Array CGH Data Research Articles

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. ArrayCyGHt can be accessed at http://genomics.catholic.ac.kr/arrayCGH/

A 2.3 Mb duplication of chromosome 8q24.3 associated with severe mental retardation and epilepsy detected by standard karyotype

Chromosome duplications are found in about 2% of subjects with a typical chromosomal phenotype but their frequency is likely to be higher, as suggested by the first array-CGH data. According to the orientation of the duplicated segment, duplications may be in tandem or inverted. The latter are usually associated with a distal deletion. We studied a de novo 2.3 Mb inverted duplication of 8q24.3 without apparently associated deletion in a subject with profound psychomotor retardation, idiopathic epilepsy and growth delay. In spite of its small size, the presence of the rearrangement was suspected on standard karyotypes (approximately 400 bands) and later confirmed by Fluorescent in situ hybridization (FISH) analysis. We hypothesize that the GRINA gene, a glutamate binding subunit of NMDA receptor ion channel lying within the duplicated segment, may be responsible for the epilepsy. This paper confirms that small subtelomeric de novo duplications may be responsible for mental retardation, facial dysmorphisms and/or congenital malformations, although their presence may be overlooked by FISH analysis.

A statistical approach for array CGH data analysis

BackgroundMicroarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile.ResultsWe demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context.ConclusionsArray CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.

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

BackgroundChromosomal 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.ResultsWe have developed ChARMView, a visualization and analysis system for guided discovery of chromosomal abnormalities from microarray data. Our system facilitates manual or automated discovery of aneuploidies through dynamic visualization and integrated statistical analysis. ChARMView can be used with array CGH and gene expression microarray data, and multiple experiments can be viewed and analyzed simultaneously.ConclusionChARMView is an effective and accurate visualization and analysis system for recognizing even small aneuploidies or subtle expression biases, identifying recurring aberrations in sets of experiments, and pinpointing functionally relevant copy number changes. ChARMView is freely available under the GNU GPL at .

ArrayCGHbase: an analysis platform for comparative genomic hybridization microarrays

BackgroundThe availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment.ResultsWe have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser.ConclusionArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at .

CGHPRO – A comprehensive data analysis tool for array CGH

BackgroundArray CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for the genome wide detection of chromosomal imbalances. It is based on the co-hybridisation of differentially labelled test and reference DNA onto arrays of genomic BAC clones, cDNAs or oligonucleotides, and after correction for various intervening variables, loss or gain in the test DNA can be indicated from spots showing aberrant signal intensity ratios.Now that this technique is no longer confined to highly specialized laboratories and is entering the realm of clinical application, there is a need for a user-friendly software package that facilitates estimates of DNA dosage from raw signal intensities obtained by array CGH experiments, and which does not depend on a sophisticated computational environment.ResultsWe have developed a user-friendly and versatile tool for the normalization, visualization, breakpoint detection and comparative analysis of array-CGH data. CGHPRO is a stand-alone JAVA application that guides the user through the whole process of data analysis. The import option for image analysis data covers several data formats, but users can also customize their own data formats. Several graphical representation tools assist in the selection of the appropriate normalization method. Intensity ratios of each clone can be plotted in a size-dependent manner along the chromosome ideograms. The interactive graphical interface offers the chance to explore the characteristics of each clone, such as the involvement of the clones sequence in segmental duplications. Circular Binary Segmentation and unsupervised Hidden Markov Model algorithms facilitate objective detection of chromosomal breakpoints. The storage of all essential data in a back-end database allows the simultaneously comparative analysis of different cases. The various display options facilitate also the definition of shortest regions of overlap and simplify the identification of odd clones.ConclusionCGHPRO is a comprehensive and easy-to-use data analysis tool for array CGH. Since all of its features are available offline, CGHPRO may be especially suitable in situations where protection of sensitive patient data is an issue. It is distributed under GNU GPL licence and runs on Linux and Windows.

A method for calling gains and losses in array CGH data

Array CGH is a powerful technique for genomic studies of cancer. It enables one to carry out genome-wide screening for regions of genetic alterations, such as chromosome gains and losses, or localized amplifications and deletions. In this paper, we propose a new algorithm 'Cluster along chromosomes' (CLAC) for the analysis of array CGH data. CLAC builds hierarchical clustering-style trees along each chromosome arm (or chromosome), and then selects the 'interesting' clusters by controlling the False Discovery Rate (FDR) at a certain level. In addition, it provides a consensus summary across a set of arrays, as well as an estimate of the corresponding FDR. We illustrate the method using an application of CLAC on a lung cancer microarray CGH data set as well as a BAC array CGH data set of aneuploid cell strains.

CGH-Explorer: a program for analysis of array-CGH data

CGH-Explorer is a program for visualization and statistical analysis of microarray-based comparative genomic hybridization (array-CGH) data. The program has preprocessing facilities, tools for graphical exploration of individual arrays or groups of arrays, and tools for statistical identification of regions of amplification and deletion.

Hidden Markov models approach to the analysis of array CGH data

The development of solid tumors is associated with acquisition of complex genetic alterations, indicating that failures in the mechanisms that maintain the integrity of the genome contribute to tumor evolution. Thus, one expects that the particular types of genomic alterations seen in tumors reflect underlying failures in maintenance of genetic stability, as well as selection for changes that provide growth advantage. In order to investigate genomic alterations we are using microarray-based comparative genomic hybridization (array CGH). The computational task is to map and characterize the number and types of copy number alterations present in the tumors, and so define copy number phenotypes and associate them with known biological markers. To utilize the spatial coherence between nearby clones, we use an unsupervised hidden Markov models approach. The clones are partitioned into the states which represent the underlying copy number of the group of clones. The method is demonstrated on the two cell line datasets, one with known copy number alterations. The biological conclusions drawn from the analyses are discussed.

Hidden Markov models approach to the analysis of array CGH data

The development of solid tumors is associated with acquisition of complex genetic alterations, indicating that failures in the mechanisms that maintain the integrity of the genome contribute to tumor evolution. Thus, one expects that the particular types of genomic alterations seen in tumors reflect underlying failures in maintenance of genetic stability, as well as selection for changes that provide growth advantage. In order to investigate genomic alterations we are using microarray-based comparative genomic hybridization (array CGH). The computational task is to map and characterize the number and types of copy number alterations present in the tumors, and so define copy number phenotypes and associate them with known biological markers. To utilize the spatial coherence between nearby clones, we use an unsupervised hidden Markov models approach. The clones are partitioned into the states which represent the underlying copy number of the group of clones. The method is demonstrated on the two cell line datasets, one with known copy number alterations. The biological conclusions drawn from the analyses are discussed.

Comparative genomic hybridisation using a proximal 17p BAC/PAC array detects rearrangements responsible for four genomic disorders

Background: Proximal chromosome 17p is a region rich in low copy repeats (LCRs) and prone to chromosomal rearrangements. Four genomic disorders map within the interval 17p11–p12: Charcot–Marie–Tooth disease type 1A,...