Transcriptome Analysis

Question 1

Why do we care about transcription?

Answer 1

It is the primary means of interpreting info in the genome
it plays a central role in evolution
Often misrelated in disease

Question 2

Complex traits

Answer 2

• > 85% of GWAS associations lie in non-coding regions

- enriched for eQTLs, overlap with regulatory elements

Question 3

Basic principles of gene regulation

Answer 3

Gene expression varies in quantity, space, time, and in response to stimuli
We typically measure steady state RNA
RNA is regulated at the level of transcription, promoter usage, splicing, poly A site usage, stability, and localization

Question 4

Perspectives to study RNA

Answer 4

spatial localization
abundance quantification
transcript isoforms and structure
emphasis on response to stimuli

Question 5

Spatial localization of RNA

Answer 5

Techniques: In situ hybridization, immuno histochemistry, gene fusions

Can provide very precise (sub)cellular resolution

Often on fixed tissues, but live imaging becoming more common

often difficult to quantify due to technical variations

Question 6

immuno histochemistry

Answer 6

treat tissue with antibodies

Question 7

Quantifying RNA abundance

Answer 7

Technqiues: Northern blots, qPCR, microarrays, nano string, RNAseq
Isolate cells, extract RNA, measure steady state RNA
Isolating cells can be difficult

Question 8

transcript isoform usage and structure

Answer 8

techniques: qPCR, nanostring, Long read or paired end RNA seq
microarrays were not particularly good for this
short read RNAseq data has inherent limitations

Question 9

Response to stimulus

Answer 9

Peturb system, measure gene expression

-knock down TF + measure RNA

Question 10

Knock down TF and measure RNA

how do you know if change is direct?

Answer 10

pulse chase experiment

Question 11

method based on pulse chase experiment

Answer 11

nascent transcription quantification (GROseq)

measuring RNA stability

Question 12

EST Sanger sequencing

Answer 12

which is great for gene identification and characterization, long reads enabled isoform reconstruction, too expensive to accurate quantification

Question 13

SAGE

Answer 13

Serial Analysis of Gene Expression

cDNAs cleaved into short <20bp fragments, concatamerized, and sequenced

Question 14

RNAseq molecular biology

Answer 14

extract RNA
purify RNAs of interest (mRNA, miRNA)
fragment, prime
convert to cDNA
attach adapters
sequence single or paired end reads

Question 15

RNAseq analysis outline

Answer 15

In some applications, reads are aligned to transcriptome (some align to transcriptome)
Assemble and quantify transcript abundance
test for differential expression(data are count based)

Question 16

RNAseq complications

Answer 16

Alignment –>short reads, large gaps, 1% error

• using annoyed gene models helps
• paired end and longer reads help

Experimental design - replicates –>bc the experiment is fairly expensive and complicated, many people do not perform (enough) replicates

Confounding variables:
-randomization is critical in experimental design

small n, large p
empirical bayes approaches

Question 17

Computation cost

Answer 17

Tophat ~1 hr/ 1 M reads on standard workstation

Question 18

Confounding variables:

Answer 18

difficult to control for variables can have large effects on RNAseq data
RNA extraction data, person performing library construction, kit batch, sequencing run, temperature, time day…

Question 19

hidden variables

Answer 19

latent variable techniques: PCA, factor analysis, PEER, SVA

Question 20

FPKM

Answer 20

Fragments per kilobase per million mapped reads
standard unit of measurement for RNA abundance from RNAseq
normalizes by transcript length and read depth

relative measure- depends upon the abundance of all transcripts,

Question 21

Surrogate variable analysis

Answer 21

ranking features of association accounting for hidden variables that are unmeasured