Download CHIP-seq and RNA-seq

RNA-seq
Manpreet S. Katari
DNA
RNA
protein
phenotype
cDNA
Abundance of mRNA is
what we try to measure
Microarrays vs Northern blots: from Gene to Genome Science
• Northern blot: limited by
number of lanes in gel
• Microarray: A large number
of DNA fragments are
attached in a systematic way
to a solid substrate, can
measure mRNA levels for
thousands of genes (~ every
gene in a genome) in parallel
Evolution of Sequence Technology
Transcriptomics using RNA-seq
Genome-wide expression analysis
• Goal: to measure RNA levels of all genes in a genome under
various experimental conditions
• RNA levels vary with:
–
–
–
–
Cell type
Developmental stage
External stimuli
Disease state
• Time and location of expression provide information on
genes’ function and interactions, and can be useful for
many purposes, including disease diagnostics and medical
applications.
For High-Throughput Transcriptomics studies,
comparisons are almost always across experiments
45
45
40
35
40
35
30
30
25
25
20
20
15
15
10
10
5
5
0
0
Gene A
Gene B
Gene A
whole body
liver
liver
lung
Gene B
brain
kidney
Questions that can be addressed with
genome-wide expression analysis:
• What genes have similar function?
• What regulatory pathways exist?
• Can we subdivide experiments or genes into
meaningful classes?
• Can we correctly classify an unknown experiment or
gene into a known class?
• Can we make better treatment decisions for a cancer
patient based on his or her gene expression profile?
First two basic tasks to generating meaningful
data for transcriptomics analysis
• Normalize or scale all samples and replicated to each
other
• Make a (statistical) statement about what changes
are evident in the comparison
Microarrays
Provides the mRNA level of thousands of genes (sometimes almost all known
genes in a genome) in a given sample
Sample=tissue (e.g., liver, brain), tissue in a specific environment or state
(e.g., brain with cancer), etc.
Three types of arrays
• Spotted microarrays
– Long dsDNA (typically genomic PCR products)
• On-chip oligonucleotide synthesis
– Photolithography
• Affymetrix (~25-mers)
– Ink-jet printing
• Agilent (~60-mers)
Sample labeling
Fluorescent cDNA
• cDNA made using reverse transcriptase
• Fluorescently labeled nucleotides added
• Labeled nucleotides incorporated into cDNA
cRNA + biotin
• cDNA made using reverse transcriptase
 Linker added with T7 RNA polymerase
recognition site
 T7 polymerase added and biotin labeled
RNA bases
 Biotin label incorporated into cRNA
+
Microarray hybridization
Spotted microarrays
– Competitive hybridization: two labeled
cDNA samples (experimental and control)
hybridized to same slide
– Cy3 and Cy5 dye labeling, fluoresce at
different wavelengths
Affymetrix GeneChips
– One labeled RNA population per chip
– Biotin labeling, binds to fluorescently
labeled avidin
(Comparison made between hybridization
intensities of same oligonucleotides on
different chips).
samples
mRNA
cDNA
DNA
microarray
Affymetrix system
What is the Affymetrix Signal?
1. Background subtraction:
1. Microarray is divided
into sectors
2. Probe signal is ordered
and the lowest 2% is
taken as the noise
level
3. A weighted mean of
the background is
subtracted from the
signal, such that closer
sectors are weighted
more heavily
Background Adjustment
Estimating background effect PM=true signal + background
Quantile Normalization
1. Sort each
column
disregarding gene
order
2. Calculate
row averages
5.3
Gene A
3
100
500
3
10
3
Gene B
17
10
150
10
100
150
Gene C
10
1000
3
17
1000 500
5.3
5.3
5.3
86.7
86.7
86.7
505.7
505.7
505.7
3. Substitute
average
values for
real ones
86.7
505.7
Gene A
5.3
86.7
505.7
Gene B
505.7
5.3
86.7
Gene C
86.7
505.7
5.3
4. Restore
gene order
Normalizing the Data
• RPKM (Reads per Kilobase of exons per million
reads)
Score =
R
NT
R = # of unique reads for the gene
N = Size of the gene (sum of exons / 1000)
T = total number of reads in the library mapped to the genome / 1,000,000
Reproducibility, linearity and
sensitivity.
