Functional Genomics

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Functional Genomics

Life comes with information. All information for an organism is encoded in its genome. Functional genomics is molecular biology that utilizes the vast wealth of data produced by genomics (and other omics) to describe the functions of genes and their gene products (including proteins) as well as their interactions. Functional genomic approaches involve the use of large-scale and/or high-throughput methods to understand the function, of major biomolecules, and infer functions from the genome to the phenotype of an organism. In contrast to genomics, functional genomics has a focus on the dynamic aspects such as transcription, translation, gene expression regulation and interactions among biological entities like proteins, as opposed to the more static aspects of genomic information that include DNA sequence and structures. Functional genomics seeks answers to questions about the function of DNA at the level of genes, transcripts and protein products. The key characteristic of studies involving functional genomics is their genome-wide approach to answer those questions, usually involving high-throughput methods rather than reductionistic "gene-by-gene" approach.


The aim of functional genomics is to generate an understanding of biology in order to bridge the gap between an organism's genome and its expressed phenotype.

Functional genomics is frequently used to refer to the many possible approaches to understand the properties and activities of an organism's genes and gene products. It involves the study of natural variation in genes, RNA and proteins over time as well as both natural or experimental functional interruptions affecting genes and their gene products.

Overall it synthesizes data from various omics (genomics, 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 suggests functional genomics investigates functional-related aspects of the genome such as analysis of mutations and polymorphisms as well as the measurement of molecular activities.


Functional genomics relies heavily on bioinformatics to make sense of the vast amount of data being generated. Among the techniques utilized are data clustering or dimensional reduction (e.g. principal component analysis) for unsupervised machine learning (class detection). Also used are support vector machines and artificial neural networks for supervised machine learning (class prediction, classification). Functional enrichment analysis with ontologies is used for instance to determine the extent of over- or under-expression of genes.

The new generation of sequencing technologies provides unprecedented opportunities for high-throughput functional genomics research. These technologies can be applied in a variety of contexts, including whole-genome sequencing, targeted resequencing, discovery of transcription factor binding sites, and coding/non-coding RNA expression profiles as well as epigenetic marks [Morozova+Marra:2008].


Systematic pairwise deletion/overexpression of genes or inhibition/activation of gene expression can be utilized to identify genes with related function, even if they do not interact physically.

The ENCODE (Encyclopedia of DNA Elements) project builds a comprehensive parts list of functional elements in the human genome, including elements that act at the RNA and protein levels and regulatory elements that control cells and circumstances in which a gene is active. It is intended as follow-up to the Human Genome Project to map all functional elements in the human genome.


Bisulfite sequencing is the utilization of bisulfite treatment of DNA to determine its pattern of modifications such as methylation and hydroxymethylation.

ChIP-sequencing (ChIP-seq) is a technique to analyse protein interactions with DNA. It combines chromatin immunoprecipitation with massively parallel DNA sequencing to identify the binding sites of proteins associated with DNA.


Microarrays measure the amount of RNA in a sample that corresponds to a probe DNA sequence or gene.

An alternative method of gene expression analysis is SAGE (serial analysis of gene expression) that is based on RNA sequencing rather than hybridization.


A yeast two-hybrid (Y2H) screen assesses a "bait" protein against many candidate interacting proteins ("prey") to identify physical protein-protein interactions.

Affinity purification and mass spectrometry (AP/MS) is capable of identifying proteins that interact with one another in complexes.

Loss/Gain of Functions

Mutagenesis is commonly used to alter genes. The function of genes can be investigated by systematically "knocking out" or "overexpressing" genes one by one. This is done either by disruption of function (via deletion or insertional mutagenesis) or by enhancing function (via inserting multiple copies or rendering promoters more active).

RNAi (RNA interference) is a natural biological process in which RNA inhibits gene expression or translation by neutralizing a target mRNA. This process can be used to transiently knock-down the expression of a gene of interest using targeted short double-stranded RNA. The knock-down can also be made permanent if DNA expressing interfering RNA is introduced into the cells.

Functional genomics uses genomics data to study gene and protein expression and function on a global scale (genome-wide or system-wide), focusing on gene transcription, translation and protein-protein interactions, and often involving high-throughput methods. Overall, functional genomics has the goal to understand the relationship between an organism's genome and its phenotype. Functional genomics helps to lay a solid foundation for the systems biology approach.


[Morozova+Marra:2008]Morozova, Olena & Marra, Marco A (2008). 'Applications of next-generation sequencing technologies in functional genomics.' Genomics. 92(5), pp. 255-64.

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