Bioinformatics Advance Access originally published online on August 9, 2006
Bioinformatics 2006 22(20):2554-2555; doi:10.1093/bioinformatics/btl434
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© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
MASQOT-GUI: spot quality assessment for the two-channel microarray platform
1 Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden
2 Umeå Plant Science Centre, Department of Plant Physiology, Umeå University SE-901 87 Umeå, Sweden
3 Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences SE-901 83 Umeå, Sweden
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 PROGRAM FEATURE SUMMARY3 SUMMARYREFERENCES |
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Summary: MASQOT-GUI provides an open-source, platform-independent software pipeline for two-channel microarray spot quality control. This includes gridding, segmentation, quantification, quality assessment and data visualization. It hosts a set of independent applications, with interactions between the tools as well as import and export support for external software. The implementation of automated multivariate quality control assessment, which is a unique feature of MASQOT-GUI, is based on the previously documented and evaluated MASQOT methodology. Further abilities of the application are outlined and illustrated.
Availability: MASQOT-GUI is Java-based and licensed under the GNU LGPL. Source code and installation files are available for download at http://masqot-gui.sourceforge.net/
Contact: max.bylesjo{at}chem.umu.se
Supplementary information: Supplementary data are available at Bioinformatics online
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 PROGRAM FEATURE SUMMARY3 SUMMARYREFERENCES |
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The microarray technology allow for parallel measurements of expression levels of numerous genes. The technology has emerged as a standard device in many genomics laboratories, but frequently suffers from contaminations during data generation. This might alter the signal quantification process and hence interpretation of the results. In a recent study, Bylesjö et al. outlined and evaluated the MASQOT (Bylesjö et al., 2005) methodology for microarray spot quality control to identify artifacts and unreliable data points. The technique assigns continuous quality values based on the physical properties of each spot, which can be utilized as weights in subsequent analysis procedures or hypothesis tests as well as to discard spots of undesired quality.
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2 PROGRAM FEATURE SUMMARY |
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TOPABSTRACT1 INTRODUCTION2 PROGRAM FEATURE SUMMARY3 SUMMARYREFERENCES |
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MASQOT-GUI hosts a set of independent applications for gridding, segmentation, information extraction, quality control and visualization for the two-channel microarray platform. It consists of three tailor-made Java applications, which are described in more detail in the following sections. A graphical representation of the tools contained in MASQOT-GUI is available in Figure 1.
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2.1 Step 1: gridding
Prior to microarray quantification (estimation of signal intensities), the actual position of each spot must be located. This process is typically referred to as gridding or addressing. Many applications have been developed for this purpose. Commercial examples include, for instance, GenePix Pro (Molecular Devices, CA, USA), Spot (http://spot.cmis.csiro.au/spot/doc/Spot.pdf) and ImaGene (BioDiscovery Inc., CA, USA) whereas open-source alternatives include TIGR Spotfinder (Saeed et al., 2003) and (free for academic use) UCSF Spot (Jain et al., 2002).
MASQOTGrid is a simple graphical application for gridding of two-channel microarray data. It handles arbitrary-sized grids with basic interactive alterations such as stretching, rotating and automated local adjustment of the grid. This helps to ensure that spot coordinates are correctly identified before segmentation. To set up the grid, MASQOTGrid handles import of GenePix Array Layout (GAL) or GenePix Result (GPR) files. The main rationale behind MASQOTGrid, despite the availability of more comprehensive software solutions for this purpose, is to provide a first (yet voluntary) step in a complete pipeline for spot quality control without mandatory involvement of external softwares.
2.2 Step 2: segmentation and quality control
Segmentation is the process of separating foreground intensities (signal) from background intensities (noise) based on the actual spot coordinates. Following segmentation, spot quality assessment based on physical spot properties (such as foreground area) is frequently utilized to identify potential artifacts that have arisen during data generation. All of the previously mentioned applications feature various implementations of univariate quality control, on the general form ‘flag the spot as bad if any of the spot properties i > xi’, where xi is an arbitrary threshold value. Only a minority of the univariate quality control values of the previously stated applications have been evaluated in published studies (Sauer et al., 2005; Tran et al., 2002).
MASQOTSeg is a tool for segmentation and quality control of two-channel microarray data. All of the steps in MASQOTSeg are designed to be performed in a batch-wise fashion and require a minimal amount of user interaction. It supports segmentation based on spot coordinates from MASQOTGrid or GPR files.
MASQOTSeg implements the MASQOT methodology for multivariate spot quality control and support data export in native (tab-delimited text) and GPR file formats. By utilizing the MASQOT quality control score, all physical spot properties will jointly contribute to the final quality assessment based on the weighted sum 
. X contains a set of physical characteristics for each spot, for instance spot area, roundness and various foreground variability measures that are calculated subsequent to segmentation. B contains a set of coefficients (weights) for each spot property that were derived and evaluated in the original MASQOT study. 
denotes the continuous quality control scores for each spot, which are typically between zero (low quality) and one (high quality).
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2.3 Step 3: visualization
Independent of how spot quality is assessed, it is typically of interest to the user to visually inspect spots that are automatically flagged as ‘bad’ (implying low quality) by a software. MASQOTView is a tool for visualization and manual re-evaluation of such spots. The main rationale behind MASQOTView is user control: to be able to manually override the classification decision for spots of borderline quality. By basing the visualization results on the MASQOT quality control score, only a subset of 
5–7% (Bylesjö et al., 2005) of the original spots may be of interest for visual inspection; thus greatly reducing manual efforts.
In MASQOTView, spots are filtered based on the MASQOT quality control value, cropped from the original raw images, scaled to a customizable size and finally displayed on a grid in an interactive window. These spots can now easily be visually re-evaluated in order to remove spot flags that, according to personal opinion, are of acceptable quality and should be retained. The manual flag assignments can later be incorporated into files of external formats, such as GPR files, for usage in subsequent data analysis steps.
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3 SUMMARY |
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TOPABSTRACT1 INTRODUCTION2 PROGRAM FEATURE SUMMARY3 SUMMARYREFERENCES |
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A public resource for automated assessment of two-channel microarray spot quality is outlined. The MASQOT-GUI software is Java-based and thus platform independent. All required steps, from raw data to final quantification and quality assessment, can be performed within MASQOT-GUI. Optionally, the user can utilize external software for gridding and subsequently import spot coordinates into MASQOT-GUI prior to segmentation.
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Acknowledgments |
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The authors are indebted to Oskar Skogström at the UPSC, Umeå, Sweden, for useful discussions. This work was supported by grants from the Swedish SSF, the Wallenberg Foundation, the European Commission, the Swedish VR, EU-strategic funding and the Functional Genomics Initiative at SLU. Funding to pay the Open Access publication charges for this article was provided by the Swedish SSF.
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Joaquin Dopazo
Received on May 29, 2006; revised on July 7, 2006; accepted on August 4, 2006
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REFERENCES |
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TOPABSTRACT1 INTRODUCTION2 PROGRAM FEATURE SUMMARY3 SUMMARYREFERENCES |
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Bylesjö, M., et al. (2005) MASQOT: a method for cDNA microarray spot quality control. BMC Bioinformatics, 6, 250[CrossRef][Medline].
Jain, A.N., et al. (2002) Fully automatic quantification of microarray image data. Genome Res, . 12, 325–332
Saeed, A.I., et al. (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques, 34, 374–378[Web of Science][Medline].
Sauer, U., et al. (2005) Quick and simple: quality control of microarray data. Bioinformatics, 21, 1572–1578
Tran, P.H., et al. (2002) Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals. Nucleic Acids Res, . 30, e54
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