Regression as a method to predict copy numbers in comparative genomic hybridization studies on bacteria

Comparative genomic hybridizations (CGH) using microarrays are performed with bacteria in order to determine the level of genomic similarity between various strains. The microarrays applied in CGH experiments are constructed on the basis of the genome sequence of one strain, which is used as a control, or reference, in each experiment. A strain being compared with the known strain is called the unknown strain. The ratios of fluorescent intensities obtained from the spots on the microarrays can be used to determine which genes are divergent in the unknown strain, as well as to predict the copy number of actual genes in the unknown strain. In this paper, we focus on the prediction of gene copy number based on data from CGH experiments. We assumed a linear connection between the log2 of the copy number and the observed log2-ratios, then predictors based on the factor analysis model and the linear random model were proposed in an attempt to identify the copy numbers. These predictors were compared to using the ratio of the intensities directly. Simulations indicated that the proposed predictors improved the prediction of the copy number in most situations. The predictors were applied on CGH data obtained from experiments with Enterococcus faecalis strains in order to determine copy number of relevant genes in five different strains.     

Development of a DNA-labeling system for array-based comparative genomic hybridization.

Chromosomal amplifications and deletions are critical components of tumorigenesis and DNA copy-number variations also correlate with changes in mRNA expression levels. Genome-wide microarray comparative genomic hybridization (CGH) has become an important method for detecting and mapping chromosomal changes in tumors. Thus, the ability to detect twofold differences in fluorescent intensity between samples on microarrays depends on the generation of high-quality labeled probes. To enhance array-based CGH analysis, a random prime genomic DNA labeling method optimized for improved sensitivity, signal-to-noise ratios, and reproducibility has been developed. The labeling system comprises formulated random primers, nucleotide mixtures, and notably a high concentration of the double mutant exo-large fragment of DNA polymerase I (exo-Klenow). Microarray analyses indicate that the genomic DNA-labeled templates yield hybridization signals with higher fluorescent intensities and greater signal-to-noise ratios and detect more positive features than the standard random prime and conventional nick translation methods. Also, templates generated by this system have detected twofold differences in gene copy number between male and female genomic DNA and identified amplification and deletions from the BT474 breast cancer cell line in microarray hybridizations. Moreover, alterations in gene copy number were routinely detected with 0.5 microg of genomic DNA starting sample. The method is flexible and performs efficiently with different fluorescently labeled nucleotides. Application of the optimized CGH labeling system may enhance the resolution and sensitivity of array-based CGH analysis in cancer and medical genetic studies.     

Reference-unbiased copy number variant analysis using CGH microarrays

Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.     

Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear. RESULTS: We compare 11 different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on diverse techniques such as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets and genetic algorithms. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real dataset obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.     

Data rotation improves genomotyping efficiency

Unsequenced bacterial strains can be characterized by comparing their genomic DNA to a sequenced reference genome of the same species. This comparative genomic approach, also called genomotyping, is leading to an increased understanding of bacterial evolution and pathogenesis. It is efficiently accomplished by comparative genomic hybridization on custom-designed cDNA microarrays. The microarray experiment results in fluorescence intensities for reference and sample genome for each gene. The log-ratio of these intensities is usually compared to a cut-off, classifying each gene of the sample genome as a candidate for an absent or present gene with respect to the reference genome. Reducing the usually high rate of false positives in the list of candidates for absent genes is decisive for both time and costs of the experiment. We propose a novel method to improve efficiency of genomotyping experiments in this sense, by rotating the normalized intensity data before setting up the list of candidate genes. We analyze simulated genomotyping data and also re-analyze an experimental data set for comparison and illustration. We approximately halve the proportion of false positives in the list of candidate absent genes for the example comparative genomic hybridization experiment as well as for the simulation experiments.     

