Lec 7- structural variant detection, microarrays + RNA seq.

Question 1

Name some large scale chromosomal rearrangements that result in structural variants (5)

Answer 1

Insertions
Deletions
Duplications
Inversions
Translocations
(IDDIT)

Question 2

How do you use coverage depth to detect structural variants?

Answer 2

1. Reference genome of WT
2. Sequence mutant genome many times (illumina)
3. Map reads of mutant against reference genome
4. Dense coverage= duplication
   Gap in coverage= deletion

Question 3

Explain read pairs to detect structural variation

Answer 3

1. Map read pairs to reference genome
2. sample with no variant should map read pairs around 500 bp apart
3. sample with deletion map reads <500bp apart (gap in genome)
4. sample with insertions, one read in reference genome + one not

Question 4

Explain split reads to detect structural variants

Answer 4

• Detects if two ends of read map best in other parts of reference + create a split (deletions mostly)
• can see exactly which bit of reference genome absent from sample

Question 5

Explain using assembly to detect structural variants

Answer 5

Map reads
Match ends + create sequence
Often used for cancers

Question 6

Is using short reads for structural variation detection effective?

Answer 6

Not effective (very time consuming) but does work
Modern detection uses long reads as length of read usually overlaps entire length of variant

Question 7

What does genomics and transcriptomics tell us?

Answer 7

Genomics = genome structure
Transcriptomics = gene expression

Question 8

Explain Northern Blotting

Answer 8

1. Extract RNA from sample
2. Use gel electrophoresis to separate transcripts by size
3. Transfer RNA to a membrane (northern blotting)
4. Addition of DNA probe with fluorescent or radioactive tag
5. Probes hybridise with complementary RNA
6. Hit with UV or Xray to visualise + Level of expression determined by band size

Question 9

Explain RT- qPCR
(reverse transcriptase quantitative PCR)

Answer 9

1. Reverse transcriptase to make cDNA from RNA
2. cDNA has fluorescent primer- can measure how much DNA is amplified after how ever many cycles
3. Watch amount of RNA in real time

Question 10

Problem with Northern blot and RT-qPCR

Answer 10

Only measures gene expression for single gene not genome as a whole

Question 11

Method of measuring gene expression genome wide

Answer 11

Microarrays and RNA-seq

Question 12

Explain Microarrays

Answer 12

1. Clusters of same sequence of oligonucleotides covalently bonded to slide
2. Flurorescently labelled RNA from sample added
3. RNA hybridise to complementary DNA
4. Brighter the cluster= more RNA bound therefore more RNA in sample and increased expression of gene

Question 13

Difference between technical replication and biological replication

Answer 13

• Technical creates many reads of same sample to show technical variation + reproducibility
• Biological repeats entire experiment again (new sample + new read)
  Shows variation in biological system

Question 14

Limitations of microarray

Answer 14

• considered low resolution tech
• Don’t know if exact seq. is present if there is a signal
• Don’t know if there are seqs. present in sample not covered by microarray
• limit to how much RNA binds to particular spot on (limit ability to distinguish expression level of highly expressed genes)

Question 15

Describe RNA-seq

Answer 15

1. Fragment RNA
2. Convert to cDNA + add adapter sequences
3. Sequence DNA (high throughput sequencing like Illumina)
4. Sequence mapped to reference genome- number of reads mapping to each gene in reference is measure of expression level of gene

Question 16

What poses a challenge to RNA-seq?

Answer 16

RNA splicing
Since mRNA sequence doesn’t exactly correspond to reference genome sequence
- use software package aware of this
(spliced out gene looks like a deletion)

Question 17

Benefits of RNA-seq over microarray

Answer 17

• Both = minimal technical variation
• RNA-seq= larger dynamic range than micro
  Greater ability to distinguish levels of expression
• RNA-seq= genome wide + detect novel transcripts
• RNA-seq detect SNPs
• RNA-seq can be done without reference genome (de novo assembly of transcriptome)
• Microarray only gives info on pre-selected regions of genome