Genomic microarrays in the spotlight

Microarray-based comparative genomic hybridization (array-CGH) has emerged as a revolutionary platform, enabling the high-resolution detection of DNA copy number aberrations. In this article we outline the use and limitations of genomic clones, cDNA clones and PCR products as targets for genomic microarray construction. Furthermore, the applications and future aspects of these arrays for DNA copy number analysis in research and diagnostics, epigenetic profiling and gene annotation are discussed. These recent developments of genomic microarrays mark only the beginning of a new generation of high-resolution and high-throughput tools for genetic analysis.     

ArrayCyGHt: a web application for analysis and visualization of array-CGH data

ArrayCyGHt is a web-based application tool for analysis and visualization of microarray-comparative genomic hybridization (array-CGH) data. Full process of array-CGH data analysis, from normalization of raw data to the final visualization of copy number gain or loss, can be straightforwardly achieved on this arrayCyGHt system without the use of any further software. ArrayCyGHt, therefore, provides an easy and fast tool for the analysis of copy number aberrations in any kinds of data format. AVAILABILITY: ArrayCyGHt can be accessed at http://genomics.catholic.ac.kr/arrayCGH/     

Optimized amplification and fluorescent labeling of small cell samples for genomic array-CGH.

BACKGROUND: Whole genome amplification (WGA) is usually needed in the genetic analysis of samples containing a low number of cells. In genome-wide analysis of DNA copy numbers by array comparative genomic hybridization (array-CGH) it is very important that the genome is evenly represented throughout the amplified product. All currently available WGA techniques are generating some degree of bias. METHODS: A way to compensate for this is using a reference sample which is similarly amplified, as the introduced amplification bias will be leveled out. Additionally, direct labeling of the amplified DNA is performed to bypass the currently widely applied random primed labeling, which involves an additional amplification of the product and is introducing extra bias. RESULTS: In this article it is shown that equal processing of the test and reference sample is indeed crucial to generate an optimal array-CGH profile of amplified DNA samples. Also presented here is that the labeling method may significantly effect the array-CGH result, it is shown that with direct chemical labeling using platinum derivates (ULS labeling) optimal array-CGH results are obtained. CONCLUSIONS: We show that an optimized WGA strategy for both test and reference sample in combination with direct chemical labeling results in a reliable array-CGH profile of samples as low as a 30 cell equivalent.     

Comparative analysis of comparative genomic hybridization microarray technologies: report of a workshop sponsored by the Wellcome Trust

BACKGROUND: Array-comparative genomic hybridization (CGH), although providing much higher resolution compared with conventional CGH, has not yet become a widely applied method for the analysis of genomic gains and losses. METHODS: In January 2002, the Wellcome Trust sponsored a workshop where many of the laboratories developing this technology met to compare different methodologies for array-CGH. Fourteen groups participated, comprising 11 from Europe and 3 from the United States. To facilitate objective analysis, each laboratory constructed arrays using the same anonymous clones and performed a series of test hybridizations using identical genomic DNAs. RESULTS: A figure of merit (FM) was developed to summarize entire collections of data from each laboratory in a single measurement. The FMs consistently showed that a few groups produced quantitative array hybridization data of high quality, whereas a majority achieved a lower standard. CONCLUSIONS: The conclusions of the workshop were that polymerase chain reaction-based methods for the amplification of large insert clones for arraying were effective for array-CGH. It was also concluded that hybridizations performed under coverslips or in automated hybridization apparatus were less effective than hybridizations performed in simple wells with gentle rocking. A common experience by the participants was the batch-to-batch variability of commercial Cot1 preparations in their ability to suppress hybridization to repeat sequences. (Supplementary material for this article can be found in the online issue, which is available at http://www.interscience.wiley.com/jpages/0196-4763/suppmat/49_2/v49.43.html or at http://www.sanger.ac.uk/HGP/Cytogenetics/Publications/Cytometry Sept 2002/Supplemental.pdf.)     

