- Open Access
ArrayD: A general purpose software for Microarray design
© Sharma et al; licensee BioMed Central Ltd. 2004
- Received: 03 June 2004
- Accepted: 02 October 2004
- Published: 02 October 2004
Microarray is a high-throughput technology to study expression of thousands of genes in parallel. A critical aspect of microarray production is the design aimed at space optimization while maximizing the number of gene probes and their replicates to be spotted.
We have developed a software called 'ArrayD' that offers various alternative design solutions for an array given a set of user requirements. The user feeds the following inputs: type of source plates to be used, number of gene probes to be printed, number of replicates and number of pins to be used for printing. The solutions are stored in a text file. The choice of a design solution to be used will be governed by the spotting chemistry to be used and the accuracy of the robot.
ArrayD is a software for standard cartesian robots. The software aids users in preparing a judicious and elegant design. ArrayD is universally applicable and is available at http://www.igib.res.in/scientists/arrayd/arrayd.html.
- Gene Probe
- Design Solution
- Space Optimization
- Microarray Design
- Executable File
Microarray is a popularly used high-throughput technology to investigate gene expression of thousands of genes simultaneously at the level of mRNA. Ever since the development of this technology [1–3], transcriptional profiling at the genomic level has been employed to address numerous issues in biology and in medicine [4–8]. It is likely that microarrays will continue to be used to explore various biological phenomena. The basic underlying principle involves spotting DNA fragments either derived from polymerase chain reaction or preparation of plasmids or oligonucleotides at high density (~10,000–25,000 spots on a glass slide of 25 mm × 75 mm) representing the probes of the genes under study. The surface on which the DNA fragments or oligonucleotides are spotted is usually glass slides coated with poly-L-lysine or amino alkyl silane that serve to improve adherence of DNA to the surface. Uniform spotting at high density requires robotic operation and a variety of robots are now available for spotting . The robots employed for the preparation of microarrays are of the cartesian type with movement in x-y-z direction.
A critical aspect of microarray production is the design considering space optimization to produce high-density arrays for a given set of samples and replicates. The softwares generally supplied with robotic spotters translate user input parameters into a set of instructions in robot language for printing arrays. These softwares do not offer design capabilities in which spotting parameters and grid configurations can be chosen for a given set of samples and replicates. Presently various solutions have to be derived manually in most academic laboratories. We have developed a user-friendly software 'ArrayD' that can be used by experts and novice alike to fill this gap to simplify and aid in rapid design. ArrayD offers a variety of design solutions given a set of requirements: Number of gene probes, number of replicates, and the source plate (384 well or 96 well). Because the algorithm implemented in ArrayD is inherently simple and uses fundamental principles of robot operation, the design solutions offered by ArrayD are universally applicable to any system. The choice of a design solution would be governed by the spotting chemistry and the humidity used in addition to elegant appearance. The hallmark of ArrayD is its overall simplicity and the variety of alternative designs it offers for users to decide on choosing the appropriate spotting parameters. The multiple design solutions offered by ArrayD provides a wide range of arrays from compact to loosely spaced spots as well as convenient grid patterning, which can be user selected for printing.
ArrayD program is developed in C and can be compiled and operated on UNIX V5.1, IRIX 5.1 and Red Hat Linux 7.0 (or higher) operating systems. A companion computer program ArraySolution was developed in Perl (Practical Extraction Report Language) version 5.6.1 and can be implemented on any UNIX or Linux operating system.
Inputs to be defined for ArrayD
Type of source plate to be used
Number of gene probes to be printed
Number of replicates
Number of pins to be used for printing
This parameter relates to time taken for printing the slides and the number of spots arrayed per slide. The number of pins in X-axis and Y-axis need to be specified. The type of pins used is assumed to be stealth pins, which are widely used. It is not necessary to specify pin type for ArrayD. Instead, this aspect is considered in the printing software according to the pin type used for implementing a particular design.
The program first validates the input given by the user for appropriate number of pins in each direction and the plate type to be used. For a valid input, ArrayD calculates maximum possible number of super grids in X (or Y) direction based on the coverslip dimensions, pin number in X (or Y) direction and pin-to-pin distance (Figure 2). The coverslip dimensions have been set in the program as 50 mm × 22 mm for the longest size coverslip that can be effectively used during hybridization. The pin-to-pin distance is fixed at 4500 microns in the print head for 384 well plates and at 9000 microns for 96 well plates.
