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NGS Sequence Analysis for Regulation and Epigenomics
Timothy Bailey Winter School in Mathematical
and Computational Biology July 2, 2013
NGS Analysis and Transcriptional Regulation
• RNA-seq – Measuring transcription levels (gene
expression) – Detecting RNA regulators (e.g., miRNA)
• ChIP-seq – Chromatin modifications – Binding of transcription factor proteins
Talk Overview I. Transcriptional Regulation 101 II. ChIP-seq 101 III. Analyzing ChIP-seq data IV. Combining ChIP-seq and RNA-seq
Part I: Basic Transcriptional Regulation
Source: Steven Chu
Transcription Factors • Mammalian transcription is controlled
(in part) by about 1400 DNA-binding transcription factor (TF) proteins.
• These proteins control transcription in two main ways: – Directly, by promoting (or preventing) the
assembly of the pre-initiation complex. – Indirectly, by modifying chromatin.
BASAL TRANSCRIPTION: • The pre-initiation complex assembles at the core promoter. • This results in only low levels of transcription because the interaction is unstable.
DNA
+
Core Promoter
TATA INR
DNA
Proximal Promoter
TATA INR
PROXIMAL PROMOTER: • The proximal promoter extends upstream of the promoter. • It contains binding sites for repressor and activator transcription factors.
• Some transcription factors (“activators”) stabilize the transcriptional machinery when they bind to sites in the proximal promoter.
ACTIVATORS:
• This increases transcription.
DNA
+ +
Proximal Promoter
TATA INR
• This reduces transcription.
• Their binding can block binding by co-factors and activators.
• Some factors do not stabilize the transcriptional machinery.
REPRESSORS:
+++ DNA
+ +
Proximal Promoter
TATA INR
ENHANCER REGIONS:
DNA
+ +
Proximal Promoter
TATA INR
Enhancer Region 1-‐-‐100Kb
• Often very distant—1000s of base pairs.
• Groups of binding sites located upstream or downstream of a promoter.
• Activator and repressor transcription factors compete to occupy enhancer regions. • DNA looping brings factors into contact with transcriptional machinery. • Bound activators increase transcription.
ENHANCER REGIONS:
DNA
+
Proximal Promoter
TATA INR
Enhancer Region
+++ TATA INR
+++
Chromatin modification by TFs:
DNA
+ +
Proximal Promoter
TATA INR
Enhancer Region
• Tissue-specific transcription factors can bind to HATs, causing chromatin to open. • This can increase transcription.
• Example: Histone Acetyltransferases (HATs) acetylate histones.
Specific General
HAT
Part II: ChIP-seq Overview
Source: Steven Chu
ChIP-seq • Chromatin
ImmunoPrecipitation followed by high-throughput sequencing.
• TF binding sites (“punctate peaks”)
• Chromatin mods (“broad peaks”
Steps in ChIP-seq
• Cross-link proteins to DNA
• Fragment chromatin • Immunoprecipitate
with antibody to protein
• Size-select and ligate
• Amplify • Sequence
Cross-‐link
What can I learn from ChIP-seq?
• What chromatin regions are marked as active promoters or enhancers?
• Where is my TF bound? • What is its DNA-binding
motif? • What genes might it
regulate?
Part III: Analyzing ChIP-seq Data
Source: Steven Chu
Analyzing TF ChIP-seq Data • Key messages of this talk:
– Use controls! – Validate your data at each
step. – But this is Science! What
could possibly go wrong…?
Things that can go wrong in ChIP-seq…
1. Low affinity antibody 2. Non-specific antibody 3. Contamination 4. Poor choice of peak calling algorithm (or
parameters) … etc.
Steps in ChIP-seq Data Analysis
1. Mapping: where do the sequence “tags” map to the genome?
2. Peak Calling: where are the regions of significant tag concentration?
3. Motif Discovery: what is the binding motif?
4. Location Analysis: where are the peaks w/respect to genes, promoters, introns etc?
1) Mapping ChIP-seq Tags • Tags: ChIP-seq produces a pool of
“tags” (~100bp) • Tag Count: measure of enrichment of region • Negative Control: “input DNA” tag count
Tallack et al., Genome Res., 2010
Do the mapped tags make sense? • Each ~100 bp tag is the
5’ end of a DNA fragment.
• But DNA is double-stranded so there are tags from both strands.
• We expect pairs of clusters of tags on opposite strands, separated by the fragment length.
Wilbanks and FaccioK, PLoS One, 2010
Strand Cross Correlation Analysis (SCCA)
• If we shift the anti-sense tags left by the (average) fragment length, we should see maximum correlation between the reads on the two strands.
Kharchenko et al., Nature Biotechnology, 2009
SCCA often shows two maxima • Fragment-length
peak at average fragment length (as we expected)
• Read-length peak at average read length(due to variable and dispersed mappability of genomic positions)
read-‐length peak
fragment-‐length peak
Landt S G et al. Genome Res. 2012;22:1813-‐1831
Quality control 1: SCCA identifies failed ChIP-seq
Landt S G et al. Genome Res. 2012;22:1813-‐1831
ENCODE Guidelines: • Normalized Strand Correlation,
NSC > 1.05 • Relative Strand Correlation,
RSC > 0.8 • https://code.google.com/p/
phantompeakqualtools
2) ChIP-seq Peak Calling • Peak callers combine
overlapping tags to get the “peak height”.