Nonparametric testing for DNA copy number induced differential mRNA gene expression

Summary .  The central dogma of molecular biology relates DNA with mRNA. Array CGH measures DNA copy number and gene expression microarrays measure the amount of mRNA. Methods that integrate data from these two platforms may uncover meaningful biological relationships that further our understanding of cancer. We develop nonparametric tests for the detection of copy number induced differential gene expression. The tests incorporate the uncertainty of the calling of genomic aberrations. The test is preceded by a "tuning algorithm" that discards certain genes to improve the overall power of the false discovery rate selection procedure. Moreover, the test statistics are "shrunken" to borrow information across neighboring genes that share the same array CGH signature. For each gene we also estimate its effect, its amount of differential expression due to copy number changes, and calculate the coefficient of determination. The method is illustrated on breast cancer data, in which it confirms previously reported findings, now with a more profound statistical underpinning.     

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.     

Variation in gene content among geographically diverse Sulfolobus isolates

The ability of competitive (i.e., comparative) genomic hybridization (CGH) to assess similarity across entire microbial genomes suggests that it should reveal diversification within and between natural populations of free-living prokaryotes. We used CGH to measure relatedness of genomes drawn from Sulfolobus populations that had been shown in a previous study to be diversified along geographical lines. Eight isolates representing a wide range of spatial separation were compared with respect to gene-specific tags based on a closely related reference strain ( Sulfolobus solfataricus P2). For the purpose of assessing genetic divergence, 232 loci identified as polymorphic were assigned one of two alleles based on the corresponding fluorescence intensities from the arrays. Clustering of these binary genotypes was stable with respect to changes in the threshold and similarity criteria, and most of the groupings were consistent with an isolation-by-distance model of diversification. These results indicate that increasing spatial separation of geothermal sites correlates not only with minor sequence polymorphisms in conserved genes of Sulfolobus (demonstrated in the previous study), but also with the regions of difference (RDs) that occur between genomes of conspecifics. In view of the abundance of RDs in prokaryotic genomes and the relevance that some RDs may have for ecological adaptation, the results further suggest that CGH on microarrays may have advantages for investigating patterns of diversification in other free-living archaea and bacteria.     

A stepwise framework for the normalization of array CGH data

Background  

In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.     

Survey of genomic diversity among Enterococcus faecalis strains by microarray-based comparative genomic hybridization

We have compared nine Enterococcus faecalis strains with E. faecalis V583 by comparative genomic hybridization using microarrays (CGH). The strains used in this study (the "test" strains) originated from various environments. CGH is a powerful and promising tool for obtaining novel information on genome diversity in bacteria. By CGH, one obtains clues about which genes are present or divergent in the strains, compared to a reference strain (here, V583). The information obtained by CGH is important from both ecological and systematic points of view. CGH of E. faecalis showed considerable diversity in gene content: Compared to V583, the percentage of divergent genes in the test strains varied from 15% to 23%, and 154 genes were divergent in all strains. The main variation was found in regions corresponding to exogenously acquired or mobile DNA in V583. Antibiotic resistance genes, virulence factors, and integrated plasmid genes dominated among the divergent genes. The strains examined showed various contents of genes corresponding to the pTEF1, pTEF2, and pTEF3 genes in V583. The extensive transport and metabolic capabilities of V583 appeared similar in the test strains; CGH indicated that the ability to transport and metabolize various carbohydrates was similar in the test strains (verified by API 50 CH assays). The contents of genes related to stress tolerance appeared similar in V583 and the nine test strains, supporting the view of E. faecalis as an organism able to resist harsh conditions.     

Cytogenetic analysis from DNA by comparative genomic hybridization

Comparative genomic hybridization (CGH) is a modified in situ hybridization technique which allows detection and mapping of DNA sequence copy differences between two genomes in a single experiment. In CGH analysis, two differentially labelled genomic DNA (study and reference) are co-hybridized to normal metaphase spreads. Chromosomal locations of copy number changes in the DNA segments of the study genome are revealed by a variable fluorescence intensity ratio along each target chromosome. Since its development, CGH has been applied mostly as a research tool in the field of cancer cytogenetics to identify genetic changes in many previously unknown regions. CGH may also have a role in clinical cytogenetics for detection and identification of unbalanced chromosomal abnormalities.     