Differential gene alteration among hepatoma cell lines demonstrated by cDNA microarray-based comparative genomic hybridization

We assayed chromosomal abnormalities in hepatoma cell lines using the microarray-based comparative genomic hybridization (array-CGH) method and investigated the relationship between genomic copy number alterations and expression profiles in these hepatoma cell lines. We modified a cDNA array-CGH assay to compare genomic DNAs from seven hepatoma cell lines, as well as DNA from two non-hepatoma cell lines and from normal cells. The mRNA expression of each sample was assayed in parallel by cDNA microarray. We identified small amplified or deleted chromosomal regions, as well as alterations in DNA copy number not previously described. We predominantly found alterations of apoptosis-related genes in Hep3B and HepG2, cell adhesion and receptor molecules in HLE, and cytokine-related genes in PLC/PRF/5. About 40% of the genes showing amplification or loss showed altered levels of mRNA (p < 0.05). Hierarchical clustering analysis showed that the expression of these genes allows differentiation between alpha-fetoprotein (AFP)-producing and AFP-negative cell lines. cDNA array-CGH is a sensitive method that can be used to detect alterations in genomic copy number in tumor cells. Differences in DNA copy alterations between AFP-producing and AFP-negative cells may lead to differential gene expression and may be related to the phenotype of these cells.     

Quantile smoothing of array CGH data

MOTIVATION: Plots of array Comparative Genomic Hybridization (CGH) data often show special patterns: stretches of constant level (copy number) with sharp jumps between them. There can also be much noise. Classic smoothing algorithms do not work well, because they introduce too much rounding. To remedy this, we introduce a fast and effective smoothing algorithm based on penalized quantile regression. It can compute arbitrary quantile curves, but we concentrate on the median to show the trend and the lower and upper quartile curves showing the spread of the data. Two-fold cross-validation is used for optimizing the weight of the penalties. RESULTS: Simulated data and a published dataset are used to show the capabilities of the method to detect the segments of changed copy numbers in array CGH data.     

Computation of recurrent minimal genomic alterations from array-CGH data

MOTIVATION: The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH data has been studied extensively, but subsequent steps in the analysis require flexible, efficient algorithms, particularly if the number of available profiles exceeds a few tens or the number of array probes exceeds a few thousands. RESULTS: We propose two algorithms for computing minimal and minimal constrained regions of gain and loss from discretized CGH profiles. The second of these algorithms can handle additional constraints describing relevant regions of copy number change. We have validated these algorithms on two public array-CGH datasets. AVAILABILITY: From the authors, upon request. CONTACT: celine@lri.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Integrating copy number polymorphisms into array CGH analysis using a robust HMM

MOTIVATION: Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions. RESULTS: We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of "outliers", which are often due to copy number polymorphisms (CNPs). By "explaining away" these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers. AVAILABILITY: Source code written in Matlab is available from http://www.cs.ubc.ca/~sshah/acgh.     

Stability-Based Comparison of Class Discovery Methods for DNA Copy Number Profiles

Motivation

Array-CGH can be used to determine DNA copy number, imbalances in which are a fundamental factor in the genesis and progression of tumors. The discovery of classes with similar patterns of array-CGH profiles therefore adds to our understanding of cancer and the treatment of patients. Various input data representations for array-CGH, dissimilarity measures between tumor samples and clustering algorithms may be used for this purpose. The choice between procedures is often difficult. An evaluation procedure is therefore required to select the best class discovery method (combination of one input data representation, one dissimilarity measure and one clustering algorithm) for array-CGH. Robustness of the resulting classes is a common requirement, but no stability-based comparison of class discovery methods for array-CGH profiles has ever been reported.

Results

We applied several class discovery methods and evaluated the stability of their solutions, with a modified version of Bertoni''s -based test [1]. Our version relaxes the assumption of independency required by original Bertoni''s -based test. We conclude that Minimal Regions of alteration (a concept introduced by [2]) for input data representation, sim [3] or agree [4] for dissimilarity measure and the use of average group distance in the clustering algorithm produce the most robust classes of array-CGH profiles.

