Determining gene expression on a single pair of microarrays

Background  

In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments.     

Patchwork: allele-specific copy number analysis of whole-genome sequenced tumor tissue

Whole-genome sequencing of tumor tissue has the potential to provide comprehensive characterization of genomic alterations in tumor samples. We present Patchwork, a new bioinformatic tool for allele-specific copy number analysis using whole-genome sequencing data. Patchwork can be used to determine the copy number of homologous sequences throughout the genome, even in aneuploid samples with moderate sequence coverage and tumor cell content. No prior knowledge of average ploidy or tumor cell content is required. Patchwork is freely available as an R package, installable via R-Forge (http://patchwork.r-forge.r-project.org/).     

CNstream: A method for the identification and genotyping of copy number polymorphisms using Illumina microarrays

Background  

Understanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases.     

GeneCount: genome-wide calculation of absolute tumor DNA copy numbers from array comparative genomic hybridization data

Absolute tumor DNA copy numbers can currently be achieved only on a single gene basis by using fluorescence in situ hybridization (FISH). We present GeneCount, a method for genome-wide calculation of absolute copy numbers from clinical array comparative genomic hybridization data. The tumor cell fraction is reliably estimated in the model. Data consistent with FISH results are achieved. We demonstrate significant improvements over existing methods for exploring gene dosages and intratumor copy number heterogeneity in cancers.     

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

Background

The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.

Results

The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.

Conclusions

We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.     

Accuracy of genotyping for single nucleotide polymorphisms by a microarray-based single nucleotide polymorphism typing method involving hybridization of short allele-specific oligonucleotides.

Advances in technologies for identifying genetic polymorphisms rapidly and accurately will dramatically accelerate the discovery of disease-related genes. Among a variety of newly described methods for rapid typing of single-nucleotide polymorphisms (SNPs), gene detection using DNA microarrays is gradually achieving widespread use. This method involves the use of short (11- to 13-mer) allele-specific oligonucleotides. This method allows simultaneous analysis of many SNPs in DNAs from a large number of individuals, in a single experiment. In this work, we evaluated the accuracy of a new microarray-based short allele-specific oligonucleotide (ASO) hybridization method. There is a 96-well formatted array on a single plate, in which up to 256 spots are included in each well. Fluorescent probes for our experiments were produced by multiplex PCR amplification often target SNP-containing regions. We genotyped 192 individuals across a panel of ten single base variations, which included an insertion/deletion polymorphism. For comparison, we genotyped the same individuals for the same SNPs by the method of single-base extension with fluorescence detection. The typing accuracies of the microarray-based PCR-ASO and single-base extension methods were calculated as 99.9% and 99.1%, respectively, on the basis of genotyping results determined by direct sequencing. We conclude that the microarray-based hybridization method using short ASO probes represents a potential breakthrough technology for typing large numbers of SNPs rapidly and efficiently.     

Estimation and assessment of raw copy numbers at the single locus level

MOTIVATION: Although copy-number aberrations are known to contribute to the diversity of the human DNA and cause various diseases, many aberrations and their phenotypes are still to be explored. The recent development of single-nucleotide polymorphism (SNP) arrays provides researchers with tools for calling genotypes and identifying chromosomal aberrations at an order-of-magnitude greater resolution than possible a few years ago. The fundamental problem in array-based copy-number (CN) analysis is to obtain CN estimates at a single-locus resolution with high accuracy and precision such that downstream segmentation methods are more likely to succeed. RESULTS: We propose a preprocessing method for estimating raw CNs from Affymetrix SNP arrays. Its core utilizes a multichip probe-level model analogous to that for high-density oligonucleotide expression arrays. We extend this model by adding an adjustment for sequence-specific allelic imbalances such as cross-hybridization between allele A and allele B probes. We focus on total CN estimates, which allows us to further constrain the probe-level model to increase the signal-to-noise ratio of CN estimates. Further improvement is obtained by controlling for PCR effects. Each part of the model is fitted robustly. The performance is assessed by quantifying how well raw CNs alone differentiate between one and two copies on Chromosome X (ChrX) at a single-locus resolution (27kb) up to a 200kb resolution. The evaluation is done with publicly available HapMap data. AVAILABILITY: The proposed method is available as part of an open-source R package named aroma.affymetrix. Because it is a bounded-memory algorithm, any number of arrays can be analyzed.     

Development of a single tube 640-plex genotyping method for detection of nucleic acid variations on microarrays

Detection of DNA sequence variation is critical to biomedical applications, including disease genetic identification, diagnosis and treatment, drug discovery and forensic analysis. Here, we describe an arrayed primer extension-based genotyping method (APEX-2) that allows multiplex (640-plex) DNA amplification and detection of single nucleotide polymorphisms (SNPs) and mutations on microarrays via four-color single-base primer extension. The founding principle of APEX-2 multiplex PCR requires two oligonucleotides per SNP/mutation to generate amplicons containing the position of interest. The same oligonucleotides are then subsequently used as immobilized single-base extension primers on a microarray. The method described here is ideal for SNP or mutation detection analysis, molecular diagnostics and forensic analysis. This robust genetic test has minimal requirements: two primers, two spots on the microarray and a low cost four-color detection system for the targeted site; and provides an advantageous alternative to high-density platforms and low-density detection systems.     

