Control and Measure of Gene Expression Flashcards
1
Q
Northern Blot
A
- RNA extraction
- electrophoresis
- northern blotting (RNA transfer to membrane)
- membrane hybridized with labelled probes and visualised
- more RNA = more probes = darker bands
- use both target gene and internal control gene to normalise RNA levels
- poor accuracy levels (visualises relative change)
2
Q
PCR
A
- using probes specific for the genes being measured the number of copies are directly proportional to the initial mRNA levels
3
Q
qPCR
A
- use of a reporter probe binding to target DNA if present
- as taq polymerase replicates the DNA the probe is destroyed releasing the fluorophore
- level is quantified
- threshold cycle shows the number of cycles taken to detect a real signal
4
Q
qPCR normalisation
A
- compensate for initial variations in mRNA and technical sequencing differenecs
- measure control and normalise against experimental
- Ct values used to normalise
5
Q
Limitations of Northern Blot and qPCR
A
- limited in number of genes tested at a time
- limited info (only tell you a gene is expressed)
- blot not quantitatively accurate
6
Q
Microarrays
A
- high throughput
- detects thousands of genes simultaneously
- relies on base pairing hybridisation with probes for each gene to be measured
- more hybridization gives higher fluoresence
- can measure differing expression over time, between tissues, co-expression, etc
7
Q
Affy GeneChip
A
- each genes has 16-20 pairs of probes synthesized on the chip
- individual probe for each gene
- control and experimental samples hybridized to separate slides and compared
- expression fold change calculated by comparasion
8
Q
Limitations of Microarrays
A
- image distortions or light merging
- may need statistical manipulation
- if you have multiple probes for the same gene giving different readouts = problem
- probes unavailable for all genes
- noisy data
- low expression genes not detected
- no information about which gene transcript is expressed
9
Q
RNA sequencing
A
- uses next gen sequencing technology to measure expression
- assumes every mRNA is sequenced the same number of times (if experiment show 2x as much mRNA vs control the expression is 2x more)
10
Q
Benefits of RNA seq
A
- accurate measure of expression, even at low levels
- can identify transcript
- identify novel transcripts with novel splice sites
11
Q
RNA seq Method
A
- mRNA isolation and conversion to cDNA
- sequencing adaptors added
- illumina sequencing
- alignment against genome
- generate sequence counts for all genes in genome
- read counts proportional to gene expression level
12
Q
RNA seq normalisation
A
- compensate for initial variations in mRNA and technical differences with sequencing
- scales read counts so they can be compared
1. raw read count normalisatoin
2. reads/fragments per kilobase per million reads
13
Q
Raw Read Count Normalisation
A
- aim to make normalised count for non differentially expressed genes similar between samples
- doesn’t adjust count distributions between samples
- assumes most genes not differentially expressed adn differentially expressed genes divided equally between up and down
- divide gene count by geometric mean and take median
- apply this normalisation factor
14
Q
RPKM/FPKM
A
- normalises for gene length and library size
15
Q
Transcriptome Profiling
A
- identify variable transcripts where reads cross exon boundaries
- microarrays can’t do this
- reads map to previously annotated sites and novel expressed sequences