ArrayD uses a predefined inter-spot distance database. Design solutions of ArrayD encompass various inter-spot distances that would be compatible with different spotting chemistries and conditions of humidity. We have used inter-spot distances of 170 μm, 180 μm, 190 μm, 200 μm, 220 μm, 250 μm and 300 μm. This database can be expanded to incorporate even lower inter-spot distances for use with other spotting chemistries by simple modification. We chose 170 μm as least distance based on several trial experiments in our laboratory using 50% DMSO as spotting solution and SMP3 pin type. In our experience, a minimum inter-spot distance of 200 microns works best with 50% DMSO at 40% – 50% humidity at 25°C.
Stealth pins for implementing various design solutions offered by ArrayD
Stealth Pin Catalog Number a
Spot Diameter b (μm)
Minimum spot spacing c (μm)
Design solutions offered by the program
Alternative array designs for a given set of input parameters are ranked on the basis of 'Distance area ratio' that describes the area covered by the array for each design. The array design spanning least area is ranked highest. This strategy allows for applying the labeled target sparingly. Subsequently, an easy report in tabular form can be generated by feeding the output data file from ArrayD into the companion Perl program 'ArraySolution.pl', which classifies array solutions into 'Square', 'Rectangle (Horizontal bar)', or 90° rotated 'Rectangle (Vertical column)' based on the geometry of a given design solution. If the number of grids are equal in both the direction we have a 'Square' design. In all other cases we obtain a 'Rectangle' design, which can be either of two types: the long side of the array is parallel to the length (Horizontal) or the width (Vertical) of the slide. The output of ArraySolution is a tab-delimited text file called 'filename.solution' where filename corresponds to the input name of the file carrying design solutions. The tabular report consist of Number of super grids in X – direction, Number of super grids in Y – direction, Number of spots per grid in X – direction, Number of spots per grid in Y – direction, Distance between two spots (in microns), Distance Area ratio and geometry of design (Square or Rectangle). This can aid users to decide on a particular design solution based on space optimization and elegant appearance.
We have developed a simple and rapid software ArrayD that offers various design solutions of designing microarrays for a specific set of user-defined requirements.
The source code and the executable file for ArrayD and ArraySolution programs are freely available and can be downloaded from our website . The source code can be compiled and executed on Unix v 5.1, or IRIX v 5.1 or Red Hat Linux v 7.0 (or higher). The executable files can be downloaded for Windows platform (Windows 98/NT/XP/2000). Further information can be requested by sending e-mail to firstname.lastname@example.org or email@example.com.
AS, GPS and VKS thank Council of Scientific and Industrial Research (CSIR) for providing financial assistance. We thank Dipayan Dasgupta for his help in compilation of the software and Mamta Khandelwal for her help in uploading the software on the web site. We thank the assistance of Technosol.
- Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995, 270: 467–70.View ArticlePubMedGoogle Scholar
- Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW: Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc Natl Acad Sci U S A 1996, 93: 10614–9. 10.1073/pnas.93.20.10614PubMed CentralView ArticlePubMedGoogle Scholar
- Schena M: Genome analysis with gene expression microarrays. Bioessays 1996, 18: 427–31.View ArticlePubMedGoogle Scholar
- Gerhold DL, Jensen RV, Gullans SR: Better therapeutics through microarrays. Nature Genetics 2002, 32(suppl):547–552. 10.1038/ng1042View ArticlePubMedGoogle Scholar
- Petricoin III EF, Hackett JL, Lesko LJ, Puri RK, Gutman SI, Chumakov K, Woodcock J, Feigal DW, Zoon KC Jr, Sistare FD: Medical applications of microarray technologies: a regulatory science perspective. Nature Genetics 2002, 32(suppl):474–479. 10.1038/ng1029View ArticleGoogle Scholar
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531–7. 10.1126/science.286.5439.531View ArticlePubMedGoogle Scholar
- Cummings CA, Relman DA.: Using DNA microarrays to study host-microbe interactions. Emerg Infect Dis 2000, 6: 513–25.PubMed CentralView ArticlePubMedGoogle Scholar
- Quackenbush J: Computational analysis of microarray data. Nat Rev Genet 2001, 2: 418–27. 10.1038/35076576View ArticlePubMedGoogle Scholar
- Schena M: Microarray analysis. Hoboken, New Jersey: Wiley and Sons, Inc; 2002.Google Scholar
- Arrayit Web site[http://arrayit.com/Products/Printing/Stealth/stealth.html]
- ArrayD Web site[http://www.igib.res.in/scientists/arrayd/arrayd.html]
This article is published under license to BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.