Abstract
Genomics is based on the ability to determine the transcriptome, proteome, and metabolome of a cell. These technologies only have added value when they are integrated and based on robust and reproducible workflows. This chapter describes the experimental design, sampling, sample pretreatment, data evaluation, integration, and interpretation. The actual generation of the data is not covered in this chapter since it is highly depended on available equipment and infrastructure.
The enormous amount of data generated by these technologies are integrated and interpreted inorder to generate leads for strain and process improvement. Biostatistics are becoming very important for the whole work flow therefore, some general recommendations how to set up experimental design and how to use biostatistics in enhancing the quality of the data and the selection of biological relevant leads for strain engineering and target identification are described.
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References
Jacobs DI, Olsthoorn MM, Maillet I, Akeroyd M, Breestraat S, Donkers S, van der Hoeven RA, van den Hondel CA, Kooistra R, Lapointe T, Menke H, Meulenberg R, Misset M, Müller WH, van Peij NN, Ram A, Rodriguez S, Roelofs MS, Roubos JA, van Tilborg MW, Verkleij AJ, Pel HJ, Stam H, Sagt CM (2009) Effective lead selection for improved protein production in Aspergillus niger based on integrated genomics. Fungal Genet Biol 46(Suppl 1):S141–S152
Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37(8):911–917
Gupta N, Bandeira N, Keich U, Pevzner PA (2011) Target-decoy approach and false discovery rate: when things may go wrong. J Am Soc Mass Spectrom 22(7):1111–1120
Lu P, Vogel C, Wang R, Yao X, Marcotte EM (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25(1):117–124
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nat Genet 29(4):365–371
Smilde AK, Van Der Werf MJ, Bijlsma S, Van Der Werff-Van Der Vat BJC, Jellema RH (2005) Fusion of mass spectrometry-based metabolomics data. Anal Chem 77(20):6729–6736
Doeswijk TG, Hageman JA, Westerhuis JA, Tikunov Y, Bovy A, van Eeuwijk FA (2011) Canonical correlation analysis of multiple sensory directed metabolomics data blocks reveals corresponding parts between data blocks. Chemometr Intell Lab 107(2):371–376
Munday DC, Surtees R, Emmott E, Dove BK, Digard P, Barr JN, Whitehouse A, Matthews D, Hiscox JAT (2012) Using SILAC and quantitative proteomics to investigate the interactions between viral and host proteomes. Proteomics 12(4–5):666–672
Kang S-U, Fuchs K, Sieghart W, Pollak A, Csaszar E, Lubec G (2009) Gel-based mass spectrometric analysis of a strongly hydrophobic GABAA-receptor subunit containing four transmembrane domains. Nat Protoc 4(7):1093–1102
Kettenbach AN, Rush J, Gerber SA (2011) Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides. Nat Protoc 6(2):175–186
Evans C, Noirel J, Ow SY, Salim M, Pereira-Medrano AG, Couto N, Pandhal J, Smith D, Pham TK, Karunakaran E, Zou X, Biggs CA, Wright PC (2012) An insight into iTRAQ: where do we stand now? Anal Bioanal Chem 2012(404):1011–1027
Acknowledgements
We sincerely thank Prof. Dr. Uwe Sauer and his group, especially Stefan Christen from ETH Zurich Institute of Molecular Systems Biology Zurich, Switzerland, for their time and help in starting the metabolomics work. Equally, we thank Prof. Dr. Joseph J. Heijnen and his group especially Dr. Lodewijk de Jonge from Department of Biotechnology, Faculty of Applied Sciences, Technical University of Delft, The Netherlands, for their contribution and collaboration on earlier works on metabolomics.
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Stam, H. et al. (2013). Sample Preparation and Biostatistics for Integrated Genomics Approaches. In: Alper, H. (eds) Systems Metabolic Engineering. Methods in Molecular Biology, vol 985. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-299-5_19
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DOI: https://doi.org/10.1007/978-1-62703-299-5_19
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