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1.
BMC Bioinformatics ; 7: 102, 2006 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-16509966

RESUMO

BACKGROUND: Microarrays were first developed to assess gene expression but are now also used to map protein-binding sites and to assess allelic variation between individuals. Regardless of the intended application, efficient production and appropriate array design are key determinants of experimental success. Inefficient production can make larger-scale studies prohibitively expensive, whereas poor array design makes normalisation and data analysis problematic. RESULTS: We have developed a user-friendly tool, SimArray, which generates a randomised spot layout, computes a maximum meta-grid area, and estimates the print time, in response to user-specified design decisions. Selected parameters include: the number of probes to be printed; the microtitre plate format; the printing pin configuration, and the achievable spot density. SimArray is compatible with all current robotic spotters that employ 96-, 384- or 1536-well microtitre plates, and can be configured to reflect most production environments. Print time and maximum meta-grid area estimates facilitate evaluation of each array design for its suitability. Randomisation of the spot layout facilitates correction of systematic biases by normalisation. CONCLUSION: SimArray is intended to help both established researchers and those new to the microarray field to develop microarray designs with randomised spot layouts that are compatible with their specific production environment. SimArray is an open-source program and is available from http://www.flychip.org.uk/SimArray/.


Assuntos
Documentação/métodos , Hibridização in Situ Fluorescente/instrumentação , Análise de Sequência com Séries de Oligonucleotídeos/instrumentação , Robótica/instrumentação , Software , Interface Usuário-Computador , Desenho de Equipamento/métodos , Análise de Falha de Equipamento/métodos , Hibridização in Situ Fluorescente/métodos , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Robótica/métodos , Design de Software
2.
Methods Enzymol ; 410: 73-98, 2006.
Artigo em Inglês | MEDLINE | ID: mdl-16938547

RESUMO

Oligonucleotide probes are increasingly the method of choice for many modern DNA microarray applications. They provide higher target specificity, probe selection gives improved experimental control of hybridization properties, and targeting of specific gene subsequences allows better discrimination of highly similar targets such as splice variants or gene families. Only recently has there been substantial progress in dealing with the complexities of probe set design and probe-specific signal interpretation. After a discussion of advantages and disadvantages of oligonucleotide probes in comparison to amplicons, this chapter focuses on recent advances and remaining key challenges in probe design and computational data analysis for spotted and in situ-synthesized oligonucleotide microarray technologies. Both experimental questions and computational aspects are addressed. Experimental issues discussed include the choice of an optimal number of probes per target and probe lengths and their influence on bias and random measurement noise, effects of different probe or substrate modifications, and laboratory protocols on signal specificity and sensitivity. Computational topics include practical considerations and a case study in probe sequence design, the exploitation of probing multiple target regions, and the modeling of probe sequence-specific signals. The current state of the art of the field is examined, and principled thermodynamic probe design criteria are proposed that are based on the free energy of the probe-target complex at the hybridization temperature rather than its melting temperature. Finally, this chapter notes and discusses an emerging trend in recent computational work toward a focus on signal interpretation rather than probe sequence design.


Assuntos
Sondas Moleculares , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Oligonucleotídeos , Animais , Humanos
3.
Brief Bioinform ; 6(1): 86-97, 2005 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-15826359

RESUMO

To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.


Assuntos
Algoritmos , Perfilação da Expressão Gênica/métodos , Perfilação da Expressão Gênica/normas , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Análise de Sequência com Séries de Oligonucleotídeos/normas , Análise de Sequência de DNA/métodos , Análise de Sequência de DNA/normas , Artefatos , Calibragem/normas , Interpretação Estatística de Dados , Perfilação da Expressão Gênica/instrumentação , Variação Genética/genética , Modelos Genéticos , Modelos Estatísticos , Análise Numérica Assistida por Computador , Análise de Sequência com Séries de Oligonucleotídeos/instrumentação , Análise de Sequência de DNA/instrumentação
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