1. Data Analysis for High-Throughput Sequencing
Mark Reimers
Tobias Guennel
Department of Biostatistics

2. 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

3. The New Boyfriend/Girlfriend

4. 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

5. Outline Biases in reads RNA-Seq Finding peaks in ChIP-Seq
normalization
basic tests
differential splicing
Finding peaks in ChIP-Seq

6. Technical Biases – Sequence Start
The initial bases of reads are highly biased, and the bias depends on RNA/DNA preparation

7. 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

8. Position of single mismatch in uniquely mapped tags
Courtesy Jean & Danielle Thierry-Mieg

9. Types of mismatches in uniquely mapped tags with a single mismatch are profoundly asymmetric and biased
Courtesy Jean & Danielle Thierry-Mieg

10. Technical Biases – Initiation Sites
COX1

11. 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

12. 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

13. 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

14. RNA-Seq Data Analysis

15. 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

16. Effects of Competition
(Robinson & Oshlak, Genome Biology, 2010)

17. 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

18. 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

19. 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 over-dispersion
Bioconductor package edgeR estimates parameters by Maximum Likelihood

20. 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

21. Detecting Alternate Splicing – I
(Wang et al, Nature, 2008) measured splicing index for several tissues

22. 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

23. ChIP-Seq

24. Courtesy Raphael Gottardo

25. 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

26. Always Have a Control
High correlation between peaks in control samples and peaks in ChIP sample
Must subtract estimate of background from control tags
From Zhang et al, Genome Biol 2008

27. 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