Comparative Genomic Hybridization (CGH) Reveals a Neo-X Chromosome and Biased Gene Movement in Stalk-Eyed Flies (Genus Teleopsis)

Chromosomal location has a significant effect on the evolutionary dynamics of genes involved in sexual dimorphism, impacting both the pattern of sex-specific gene expression and the rate of duplication and protein evolution for these genes. For nearly all non-model organisms, however, knowledge of chromosomal gene content is minimal and difficult to obtain on a genomic scale. In this study, we utilized Comparative Genomic Hybridization (CGH), using probes designed from EST sequence, to identify genes located on the X chromosome of four species in the stalk-eyed fly genus Teleopsis. Analysis of log₂ ratio values of female-to-male hybridization intensities from the CGH microarrays for over 3,400 genes reveals a strongly bimodal distribution that clearly differentiates autosomal from X-linked genes for all four species. Genotyping of 33 and linkage mapping of 28 of these genes in Teleopsis dalmanni indicate the CGH results correctly identified chromosomal location in all cases. Syntenic comparison with Drosophila indicates that 90% of the X-linked genes in Teleopsis are homologous to genes located on chromosome 2L in Drosophila melanogaster, suggesting the formation of a nearly complete neo-X chromosome from Muller element B in the dipteran lineage leading to Teleopsis. Analysis of gene movement both relative to Drosophila and within Teleopsis indicates that gene movement is significantly associated with 1) rates of protein evolution, 2) the pattern of gene duplication, and 3) the evolution of eyespan sexual dimorphism. Overall, this study reveals that diopsids are a critical group for understanding the evolution of sex chromosomes within Diptera. In addition, we demonstrate that CGH is a useful technique for identifying chromosomal sex-linkage and should be applicable to other organisms with EST or partial genomic information.     

Survey of Genomic Diversity among Enterococcus faecalis Strains by Microarray-Based Comparative Genomic Hybridization

We have compared nine Enterococcus faecalis strains with E. faecalis V583 by comparative genomic hybridization using microarrays (CGH). The strains used in this study (the “test” strains) originated from various environments. CGH is a powerful and promising tool for obtaining novel information on genome diversity in bacteria. By CGH, one obtains clues about which genes are present or divergent in the strains, compared to a reference strain (here, V583). The information obtained by CGH is important from both ecological and systematic points of view. CGH of E. faecalis showed considerable diversity in gene content: Compared to V583, the percentage of divergent genes in the test strains varied from 15% to 23%, and 154 genes were divergent in all strains. The main variation was found in regions corresponding to exogenously acquired or mobile DNA in V583. Antibiotic resistance genes, virulence factors, and integrated plasmid genes dominated among the divergent genes. The strains examined showed various contents of genes corresponding to the pTEF1, pTEF2, and pTEF3 genes in V583. The extensive transport and metabolic capabilities of V583 appeared similar in the test strains; CGH indicated that the ability to transport and metabolize various carbohydrates was similar in the test strains (verified by API 50 CH assays). The contents of genes related to stress tolerance appeared similar in V583 and the nine test strains, supporting the view of E. faecalis as an organism able to resist harsh conditions.     

Visualization-based discovery and analysis of genomic aberrations in microarray data

Background  

Chromosomal copy number changes (aneuploidies) play a key role in cancer progression and molecular evolution. These copy number changes can be studied using microarray-based comparative genomic hybridization (array CGH) or gene expression microarrays. However, accurate identification of amplified or deleted regions requires a combination of visual and computational analysis of these microarray data.     