Availability

The software is available from http://bioinfo.curie.fr/projects/cgh-clustering. It has also been partly integrated into "Visualization and analysis of array-CGH"(VAMP)[5]. The data sets used are publicly available from ACTuDB [6].     

Spatial normalization of array-CGH data

Background  

Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.     

Identification of Differential Aberrations in Multiple‐Sample Array CGH Studies

Summary Most existing methods for identifying aberrant regions with array CGH data are confined to a single target sample. Focusing on the comparison of multiple samples from two different groups, we develop a new penalized regression approach with a fused adaptive lasso penalty to accommodate the spatial dependence of the clones. The nonrandom aberrant genomic segments are determined by assessing the significance of the differences between neighboring clones and neighboring segments. The algorithm proposed in this article is a first attempt to simultaneously detect the common aberrant regions within each group, and the regions where the two groups differ in copy number changes. The simulation study suggests that the proposed procedure outperforms the commonly used single‐sample aberration detection methods for segmentation in terms of both false positives and false negatives. To further assess the value of the proposed method, we analyze a data set from a study that identified the aberrant genomic regions associated with grade subgroups of breast cancer tumors.     

SimRAD: an R package for simulation‐based prediction of the number of loci expected in RADseq and similar genotyping by sequencing approaches

Application of high‐throughput sequencing platforms in the field of ecology and evolutionary biology is developing quickly with the introduction of efficient methods to reduce genome complexity. Numerous approaches for genome complexity reduction have been developed using different combinations of restriction enzymes, library construction strategies and fragment size selection. As a result, the choice of which techniques to use may become cumbersome, because it is difficult to anticipate the number of loci resulting from each method. We developed SimRAD, an R package that performs in silico restriction enzyme digests and fragment size selection as implemented in most restriction site associated DNA polymorphism and genotyping by sequencing methods. In silico digestion is performed on a reference genome or on a randomly generated DNA sequence when no reference genome sequence is available. SimRAD accurately predicts the number of loci under alternative protocols when a reference genome sequence is available for the targeted species (or a close relative) but may be unreliable when no reference genome is available. SimRAD is also useful for fine‐tuning a given protocol to adjust the number of targeted loci. Here, we outline the functionality of SimRAD and provide an illustrative example of the use of the package (available on the CRAN at http://cran.r-project.org/web/packages/SimRAD ).     

Denoising array-based comparative genomic hybridization data using wavelets

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.     

SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics

SUMMARY: SPREAD is a user-friendly, cross-platform application to analyze and visualize Bayesian phylogeographic reconstructions incorporating spatial-temporal diffusion. The software maps phylogenies annotated with both discrete and continuous spatial information and can export high-dimensional posterior summaries to keyhole markup language (KML) for animation of the spatial diffusion through time in virtual globe software. In addition, SPREAD implements Bayes factor calculation to evaluate the support for hypotheses of historical diffusion among pairs of discrete locations based on Bayesian stochastic search variable selection estimates. SPREAD takes advantage of multicore architectures to process large joint posterior distributions of phylogenies and their spatial diffusion and produces visualizations as compelling and interpretable statistical summaries for the different spatial projections. AVAILABILITY: SPREAD is licensed under the GNU Lesser GPL and its source code is freely available as a GitHub repository: https://github.com/phylogeography/SPREAD CONTACT: filip.bielejec@rega.kuleuven.be.     

Copy number variations of genes involved in stress responses reflect the redox state and DNA damage in brewing yeasts