• Often, strand information and shifting is used to combine tags on opposite strands.
• Fold-enrichment (tag count / control tag count) is usually used as the criterion for declaring a peak.
Wilbanks and Faccio., PLoS One, 2010
Some ChIP-seq peak callers use SCCA
Bailey et. al., PLoS Comp Bio, in press.
Uses SCCA
Uses SCCA
Sanity checks: Are your peaks reasonable?
• Width: TF ChIP-seq peaks should be relatively short (< 300bp) compared to histone modification peaks. – Are your peaks too wide?
• Number: Is the number of TF ChIP-seq peaks reasonable? – Some key TFs bind ~30,000 sites but your TF
probably only binds far fewer (~1000?) • Location: Do your peaks co-occur with histone
marks and genes that your TF regulates? – Examine some peaks using the UCSC genome
browser and ENCODE histone tracks
Quality control 2: Fraction of Reads in Peaks (FRiP)
• Only a fraction of reads typically fall within ChIP-seq peaks.
• ENCODE guideline: FRiP > 1%
• Caveat: A lower FRiP threshold may be appropriate if there are very few peaks.
Landt S G et al. Genome Res. 2012;22:1813-‐1831
How many of my peaks are “real”?
• Irreproducible Discovery Rate (IDR) compares the ranks of peaks from two biological replicates. – Rank peaks by significance (p-value or q-
value) – Reproducible discoveries (peaks) should have
similar ranks between replicates. • ENCODE: reports peaks at 1% IDR • https://sites.google.com/site/
anshulkundaje/projects/idr
Quality control 3: IDR identifies failed ChIP-seq
Landt S G et al. Genome Res. 2012;22:1813-‐1831
High Reproducibility
Low Reproducibility
3) Motif Discovery & Enrichment Analysis
• If your TF binds DNA directly (and sequence-specifically), Motif Discovery should find its binding motif.
• The DNA-binding motif of your TF should be centrally enriched in the peaks, and Central Motif Enrichment Analysis (CMEA) should find it.
Caveats in ChIP-seq Motif Analysis
• Peak regions may contain other TF motifs due to looping.
• The binding of the ChIP-ed factor “X” may be indirect.
• ChIP-ed motif might be weak due to assisted binding.
Farnham, Nature Reviews Gene>cs, 2009
TF Binding Motif Discovery • ChIP-seq provides
extremely rich data for inferring the DNA-binding affinity of the ChIP-ed transcription factor.
• In principle, discovering the motif is simple. ààà • ChIP-seq peaks tend
to be within +/- 50bp of the bound factor.
• So we just examine the peak regions for enriched patterns.
MEME Suite tools for ChIP-seq motif discovery and enrichment
• The MEME Suite (http://meme.nbcr.net) contains several motif discovery and enrichment algorithms appropriate for ChIP-seq data analysis.
– Discovery & Enrichment: MEME-ChIP
– Discovery: MEME, DREME, GLAM2
– Enrichment: CentriMo, AME
Example: Motif discovery in NFIC ChIP-seq data
• Pjanic et al. predicted 39,807 ChIP-seq peaks in NFIC ChIP-seq data.
• They do not report a using motif discovery on these peaks.
• We used MEME-ChIP which runs both MEME and DREME to perform motif discovery on the 100-bp NFIC ChIP-seq peak regions.
Machanick & Bailey, Bioinforma>cs, 2011
Motif discovery fails in the (original) NFIC dataset
• An NFIC motif is known from in vitro data, based on only 16 sites.
• MEME and DREME fail to find this motif in the NFIC data.
• But so do the other algorithms we tried: Amadeus, peak-motifs, Trawler and Weeder.
The problem: poor peak calling! • We applied a
different ChIP-seq peak calling algorithm (ChIP-peak) which predicts only 700 peaks (rather than 40,000).
• MEME discovers the NFI-family binding motif in this new set of peaks.
“site-‐probability” curve MA0119.1
Position CEQLOGO 22.09.10 17:31
TGGCCTAAGCATGCTGACATGCCAGTA
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
0.004
0.0045
-250 -200 -150 -100 -50 0 50 100 150 200 250
pro
ba
bility
position of best site in sequence
ATGGCG p=9.9e-081,w=89,n=3495CRSAGC p=9.2e-040,w=207,n=15406
CHGSAGC p=2.3e-038,w=138,n=11787MTGCGCA p=1.4e-033,w=250,n=2184
CDCCKCC p=2.6e-028,w=266,n=11544
PosiKon of Best Site
Prob
ability
Central Motif Enrichment Analysis: CentriMo
• CentriMo searches for known motifs whose sites are most centrally enriched in the ChIP-seq regions.
