corresponding to a primer conversion rate of ~75%, which show an average call rate over 95% and concordance >99% across seven DNA samples. Thus, MARA may potentially improve the throughput of SNP genotyping when coupled with allele discrimination on high-density arrays by allowing levels of multiplexing during target generation that far exceed the capacity of traditional multiplex PCR.     

Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays

Oligonucleotide microarrays are based on the hybridization of labeled mRNA molecules to short length oligonucleotide probes on a glass surface. Two effects have been shown to affect the raw data: the sequence dependence of the probe hybridization properties and the chemical saturation resulting from surface adsorption processes. We address both issues simultaneously using a physically motivated hybridization model. Based on publicly available calibration data sets, we show that Langmuir adsorption accurately describes GeneChip hybridization, with model parameters that we predict from the sequence composition of the probes. Because these parameters have physical units, we are able to estimate absolute mRNA concentrations in picomolar. Additionally, by accounting for chemical saturation, we substantially reduce the compressive bias of differential expression estimates that normally occurs toward high concentrations.     

Combining gene expression data from different generations of oligonucleotide arrays

Background  

One of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public repositories. Through a comparative analysis on a variety of datasets, a more comprehensive view of the underlying mechanism or structure can be obtained. However, as we discover in this work, continual changes in genomic sequence annotations and probe design criteria make it difficult to compare gene expression data even from different generations of the same microarray platform.