Each theme represents an area in which members either have considerable strength upon which to capitalize, or one that we recognize as being important and wish to build upon existing scientific strengths. While these areas may seem different, they all share a common interest in the use of genetics. In fact, a number of members participate in more than one research area.
A more long-term goal is to understand the relationship between an organism's genome and its phenotype. The promise of functional genomics is to generate and synthesize genomic and proteomic knowledge into an understanding of the dynamic properties of an organism.
This would provide a more complete picture than studies of single genes. Integration of functional genomics data is also the goal of systems biology. Techniques and applications[ edit ] Functional genomics includes function-related aspects of the genome itself such as mutation and polymorphism such as single nucleotide polymorphism SNP analysisas well as measurement of molecular activities.
The latter comprise a number of "- omics " such as transcriptomics gene expressionproteomics protein productionand metabolomics. Functional genomics uses mostly multiplex techniques to measure the abundance of many or all gene products such as mRNAs or proteins within a biological sample.
Together these measurement modalities endeavor to quantitate the various biological processes and improve our understanding of gene and protein functions and interactions. Epistasis Systematic pairwise deletion of genes or inhibition of gene expression can be used to identify genes with related function, even if they do not interact physically.
Epistasis refers to the fact that effects for two different gene knockouts may not be additive; that is, the phenotype that results when two genes are inhibited may be different from the sum of the effects of single knockouts.
To this point[ when?
Important results include evidence from genomic tiling arrays that most nucleotides are transcribed as coding transcripts, noncoding RNAs, or random transcripts, the discovery of additional transcriptional regulatory sites, further elucidation of chromatin-modifying Functional genomics and proteomics.
At the RNA level: The intensity of fluorescence of a spot is proportional to the amount of target sequence that has hybridized to that spot, and therefore to the abundance of that mRNA sequence in the sample.
Microarrays allow for identification of candidate genes involved in a given process based on variation between transcript levels for different conditions and shared expression patterns with genes of known function. Serial analysis of gene expression Serial analysis of gene expression SAGE is an alternate method of analysis based on RNA sequencing rather than hybridization.
SAGE relies on the sequencing of 10—17 base pair tags which are unique to each gene. These tags are produced from poly-A mRNA and ligated end-to-end before sequencing. SAGE gives an unbiased measurement of the number of transcripts per cell, since it does not depend on prior knowledge of what transcripts to study as microarrays do.
This is typically done by next-generation sequencing. Next generation sequencing is the gold standard tool for non-coding RNA discovery, profiling and expression analysis. At the protein level: Two-hybrid screening A yeast two-hybrid screening Y2H tests a "bait" protein against many potential interacting proteins "prey" to identify physical protein—protein interactions.
This system is based on a transcription factor, originally GAL4,  whose separate DNA-binding and transcription activation domains are both required in order for the protein to cause transcription of a reporter gene. In a Y2H screen, the "bait" protein is fused to the binding domain of GAL4, and a library of potential "prey" interacting proteins is recombinantly expressed in a vector with the activation domain.
In vivo interaction of bait and prey proteins in a yeast cell brings the activation and binding domains of GAL4 close enough together to result in expression of a reporter gene.
It is also possible to systematically test a library of bait proteins against a library of prey proteins to identify all possible interactions in a cell.
The bait protein is identified using an antibody or a recombinant tag which allows it to be extracted along with any proteins that have formed a complex with it. The proteins are then digested into short peptide fragments and mass spectrometry is used to identify the proteins based on the mass-to-charge ratios of those fragments.
This is done by either deletion or disruption of function such as by insertional mutagenesis and the resulting organisms are screened for phenotypes that provide clues to the function of the disrupted gene.
RNAi screens, typically performed in cell culture-based assays or experimental organisms such as C. Functional annotations for genes[ edit ] Main article: A sequence identified as a putative gene must be confirmed by further evidence, such as similarity to cDNA or EST sequences from the same organism, similarity of the predicted protein sequence to known proteins, association with promoter sequences, or evidence that mutating the sequence produces an observable phenotype.
Rosetta stone approach[ edit ] The Rosetta stone approach is a computation method of de novo protein function prediction, based on the hypothesis that some proteins involved in a given physiological process may exist as two separate genes in one organism and as a single gene in another.
Genomes are scanned for sequences that are independent in one organism and in a single open reading frame in another. If two genes have fused, it is predicted that they have similar biological functions that make such co-regulation advantageous.
Functional genomics and bioinformatics[ edit ] Because of the large quantity of data produced by these techniques and the desire to find biologically meaningful patterns, bioinformatics is crucial to analysis of functional genomics data.
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.
Functional enrichment analysis is used to determine the extent of over- or under-expression positive- or negative- regulators in case of RNAi screens of functional categories relative to a background sets.Functional Genomics and Proteomics Research The free-living, transparent nematode Our aims are to use a genome-wide approach to identify and study the functional components that are part of the posttranslational machinery or molecular targets of the posttranslational machinery.
Proteomics and Functional Genomics Core FacilityCurrently under renovation. Please check back for updates. Thank you. Functional genomics is the generic term used to describe methods that analyse the various genes expressed by a cell or tissue, while proteomics aims to define the protein complement.
These technologies now allow an almost complete examination of . Genomics and proteomics are just two of the terms used to describe the identification and characterization of set of genes, RNA, and proteins that comprise an organism.
Building upon the work carried out by members of the Structural and Functional Genomics research program, the systems biology effort in Structural and Functional Genomics is focused on the DNA damage response utilizing model organisms and a diversity of approaches including microarray methods, proteomics, and classical genetics.
The Network Biology Collaborative Centre at the Lunenfeld-Tanenbaum Research Institute is a national platform providing integrated solutions for functional genomics and proteomics.