• Use 500bp regions centered on each ChIP-seq peak.
500-‐bp ChIP-‐seq regions
W=120 L=500
S = number of “successes” = 4 T = number of “trials” = 5
Bailey et al, NAR, 2012
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
-250 -200 -150 -100 -50 0 50 100 150 200 250
pro
babili
ty
position of best site in sequence
MA0119.1 p=2.4e-031,w=295,n=5409MA0244.1 p=4.6e-015,w=381,n=39398MA0161.1 p=7.3e-015,w=329,n=39356MA0099.1 p=5.5e-014,w=343,n=34267MA0406.1 p=8.1e-012,w=323,n=31383
Central Motif Enrichment confirms the known NFIC motif—even in the original peaks
• NFIC motif is most centrally enriched of 862 JASPAR and UniPROBE motifs (p = 10-31).
MA0119.1
Position CEQLOGO 22.09.10 17:31
TGGCCTAAGCATGCTGACATGCCAGTA NFIC
• However, standard motif enrichment algorithms do not show the NFIC as the most enriched motif.
Quality control 4: CMEA identifies failed ChIP-seq
0
0.0005
0.001
0.0015
0.002
0.0025
-250 -200 -150 -100 -50 0 50 100 150 200 250
pro
babili
ty
position of best site in sequence
MA0039.2 p=7.2e-001,w=365,n=11404
MA0039.2
Position CEQLOGO 10.10.11 18:17
T
C
AGT
G
A
CA
T
CACCT
GACC
T
CC
TA
p = 0.7
2. Failed KLF1 ChIP-‐seq
KLF4
Pilon et al., Blood, 2011
-0.002
-0.001
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
-250 -200 -150 -100 -50 0 50 100 150 200 250
pro
babili
ty
position of best site in sequence
MA0039.2 p=4.4e-066,w=111,n=712Klf7_primary p=6.9e-056,w=103,n=676
MA0140.1 p=1.5e-048,w=177,n=693MA0035.2 p=2.4e-040,w=194,n=756
1. Successful KLF1 ChIP-‐seq
MA0039.2
Position CEQLOGO 10.10.11 18:17
T
C
AGT
G
A
CA
T
CACCT
GACC
T
CC
TA
KLF4
Tallack et al., Genome Res, 2010
New motif databases
• In vitro motifs are especially useful for verifying that your ChIP-seq worked.
• They are independent of the motifs found by motif discovery in your ChIP-seq data. – UniPROBE: 386 mouse TF motifs from
protein-binding microarrays. – Jolma et al., Cell, 2013: 738 human and
mouse TF motifs from SELEX
4) Location Analysis • Counts how often TF binding sites are in, say,
promoters, intergenic or intragenic regions.
Farnham, Nature Reviews Gene>cs, 2009
Example: Predicting Target Genes • TF binding sites in promoters probably are
regulatory.
• “Nearest TSS” rule is often used to assign binding sites to target genes.
• But distal sites may regulate some other gene via chromatin looping.
Farnham, Nature Reviews Gene:cs, 2009
Klf1 binding near TSSs • Histogram of
distances from Klf1 ChIP-seq peak to the nearest TSS.
• KLF1 has a population of binding sites in promoters (small hump on left), but most are distal.
Tallack et al, Genome Res, 2010
Motif Spacing Analysis finds co-factor motifs and TF complexes
Part IV: Combining ChIP-seq and RNA-seq
Source: Steven Chu
Identification of KLF1 target genes using RNA-seq
3 x Klf1-/- libraries
3 x Klf1+/+ libraries CuffDiff
RefSeq.gtf (gene definition set)
690 KLF1 “Activated” genes
118 KLF1 “Repressed” genes At Bonferroni corrected p-val <0.05 and >1.5 fold change (KO vs WT)
E2f2 E2f40
200
400
600
800
1000
mR
NA
-seq
FPK
M
mRNA-seq
**
qRT-PCR
valida.on
Tallack et al, Genome Res, 2012
The KLF1 Transcriptome
Tallack et al, Genome Res, 2012
KLF1 is a (direct) Activator The distance from KLF1 ChIP-seq peaks to the nearest TSS (putative target gene) is less for “Activated” genes than for “Repressed” genes.
Tallack et al, Genome Res, 2012
Final reminders • Check your data at each step!
– Read mapping • Strand Cross Correlation Analysis (SCCA)
– Peak calling • Fraction of Reads in Peaks (FRiP) • Irreproducible Discovery Rate (IDR) analysis
– Motif discovery / enrichment analysis • De novo motif found? • In vitro motif centrally enriched?
Acknowledgements The MEME Suite • Tom Whitington • Philip Machanick • James Johnson • Martin Frith • William Noble • Charles Grant • Shobhit Gupta
KLF Project • Michael Tallack • Tom Whitington • Andrew Perkins • Sean Grimmond • Brooke Gardiner • Ehsan Nourbakhsh • Nicole Cloonan • Elanor Wainwright • Janelle Keys • Wai Shan Yuen