A computational approach to measuring coherence of gene expression in pathways

This study uses a computational approach to analyze coherence of expression of genes in pathways. Microarray data were analyzed with respect to coherent gene expression in a group of genes defined as a pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Our hypothesis is that genes in the same pathway are more likely to be coordinately regulated than a randomly selected gene set. A correlation coefficient for each pair of genes in a pathway was estimated based on gene expression in normal or tumor samples, and statistically significant correlation coefficients were identified. The coherence indicator was defined as the ratio of the number of gene pairs in the pathway whose correlation coefficients are significant, divided by the total number of gene pairs in the pathway. We defined all genes that appeared in the KEGG pathways as a reference gene set. Our analysis indicated that the mean coherence indicator of pathways is significantly larger than the mean coherence indicator of random gene sets drawn from the reference gene set. Thus, the result supports our hypothesis. The significance of each individual pathway of n genes was evaluated by comparing its coherence indicator with coherence indicators of 1000 random permutation sets of n genes chosen from the reference gene set. We analyzed three data sets: two Affymetrix microarrays and one cDNA microarray. For each of the three data sets, statistically significant pathways were identified among all KEGG pathways. Seven of 96 pathways had a significant coherence indicator in normal tissue and 14 of 96 pathways had a significant coherence indicator in tumor tissue in all three data sets. The increase in the number of pathways with significant coherence indicators may reflect the fact that tumor cells have a higher rate of metabolism than normal cells. Five pathways involved in oxidative phosphorylation, ATP synthesis, protein synthesis, or RNA synthesis were coherent in both normal and tumor tissue, demonstrating that these are essential genes, a high level of expression of which is required regardless of cell type.     

Defining genomic islands and uropathogen-specific genes in uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) strains are responsible for the majority of uncomplicated urinary tract infections, which can present clinically as cystitis or pyelonephritis. UPEC strain CFT073, isolated from the blood of a patient with acute pyelonephritis, was most cytotoxic and most virulent in mice among our strain collection. Based on the genome sequence of CFT073, microarrays were utilized in comparative genomic hybridization (CGH) analysis of a panel of uropathogenic and fecal/commensal E. coli isolates. Genomic DNA from seven UPEC (three pyelonephritis and four cystitis) isolates and three fecal/commensal strains, including K-12 MG1655, was hybridized to the CFT073 microarray. The CFT073 genome contains 5,379 genes; CGH analysis revealed that 2,820 (52.4%) of these genes were common to all 11 E. coli strains, yet only 173 UPEC-specific genes were found by CGH to be present in all UPEC strains but in none of the fecal/commensal strains. When the sequences of three additional sequenced UPEC strains (UTI89, 536, and F11) and a commensal strain (HS) were added to the analysis, 131 genes present in all UPEC strains but in no fecal/commensal strains were identified. Seven previously unrecognized genomic islands (>30 kb) were delineated by CGH in addition to the three known pathogenicity islands. These genomic islands comprise 672 kb of the 5,231-kb (12.8%) genome, demonstrating the importance of horizontal transfer for UPEC and the mosaic structure of the genome. UPEC strains contain a greater number of iron acquisition systems than do fecal/commensal strains, which is reflective of the adaptation to the iron-limiting urinary tract environment. Each strain displayed distinct differences in the number and type of known virulence factors. The large number of hypothetical genes in the CFT073 genome, especially those shown to be UPEC specific, strongly suggests that many urovirulence factors remain uncharacterized.     

A segmentation/clustering model for the analysis of array CGH data

Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.     

An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH

Background  

Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the "normal" reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations.     

Comparative Genomic Hybridization As a New Method for Detection of Genomic Imbalance

Comparative Genomic Hybridization (CGH) is a molecular cytogenetic method for detecting chromosomal imbalances by comparing the copy number of DNA sequences in cells of tested tissue and the reference specimen. CGH is based on two-color fluorescence suppressive in situ hybridization of genomic test and reference DNAs, each labeled with a different fluorochrome, to metaphase chromosomes of a healthy individual. First described by Kallioniemi et al. in 1992, the CGH assay has been widely used for identification and characterization of both numerical and unbalanced structural chromosome abnormalities in cells of different tissues at various pathological conditions in humans, especially in tumor diseases. We discuss the specific features and quality control of comparative genomic hybridization, its advantages and limitations in detection of genomic imbalance and the prospects for development of this technology.