Improvement of single nucleotide polymorphism genotyping by allele-specific PCR using primers modified with an ENA residue

When we placed an ENA residue into primers at the 3' end, or the n-1, n-2, or n-3 position, which included a single nucleotide polymorphism (SNP) site at the 3' end, only primers containing the ENA residue at the n-2 position were read by Taq DNA polymerase for amplification. The use of the ENA primers avoided the generation of undesired short products, which are thought to be derived from primer-dimers. A greater discrimination of the SNP site by these primers containing the ENA residue was observed compared with that of the corresponding unmodified DNA primers that are often used for allele-specific polymerase chain reaction (AS-PCR). This improvement is probably due to the difficulty of incorporating a nucleotide into the mismatched ENA primer by Taq DNA polymerase in the modified primer-template duplex. These results demonstrate that ENA primer-based AS-PCR would enable a rapid and reliable technique for SNP genotyping.     

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of approximately 160,000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300,000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from http://www2.umdnj.edu/lilabweb/publications/AccuTyping.html.     

Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Normalization is critical for removing systematic variation from microarray data. For two-color microarray platforms, intensity-dependent lowess normalization is commonly used to correct relative gene expression values for biases. Here we outline a normalization method for use when the assumptions of lowess normalization fail. Specifically, this can occur when specialized boutique arrays are constructed that contain a subset of genes selected to test particular biological functions.     

Reciprocal intrapool variation in plasmid copy numbers: a characteristic of segregational incompatibility

An experimental analysis of the concept that incompatible plasmids occupy a common intracellular pool from which copies are drawn at random for replication and assortment is presented. Intrapool variations in an incompatible heteroplasmid strain are inevitable and it is shown that these variations can be exploited by differential selection to amplify one plasmid at the expense of the other. Constant overall copy number is demonstrated for isogenic wild-type replicons and also for isogenic copy mutants whose copy numbers are so great that segregational incompatibility cannot be measured. In the test system used, that of the Staphylococcus aureus plasmid pT181, the rate of replication is probably determined by the availability of a trans-active initiator protein, RepC. In heteroplasmid strains containing wild-type and dominant copy mutant plasmids, although intrapool variation occurs, the total copy number is not constant but varies as a consequence of selection for or against the mutant plasmid. This is because all of the RepC is synthesized from the mutant plasmid (the wild-type is hyper-repressed) and therefore the selection affects the supply of RepC at the same time that it affects the copy number of the plasmid. None of these effects are seen with single plasmids or with compatible pairs.     

Trends in the quality of data from 5168 oligonucleotide microarrays from a single facility.

Improvements in the quality of gene expression data were investigated based on a database consisting of 5168 oligonucleotide microarrays collected over 3 years. The database includes diverse treatments of human and mouse samples collected from multiple laboratories. The array designs and algorithms used to capture the data have also changed over the 3 years of data collection. All hybridizations and labeling were conducted in the Hartwell Center for Bioinformatics and Biotechnology at St. Jude's Children's Research Hospital. Quality metrics for each human and mouse array were collected and analyzed. Statistical tests, such as ANOVA and linear regression, were applied to test for the effects of array design, algorithm, and time. The quality metrics tested were average background, actin 3'/5' ratio, Bio B signal, percent present, and scale factor. ANOVA results indicate that both recent algorithms and chip designs significantly correlate with improvements in Bio B, scale factor, percent present, and average background. Significant quality improvements correlated with new chip designs, algorithms, and their interaction. In addition, within one chip type analyzed by the same algorithm significant improving trends were still observed. Scale factor, percent present, and average background significantly improved over time for U133A arrays analyzed by the Affymetrix MicroArray Suite 5.0 algorithm according to linear regression. Proportionally fewer outlier arrays (those with less than 25% present calls) were seen over time. Also, high throughput periods did not increase the proportion of outliers, indicating that laboratory monitoring of quality is successfully preventing failures.     

A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.     

High-throughput genotyping system as a robust and useful tool in oncology: Experience from a single institution

Background and aimSingle nucleotide polymorphisms (SNPs) are substitutions of one base for another in the gene sequence and conforms the basis for pharmacogenetics and the development of personalized medicine. Many methods have been developed for SNP genotyping. The aim of the present study was to validate the use of a novel high-throughput genotyping system.MethodsFive SNPs (rs25487, rs25489, rs1799782, rs13181, and rs11615) were genotyped in 118 cancer patients using the classical method PCR restriction fragment length polymorphism (RFLP) and the high-throughput, automated assay Biotrove OpenArray^® NT Cycler, trying to explore the feasibility and reproducibility of the OpenArray system in the context of oncology.ResultsThe call rates obtained ranged from 95.7 to 100% for both techniques. The percentage of overlapping ranged from 96.2 to 100% among both assays, showing a high reproducibility between the techniques.ConclusionThese findings, together with the low-cost and the simple and fast work flow, suggest that the OpenArray system is a robust and easy methodology for genotyping in the field of oncology.