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Functional genomics is a field of molecular biology that is dealing with dynamic aspects such as gene transcription and translation as opposed to the static aspects of the genomic information included in the nucleotide sequence. It makes use of the vast wealth of data produced by genome sequencing projects to describe genome function.
Fields of Application[edit | edit source]
Functional genomics includes function-related aspects of the genome itself such as mutation and SNP analysis as well as all aspects of gene expression. The latter comprise a number of other "omics" such as transcriptomics, proteomics, or metabolomics. Together these fields of application describe the function and interactions of genes.
Frequently Used Techniqes[edit | edit source]
Functional genomics uses mostly high-throughput techniques to characterize the abundance gene products such as mRNA and proteins. Typical technology platforms are DNA microarrays and SAGE for mRNA or two-dimensional gel electrophoresis and mass spectrometryfor protein analysis. Because of the large quantity of data produced by these techniques and the desire to find biologically meaningful patterns, bioinformatics is crucial to this type of analysis. Examples of techniques in bioinformatics are data clustering or principal component analysis for unsupervised machine learning (class detection) as well as artificial neural networks or support vector machines for supervised machine learning (class prediction, classification).
See also[edit | edit source]
|Genome project | Glycomics | Human Genome Project | Proteomics|
|Chemogenomics | Structural genomics | Pharmacogenetics | Pharmacogenomics | Toxicogenomics|
|Bioinformatics | Cheminformatics | Systems biology|
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