Quantification of global transcription patterns in prokaryotes using spotted microarrays

Ben Sidders¹ , Mike Withers¹ , Sharon L Kendall¹ , Joanna Bacon³ , Simon J Waddell⁴ , Jason Hinds⁴ , Paul Golby⁵ , Farahnaz Movahedzadeh¹^,6 , Robert A Cox⁷ , Rosangela Frita¹ , Annemieke MC ten Bokum⁸ , Lorenz Wernisch² and Neil G Stoker¹

¹Department of Pathology and Infectious Diseases, Royal Veterinary College, Royal College Street, London, NW1 0TU, UK

²School of Crystallography, Birkbeck College, London, WC1E 7HX, UK

³TB Research, CEPR, Health Protection Agency, Porton Down, Salisbury, SP4 0JG, UK

⁴Medical Microbiology, Division of Cellular and Molecular Medicine, St George's University of London, Cranmer Terrace, Tooting, London, SW17 0RE, UK

⁵Veterinary Laboratories Agency, Woodham Lane, New Haw, Addlestone, Surrey, KT15 3NB, UK

⁶Institute for Tuberculosis Research College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, 60612-7231, USA

⁷Division of Mycobacterial Research, National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK

⁸Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK

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Genome Biology 2007, 8:R265doi:10.1186/gb-2007-8-12-r265


Published:	13 December 2007

Subject areas: Bioinformatics, Genome studies, Microbiology and parasitology

Abstract

We describe an analysis, applicable to any spotted microarray dataset produced using genomic DNA as a reference, that quantifies prokaryotic levels of mRNA on a genome-wide scale. Applying this to Mycobacterium tuberculosis, we validate the technique, show a correlation between level of expression and biological importance, define the complement of invariant genes and analyze absolute levels of expression by functional class to develop ways of understanding an organism's biology without comparison to another growth condition.