Functional genomics
From Wikipedia, the free encyclopedia
Functional genomics is a field of molecular biology that attempts to make use of the vast wealth of data produced by genomic projects (such as genome sequencing projects) to describe gene (and protein!) functions and interactions. Unlike genomics and proteomics, functional genomics focus on the dynamic aspects such as gene transcription, translation, and protein-protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures.
[edit] Fields of Application
Functional genomics includes function-related aspects of the genome itself such as mutation and polymorphism (such as SNP) analysis, as well as measurement of molecular activities. The latter comprise a number of "-omics" such as transcriptomics (gene expression), proteomics (protein expression), phosphoproteomics and metabolomics. Together these measurement modalities quantifies the various biological processes and powers the understanding of gene and protein functions and interactions.
[edit] Frequently Used Techniques
Functional genomics uses mostly high-throughput techniques to characterize the abundance gene products such as mRNA and proteins. Some typical technology platforms are:
- DNA microarrays and SAGE for mRNA
- two-dimensional gel electrophoresis and mass spectrometry for protein
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 this class 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).
