All information of an organism is encoded in its genome. Functional Genomics is molecular biology that utilizes the vast wealth of data produced by genomics project to describe the functions of genes and their gene products as well as their interactions. Functional genomic approaches involve the use of large-scale and/or high-throughput methods to understand function of the major biomolecules.
The approach aims to explains dynamic processes like development and aging, and applies high throughput methods in order to infer functions from the genome to the phenotype of an organism.
It involves the studies of natural variation in genes, RNA and proteins over time as well as natural or experimental functional interruptions affecting genes and their gene products.
Overall it synthesizes data from various omics (transcriptomics, proteomics, metabolomics and interactomics) into an understanding of the dynamic properties of organismal processes and activities. It provides insights into how biological function arises from information encoded in an organism's genome.
As the name suggest functional genomics investigates functional-related aspects of the genome like mutations and polymorphisms analysis as well as measurement of molecular activities.
Functional genomics heavily uses bioinformatics to make any sense of the vast amount of data. Among the utilized techniques are data clustering or principle 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). Functional enrichment analysis with ontologies is used to for instance determine the extent of over or under expressions.