Function Genomics

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Genomics is a discipline in genetics concerned with the study of thegenomes of organisms.

The field includes efforts to determine the entire DNA sequence oforganisms and fine-scale genetic mapping.

The field also includes studies of intra-genomic phenomena such asheterosis, epistasis, pleiotropy and other interactions between lociand alleles within the genome.

Research of single genes fall into the definition of genomics if the aimof this genetic, pathway, and functional information analysis is toelucidate its effect on, place in, and response to the entire genome'snetworks.


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Genomics

StructuralGenomics

FunctionalGenomics

ComparativeGenomics

MetaGenomics


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Metagenomics is the study ofmetagenomes, genetic material

recovered directly from environmental samples. The broad field mayalso be referred to as environmental genomics, ecogenomics orcommunity genomics.

Structural genomics seeks to describe the 3-dimensional structureof every protein encoded by a given genome. This genome-based

approach allows for a high-throughput method of structuredetermination by a combination of experimental and modelingapproaches.

Comparative genomics is the study of the relationship of genomestructure and function across different biological species or strains.

Comparative genomics exploits both similarities and differences in theproteins, RNA, and regulatory regions of different organisms to inferhow selection has acted upon these elements.


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Functional genomics is a field of molecular biology that attempts tomake use of the vast wealth of data produced by genomic projects(such as genome sequencing projects) to describe gene (and protein)functions and interactions.

Functional Genomics V/S Genomics

Functional Genomics Genomics

Functional genomics focuses onthe dynamic aspects such as

gene transcription, translation,and proteinprotein interactions.

Involves high-throughputmethods.

Genomics deals with the staticaspects of the genomic

information such as DNAsequence or structures.

Involves traditional gene-by-gene approach.


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To understand the relationship between an organism's genome andits phenotype.

It helps in understanding of the dynamic properties of an organismat cellular and/or organism levels.

It provides a more complete picture of how biological function arisesfrom the information encoded in an organism's genome.

How a particular mutation leads to a given phenotype has importantimplications for human genetic diseases.

Attempt are being made to find out the pattern of gene expression,which genes are switched on and which are switched off in different

tissues during different developmental stage.


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Functional genomics includes function-related aspects of the genomeitself such as mutation and polymorphism (such as SNP) analysis, aswell as measurement of molecular activities.

The latter comprise a number of "-omics" such as transcriptomics(gene expression), proteomics (protein expression), andmetabolomics.

Functional genomics uses mostly multiplex techniques to measure the

abundance of many or all gene products such as mRNAs or proteinswithin a biological sample.

Together these measurement modalities endeavor to quantitate thevarious biological processes and improve our understanding of geneand protein functions and interactions.


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a) Genetic interactionmapping

Systematic pairwise deletion ofgenes or inhibition of geneexpression can be used to

identify genes with relatedfunction, even if they do notinteract physically.

Epistasis refers to the fact thateffects for two different geneknockouts may not be additive;that is, the phenotype thatresults when two genes areinhibited may be different fromthe sum of the effects of singleknockouts.


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b) The ENCODE project

The ENCODE (Encyclopedia of DNA elements) project is an in-depthanalysis of the human genome whose goal is to identify all thefunctional elements of genomic DNA, in both coding and noncodingregions.

To this point, only the pilot phase of the study has been completed,

involving hundreds of assays performed on 44 regions of known orunknown function comprising 1% of the human genome.

Important results include evidence from genomic tiling arrays thatmost nucleotides are transcribed as coding transcripts, noncodingRNAs, or random transcripts, the discovery of additional

transcriptional regulatory sites, further elucidation of chromatin-modifying mechanisms.


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a)SAGE

SAGE (Serial analysis of gene expression) is an alternate methodof gene expression analysis based on RNA sequencing rather thanhybridization.

SAGE relies on the sequencing of 1017 base pair tags which areunique 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 oftranscripts per cell, since it does not depend on prior knowledgeof what transcripts to study (as microarrays do).


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b) Microarrays

Microarrays measure the amount of

mRNA in a sample that correspondsto a given gene or probe DNAsequence.

Probe sequences are immobilized ona solid surface and allowed tohybridize with fluorescently labeled

target mRNA.

The intensity of fluorescence of aspot is proportional to the amount of

target sequence that has hybridizedto that spot, and therefore to theabundance of that mRNA sequencein the sample.


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Microarrays allow for identification of candidate genesinvolved in a given process based on variation between

transcript levels for different conditions and shared expressionpatterns with genes of known function.


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a) AP/MS

Affinity purification and mass spectrometry(AP/MS) is able to identify proteins thatinteract with one another in complexes.

Complexes of proteins are allowed to formaround a particular bait protein.

The bait protein is identified using anantibody or a recombinant tag which allowsit to be extracted along with any proteinsthat have formed a complex with it.

The proteins are then digested into shortpeptide fragments and mass spectrometryis used to identify the proteins based onthe mass-to-charge ratios of those

fragments.
http://en.wikipedia.org/wiki/Affinity_purification


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b) Yeast two-hybrid system

A yeast two-hybrid (Y2H) screen tests a "bait" protein against manypotential interacting proteins ("prey") to identify physical protein

protein interactions.

This system is based on a transcription factor, originally GAL4,whoseseparate DNA-binding and transcription activation domains are bothrequired in order for the protein to cause transcription of a reportergene.

In a Y2H screen, the "bait" protein is fused to the binding domain ofGAL4, and a library of potential "prey" (interacting) proteins isrecombinantly expressed in a vector with the activation domain.

In vivo interaction of bait and prey proteins in a yeast cell brings theactivation and binding domains of GAL4 close enough together to resultin expression of a reporter gene.

It is also possible to systematically test a library of bait proteinsagainst a library of prey proteins to identify all possible interactions in

a cell.


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a) Mutagenesis

Gene function can be investigated by systematically knockingout genes one by one.

This is done by either deletion or disruption of function (suchas by insertional mutagenesis) and the resulting organismsare screened for phenotypes that provide clues to the function

of the disrupted gene.


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b) RNAi

RNA interference (RNAi) methods can be used to transiently silence

or knock down gene expression using ~20 base-pair double-stranded RNA typically delivered by transfection of synthetic ~20-mer short-interfering RNA molecules (siRNAs) or by virally encodedshort-hairpin RNAs (shRNAs).

RNAi screens, typically performed in cell culture-based assays orexperimental organisms (such as C. elegans) can be used tosystematically disrupt nearly every gene in a genome or subsets ofgenes (sub-genomes); possible functions of disrupted genes can beassigned based on observed phenotypes.


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Because of the large quantity of data produced by these techniquesand the desire to find biologically meaningful patterns, bioinformaticsis crucial to analysis of functional genomics data.

Examples of techniques in this class are:

1. Data clustering or principal component analysis for unsupervisedmachine learning (class detection) as well as2. Artificial neural networks or support vector machines for supervisedmachine learning (class prediction, classification).


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Function Genomics