RNA-seq provides even more
Candidate new and revised exons
Comparison of platforms for detecting
gene expression
AFFY Gene
Chip
Illumina
All protein coding genes are represented
X
Can detect all the different types of RNA
X
Cost
X
X
Can determine gene regulation
X
X
Requires pre-existing knowledge of gene sequence
X
As the price of sequencing goes down, there will be almost no advantage
Of Microarray over RNA-seq
Mapping Reads from RNA molecules
• What is the advantage of mapping reads from
RNA to the genome sequenced instead of a
database of all predicted RNA molecules?
– We are not depending on the quality of annotation.
– We are not assuming that we know about all of the
RNA molecules in the cell.
• How can we find reads mapping to spliced
junctions?
– Create a separate database of all possible spliced
junctions
– Split reads in half and map them separately.
Bowtie
&
TopHat
Cufflinks first starts with the output of any
alignment tool such as TopHat
Then it assembles the isoforms by first identifying
the reads that can not be assembled together.
Then calculate abundance
Assembling the reads to identify
transcripts.
CuffCompare
• The program cuffcompare helps you:
– Compare your assembled transcripts to a reference
annotation
– Track Cufflinks transcripts across multiple experiments
(e.g. across a time course)
• Output contains codes
–
–
–
–
–
= match
c contained
j new isoform
u unknown, intergenic transcript
i single exon in intron region
Identification of spliced junctions depends
largely on the depth of sequences
coverage.
Cuffdiff
• Can be use to find significant changes in
transcript expression, splicing, and promoter
use.
– Inputs are:
• Annotation to compare (can be output from cufflinks)
• Tophat output from different samples
• Options are similar to cufflinks, can also specify a
different FDR cutoff.
GENE B
GENE A
Which comparison is more convincing
that
genes
are
different?
Treatment
Control
Rep1
20
Rep1
30
Rep2
21
Rep2
31
Rep3
19
Rep3
29
Mean
20
Mean
30
Rep1
10
Rep1
20
Rep2
20
Rep2
30
Rep3
30
Rep3
40
Mean
20
Mean
30
COMPARISON A
COMPARISON B
t test
Difference in the means
Standard Error of the
difference
Can use this test statistic
to evaluate the probability
that the two means are
Var = sum of squares of the difference
same using critical values
n-1
of T:
Degrees of freedom = nt+ncWhere you select the
2
probability of making a
type I error e.g., 0.05
Volcano plot: visualizing significance and fold
change
Volcano plot: visualizing significance and fold
change
Volcano plot: visualizing significance and fold
change
Assumptions of the t-test
• Samples are drawn from normal distributions
– i.e. our estimates of geneA and geneB are random
samples from a normal distribution
• The variance of the two populations is equal
• There is no mean variance relationship
RNA-seq data
•
•
•
•
Count data (discrete)
Possible to get zero
Cannot get negative number
Each sequence read is a random event drawn
from a larger population.
• Variance increases with the mean
RNA-seq data: variance > mean
RNA-seq data are consistent with an over-dispersed poisson: variance = a*mean
Should we give treat a difference between 9 vs 12
reads the same as 900 vs 1200?
t = -3.6742, p-value = 0.02131
t = -3.6742, p-value = 0.02131
t test does not account for scale of the data
t = -3.6742, p-value = 0.02131
t = -3.6742, p-value = 0.02131
Test using a negative binomial model [glm.nb()]
p-value = 0.258
p-value = 1.03e-05
Test using a negative binomial model [glm.nb()]
p-value = 0.258
p-value = 1.03e-05
RNA-seq pipeline
Manpreet S. Katari
The basic workflow
1. Perform Quality control - fastqc
2. Trim low quality sequence - trimmomatic
3. Map the reads to the Genome Build the database – bowtie2
b. Run the alignment - tophat
a.