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Bioinformatics Advance Access originally published online on March 3, 2005
Bioinformatics 2005 21(10):2554-2555; doi:10.1093/bioinformatics/bti357
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

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

Su Young Kim 1,2, Suk Woo Nam 1,2, Sug Hyung Lee 1,2, Won Sang Park 1,2, Nam Jin Yoo 1,2, Jung Young Lee 1,2 and Yeun-Jun Chung 3,*

1Microdissection Genomics Research Center, The Catholic University of Korea Seoul, 137-701, Republic of Korea
2Department of Pathology, The Catholic University of Korea Seoul, 137-701, Republic of Korea
3Department of Microbiology, College of Medicine, The Catholic University of Korea Seoul, 137-701, Republic of Korea

*To whom correspondence should be addressed.


   Abstract
TOP
 Abstract
INTRODUCTION
 SERVER OVERVIEW
 STREAMLINED ANALYSIS STEPS
 OUTLOOK
 REFERENCES
 

Summary: 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/

Contact: yejun{at}catholic.ac.kr

Supplementary information: Technical documentation is available. See http://genomics.catholic.ac.kr/arrayCGH/


   INTRODUCTION
TOP
 Abstract
 INTRODUCTION
SERVER OVERVIEW
 STREAMLINED ANALYSIS STEPS
 OUTLOOK
 REFERENCES
 
The advantages of comparative genomic hybridization (CGH) have been accentuated by combining with microarray technology, array-CGH (Pinkel et al., 1998). It is now commonly used for investigating DNA copy number aberrations in various diseases.

Analysis of microarray data requires sophisticated processing steps to make reliable interpretation. Therefore it is crucial to streamline the processes and provide integrated environment for array-CGH data. Recently, several algorithms for array-CGH data analysis have been developed (Hupe et al., 2004; Jong et al., 2004; Lingjaerde et al., 2005). With these technical improvements, recognition of the copy number alteration breakpoints became more accurate and convincing. However, applications supporting raw array-CGH data processing are uncommon. In this study, we intended to develop more user-friendly and feasible web-based tool for array-CGH data analysis with any set of BAC clone chips and with any format of raw data file produced by GenePix (Axon Inc., CA), UCSC SPOT (Jain et al., 2002), Imagine (Biodiscovery Inc., CA) or QuantArray (Packard BioChip Technology).


   SERVER OVERVIEW
TOP
 Abstract
 INTRODUCTION
 SERVER OVERVIEW
STREAMLINED ANALYSIS STEPS
 OUTLOOK
 REFERENCES
 
ArrayCyGHt is available on the web. To develop arrayCyGHt we have used R (http://www.r-project.org), Apache (http:www.apache.org), PHP (http://www.php.net) and Perl (http://www.perl.org) which are well-known softwares available on the Internet.

This web application provides basic functions for filtering, normalizing, analyzing and plotting array-CGH data. Since arrayCyGHt is running on a server, a web browser is only a requirement to use the server.

ArrayCyGHt has the following features:

  • data import from tab-delimited text files,
  • data preprocessing (filtering, normalization, log transformation, realigning the raw data to linear mapping order),
  • graphical representation of copy number gain or loss under user guided threshold and
  • direct linking of the identified regions to public database.


   STREAMLINED ANALYSIS STEPS
TOP
 Abstract
 INTRODUCTION
 SERVER OVERVIEW
 STREAMLINED ANALYSIS STEPS
OUTLOOK
 REFERENCES
 
Loading data
Dataset to be analyzed should be given in tab-delimited text files. Chromosome mapping information and raw signal of every individual spot can be uploaded as two separate files or as a single file. Data files consist of a header row followed by rows of data.

Normalization
When users upload array layout, the server shows several data normalization options available to users. After normalization, the server illustrates reconstructed array data, MA plots and box plots representing distribution of both submitted and normalized data (Fig. 1a and b). These plots indicate the quality of submitted data and effects of normalization. For this process, limma (Smyth, 2004) and sma (Yang et al., 2002) packages of R are used.



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  		Fig. 1 Box plots of cy5–cy3 ratio distribution in individual blocks before (a) and after (b) normalization. (c) Genome-wide copy number profile of a primary colon cancer (DUC17) using whole-genome human array-CGH chip. Red and green dots indicate the clones above or below the threshold, ±0.2 in log2 ratio, respectively. (d) Table of outlier clone list. They are linked to Human Genome Browser Gateway.

 
Analysis
If spots are multiplicated on a chip, their signal intensities are averaged. The averaged data will be transformed to log2 scale and realigned according to the linear chromosomal mapping order. Then plots of whole genome are presented. Figure 1c illustrates the example of genome-wide array-CGH plot of primary colon cancer (unpublished data). All of the plot images can be downloaded. ArrayCyGHt defines the clones of copy number alterations by user guided threshold, and subsequently, table of outliers is presented (Fig. 1d). The outlier clones are directly linked to the Human Genome Browser Gateway (http://genome.ucsc.edu/cgi-bin/hgGateway). When we compared ArrayCyGHt with several other array-CGH analysis programs using published array-CGH data, the expected alterations were concordantly identified by ArrayCyGHt which means that the reliability and accuracy of this application are comparable with other applications. These comparative study results are available at ArrayCyGHt website.


   OUTLOOK
TOP
 Abstract
 INTRODUCTION
 SERVER OVERVIEW
 STREAMLINED ANALYSIS STEPS
 OUTLOOK
REFERENCES
 
Although arrayCyGHt is not an all-in-one tool for analysis of array-CGH data, this web-based tool can be a valuable resource for analyzing the constitutional chromosome aberrations with any set of BAC clone chip and for using data files from any kind of software. To our knowledge, this is the first web-based tool for array-CGH data analysis and visualization. ArrayCyGHt is the only application until now that supports the normalization and realigning of the signal intensity ratios in a user-friendly format. However, the comparison of multiple experiments and hence the determination of recurrent copy number alterations is not available in this application and will be implemented in the next version. This temporary shortcoming could be easily overcome by uploading the normalized data to othersoftware packages that focus mainly on smoothing, clustering and analysis of multiple array-CGH data.


   Acknowledgments
 
This work was supported by BumSuk Academic Research Fund of 2004.

Received on October 15, 2004; revised on February 7, 2005; accepted on February 24, 2005

   REFERENCES
TOP
 Abstract
 INTRODUCTION
 SERVER OVERVIEW
 STREAMLINED ANALYSIS STEPS
 OUTLOOK
 REFERENCES
 

    Hupe, P., et al. (2004) Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics, 20, 3413–3422[Abstract/Free Full Text].

    Jain, A.N., et al. (2002) Fully automatic quantification of microarray image data. Genome Res., 12, 325–332[Abstract/Free Full Text].

    Jong, K., et al. (2004) Breakpoint identification and smoothing of array comparative genomic hybridization data. Bioinformatics, 20, 3636–3637[Abstract/Free Full Text].

    Lingjaerde, O.C., et al. (2005) CGH-Explorer: a program for analysis of array-CGH data. Bioinformatics, 21, 821–822[Abstract/Free Full Text].

    Pinkel, D., et al. (1998) High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet., 20, 207–211[CrossRef][Web of Science][Medline].

    Smyth, G.K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, Article 1.

    Yang, Y.H., et al. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res., 30, e15[Abstract/Free Full Text].


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