Functional genomics
Functional genomics is a field of genomics that aims to understand the complex relationship between genotype and phenotype on a global (genome-wide) scale. This is achieved by employing high-throughput techniques to measure the expression of genes and their interactions.
Overview
Functional genomics uses a wide range of experimental and computational methods. These include microarray technology, next-generation sequencing, proteomics, metabolomics, and bioinformatics. These techniques allow researchers to measure the expression of many genes at once, to identify gene networks and pathways, and to observe changes in gene expression and regulation in response to genetic and environmental changes.
Techniques
Microarray technology
Microarray technology is a powerful tool used in functional genomics to study gene expression on a genome-wide scale. It allows researchers to measure the expression levels of thousands of genes simultaneously.
Next-generation sequencing
Next-generation sequencing (NGS) is another important tool in functional genomics. NGS technologies can sequence DNA and RNA much more quickly and cheaply than the previously used Sanger sequencing.
Proteomics
Proteomics is the large-scale study of proteins, particularly their structures and functions. It is an important component of functional genomics as proteins are the functional units of the cell and changes in their expression can reflect changes in gene function.
Metabolomics
Metabolomics is the study of the unique chemical fingerprints that specific cellular processes leave behind. It is a relatively new field and is considered the "final frontier" of molecular biology.
Bioinformatics
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. In functional genomics, bioinformatics tools are used to analyze and interpret the vast amounts of data produced.
Applications
Functional genomics has many applications in various fields such as medicine, agriculture, and biology. It can be used to identify disease genes and drug targets, to improve crop varieties, and to understand basic biological processes.
See also
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