The yeast strains of the Saccharomyces sensu stricto complex involved in beer production are a heterogeneous group whose genetic and genomic features are not adequately determined. Thus, the aim of the present study was to provide a genetic characterization of selected group of commercially available brewing yeasts both ale top-fermenting and lager bottom-fermenting strains. Molecular karyotyping revealed that the diversity of chromosome patterns and four strains with the most accented genetic variabilities were selected and subjected to genome-wide array-based comparative genomic hybridization (array-CGH) analysis. The differences in the gene copy number were found in five functional gene categories: (1) maltose metabolism and transport, (2) response to toxin, (3) siderophore transport, (4) cellular aldehyde metabolic process, and (5) L-iditol 2-dehydrogenase activity (p < 0.05). In the Saflager W-34/70 strain (Fermentis) with the most affected array-CGH profile, loss of aryl-alcohol dehydrogenase (AAD) gene dosage correlated with an imbalanced redox state, oxidative DNA damage and breaks, lower levels of nucleolar proteins Nop1 and Fob1, and diminished tolerance to fermentation-associated stress stimuli compared to other strains. We suggest that compromised stress response may not only promote oxidant-based changes in the nucleolus state that may affect fermentation performance but also provide novel directions for future strain improvement.     

A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data

In the analysis of data generated by change-point processes, one critical challenge is to determine the number of change-points. The classic Bayes information criterion (BIC) statistic does not work well here because of irregularities in the likelihood function. By asymptotic approximation of the Bayes factor, we derive a modified BIC for the model of Brownian motion with changing drift. The modified BIC is similar to the classic BIC in the sense that the first term consists of the log likelihood, but it differs in the terms that penalize for model dimension. As an example of application, this new statistic is used to analyze array-based comparative genomic hybridization (array-CGH) data. Array-CGH measures the number of chromosome copies at each genome location of a cell sample, and is useful for finding the regions of genome deletion and amplification in tumor cells. The modified BIC performs well compared to existing methods in accurately choosing the number of regions of changed copy number. Unlike existing methods, it does not rely on tuning parameters or intensive computing. Thus it is impartial and easier to understand and to use.     

Stochastic segmentation models for array-based comparative genomic hybridization data analysis

Array-based comparative genomic hybridization (array-CGH) is a high throughput, high resolution technique for studying the genetics of cancer. Analysis of array-CGH data typically involves estimation of the underlying chromosome copy numbers from the log fluorescence ratios and segmenting the chromosome into regions with the same copy number at each location. We propose for the analysis of array-CGH data, a new stochastic segmentation model and an associated estimation procedure that has attractive statistical and computational properties. An important benefit of this Bayesian segmentation model is that it yields explicit formulas for posterior means, which can be used to estimate the signal directly without performing segmentation. Other quantities relating to the posterior distribution that are useful for providing confidence assessments of any given segmentation can also be estimated by using our method. We propose an approximation method whose computation time is linear in sequence length which makes our method practically applicable to the new higher density arrays. Simulation studies and applications to real array-CGH data illustrate the advantages of the proposed approach.     

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

SUMMARY: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. AVAILABILITY: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.     

Randomly picked cosmid clones overlap the pyrB and oriC gap in the physical map of the E. coli chromosome.

Sets of overlapping cosmid clones generated by random sampling and fingerprinting methods complement data at pyrB (96.5') and oriC (84') in the published physical map of E. coli. A new cloning strategy using sheared DNA, and a low copy, inducible cosmid vector were used in order to reduce bias in libraries, in conjunction with micro-methods for preparing cosmid DNA from a large number of clones. Our results are relevant to the design of the best approach to the physical mapping of large genomes.     

Determination of Transgene Copy Number in Stably Transfected Mammalian Cells by PCR-Capillary Electrophoresis Assay

The quantitative determination of transgene copy number in stably transfected mammalian cells has been traditionally estimated by Southern blot analysis. Recently, other methods have become available for appraisal of gene copy number, such as real-time PCR. Herein we describe a new method based on a fluorescently labeled PCR, followed by capillary electrophoresis. We amplified our target gene (prothrombin) and the internal control originating from genomic DNA (18S rRNA) in the same PCR tube and calculated the mean peak height ratio of the target:control gene for every cell clone sample. With this approach we identified stably transfected cell clones bearing the same transgene copy number. The results of our assay were confirmed by real-time PCR. Our method proves to be fast, low-cost, and reproducible compared with traditionally used methods. This assay can be used as a rapid screening tool for the determination of gene copy number in gene expression experiments.