Chapter !9 - Microarrays and Transcriptomics Code : KSI0018
<Index of Chpater 9>
Introduction
Microarrays provide the link between the static genome and the dynamic proteome. We use microarrays
(1) To analyse the mRNAs in a cell, to reavel the expression patterns of proteins.
(2) To detect genomic DNA sequences to reveal absent or mutated genes.
The transcriptome of a cell is the set of RNA molecules it contains. The proteome is its proteins .
We infer protein expression patterns from measuements of the relative amounts of the corresponding mRNAs. Hybridization in an accurate and sensitive sequence is present
<The basic innovation of microarrays is parallel processing>
1. One-to-one . To detect whether one oligonucleotide has a particular known sequence, test whether it can hybridize to the oligonucleotide with the complementar sequence.
2. Many to one. To detect the presence or absence of a query oligonucleotide in a mixture, spread the mixture out and test component of the mixture for binding to the oligonucleotide complementary to the query. This is a nothern or Southern blot.
3. Many to many. To detect the presence or absence of many oligonucleotides in a mixture, synthesize a set of oligonucleotides, one complemantary to each sequence of the query list and test each component of the mixture for binding to each memeber of the set of complementary oligonucleotides.
The immobilized material on the chip is the probe. THe sample tested is the target.
Microarrays are also need to screen for mutations and polymorphisms. Microarrays containing many sequence variants of a single gene can detect differences from a standard reference sequence.
Different types of chip support different investigation
Expression chip / mismatched oligonucleotide / probe pair/ genomic hybridization / Mutation or polymorphism microarray analysis / protein microarrays / tissue microarrays.
-Applications of DNA microarrays
>Investigating cellular states and processes
> COmparision of related species
> Diagnosis of genetic disease
> Genetic warning signs
> Preciese diagnosis of disease
> Drug selection
> Determination of gene function
> Target selection for drug design
Analysis of microarrary data
Two general approaches to the analysis of a gene expression matrix involve
1. Comparisons focused on the genes. i.e. Comparing distrivbutions of expression patterns of different genes by comparing rows in the expression matrix.
2. Comparisons focused on samples. Comparing expression profiles of different samples by comparing columns of the expression matrix.
Gene vectors/ sample vectors
Depending on the origin of the samples, what is already known about them and what we want to learn data anaylsis can proceed in different directions .
1. Known characteristics.
2. Pre-assign
Reduction of dimensionality.
> Processing the data from a microarray experiment produces a gene expression table, or matrix. The rows index the genes and the columns index the samples. We can either focus on the genes, and ask. How do patterns of epxpression of different genes very among the different samples? Or we can focus on the samples, and ask. How do the samples differ in teir gene expression pattterns?
Expression patterns in different physiological states
-The diauxic shift in Saccharomyces cerevisiae
-Sleep in rats and fruit flies
Expression pattern changes in development
-Variation of expression patterns during the life cycle of Drosophila melanogaster
-Flower formation in roses
Expression patterns in learning and memory : Long-term potentiation
-Conserved clusters of co-expressing genes
Evolutionary changes in expression patterns
Applications of microarrays in medicine
-Development of antibiotic resistance in bacteria
-Childhood leukaemias
Whole trranscriptome shotgun sequencing : RNA-seq
