Download Data Analysis for High-Throughput Sequencing

Data Analysis for HighThroughput Sequencing
Mark Reimers
Tobias Guennel
Department of Biostatistics
Unto the Frontiers of Ignorance
“I love the way this workshop starts off
with things we understand fairly well and
works up to the cutting edge of things we
don’t understand at all”
- Mike Neale, Oct 14, 2010
The New Boyfriend/Girlfriend
Where Does HTS Really Make the Difference?
• Sequencing for novel variants
• ChIP-Seq for DNA-binding proteins or less
common histone marks
• Allele-specific expression
• COMING SOON
• DNA methylation
Outline
• Biases in reads
• RNA-Seq
– normalization
– basic tests
– differential splicing
• Finding peaks in ChIP-Seq
Technical Biases – Sequence Start
The initial bases of reads are highly biased, and the
bias depends on RNA/DNA preparation
Sequence Biases – K-mers Differ
• (Schroeder et al,
PLoS One, 2010)
calculated proportions
of words (k-mers)
starting at various
positions
Expected frequencies
if bases random
Position of single mismatch in
uniquely mapped tags
60000
50000
40000
30000
sample 1
sample 2
20000
10000
0
0
3
6
9 12 15 18 21 24 27 30 33 36
position of single mismatch
Courtesy Jean & Danielle Thierry-Mieg
Types of mismatches in uniquely mapped
tags with a single mismatch are profoundly
asymmetric and biased
800000
700000
600000
500000
400000
300000
200000
Any single
G >A
G >T
G >C
A >C
T >C
T >A
C >A
A >G
C >T
T >G
A >T
Delete T
C >G
Insert A
Delete A
Delete G
Insert T
Delete C
Insert C
0
Insert G
100000
Courtesy Jean & Danielle Thierry-Mieg
Technical Biases – Initiation Sites
COX1
Different Platforms Have Different Biases
• (Harismendy et al,
Genome Biology,
2009) sequenced a
section of 4 HapMap
individuals on Roche
454, on Illumina, and
on SOLiD
• 454 had most even
coverage
Initiation Biases Dwarf Splicing
• Counts of reads along
gene APOE in
different tissues of
data from Wold lab.
(a) Brain, (b) liver, (c)
skeletal muscle
Variation in Technical Biases
• Sometimes the initial base biases change
substantially – most base proportions
change together – one PC explains 95%
• In most preparations the initiation site
biases change by a few percent
• In a few preparations the initiation site
biases change by ~20%-30%
• This may have consequences for
representation in ChIP-Seq assays
RNA-Seq Data Analysis
Biases in Proportions
• Fragments compete for real-estate on the
lane
• If a few dozen genes are highly expressed
in one tissue, they will competitively inhibit
the sequencing of other genes, resulting in
what appears to be lower expression
Effects of Competition
• (Robinson & Oshlak, Genome Biology, 2010)
A Simple Normalization
• Align the medians of the housekeeping
genes, or the genes that are not
expressed at very high levels in any
sample, across the samples
A Simple Model for Counts
• Poisson distribution of counts within a
gene with mean proportional to Np
• SD of variation equal to square root of Np
• Problem: Actual variation of counts
between replicate samples is significantly
higher than root Np
• Probably reflecting systematic biases
Hacks for Over-Dispersion
• Like l fudge-factor in GWAS
• Use negative binomial model
– There is no relation to meaning of distribution
– numbers of nulls until something happens
– Convenient way to parametrise overdispersion
• Bioconductor package edgeR estimates
parameters by Maximum Likelihood
Alternate Transcripts: Splicing Index
• For each exon, the proportion of
transcripts in which the exon appears
• Hard to estimate because different exons
have different representation probabilities
• Use ratios of exons
• Use constitutive exons (if known) as
baseline: for them SI=1
from Wang et al, Nature, 2008
Detecting Alternate Splicing – I
• (Wang et al, Nature,
2008) measured
splicing index for
several tissues
Splicing: Junction Reads
• Some reads will span two different exons
• Need long enough reads to be able to
reliably map both sides
• Can use information from one exon to
identify gene and restrict possibilities for 5’
end other exon
from Wang et al
NAR 2010
ChIP-Seq
A View of ChIP-Seq Data
• Typically reads are quite sparsely distributed
over the genome
• Controls (i.e. no pull-down by antibody) often
show smaller peaks at the same locations
• Probably due to open chromatin at promoter
Rozowsky et al Nature Methods, 2009
Always Have a Control
• High correlation
between peaks in
control samples and
peaks in ChIP sample
• Must subtract
estimate of
background from
control tags
Locating Binding Sites
• Use the fact that
reads on opposite
sides of the site
represent are
sequenced in
opposite senses
From Zhao et al
NAR 2009