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Previous Lecture: NGS Alignment
NGS Alignment
Spring CHIBI Courses
• BMI Foundations I: Bioinformatics (BMSC-GA 4456)
• Constantin Aliferis
– Study classic Bioinformatics/Genomics papers and reproduce data analysis, for advanced
informatics students
• Integrative Genomic Data Analysis (BMSC-GA 4453)
• Jinhua Wang
– build competence in quantitative methods for the analysis of high-throughput genomic data
•
Microbiomics Informatics (BMSC-GA 4440)
• Alexander Alekseyenko
– analysis of microbial community data generated by sequencing technologies: preprocess raw
sequencing data into abundance tables, associate abundance with clinical phenotype and
outcomes.
•
Next Generation Sequencing (BMSC-GA 4452)
• Stuart Brown
– An overview of Next-Generation sequencing informatics methods for data pre-processing,
alignment, variant detection, structural variation, ChIP-seq, RNA-seq, and metagenomics.
•
Proteomics Informatics (BMSC-GA 4437)
• David Fenyo
– A practical introduction of proteomics and mass spectrometry workflows, experimental
design, and data analysis
Immunoprecipitate
This Lecture
High‐throughput sequencing
ChIP-seq & RNA-seq
Map sequence tags
to genome
Release DNA
Learning Objectives
• ChIP-seq experimental methods
• Transcription factors and epigenetics
• Alignment and data processing
• Finding peaks: MACS algorithm
• Annotation
• RNA-seq experimental methods
• Alignment challenges (splice sites)
• TopHat
• Counting reads per gene
• Normalization
• HTSeq-count and Cufflinks
• Statistics of differential expression for RNA-seq
ChIP-seq
• Combine sequencing with
Chromatin‐Immunoprecipitaion
• Select (and identify) fragments of DNA that interact
with specific proteins such as:
–
–
–
–
Transcription factors
Modified histones
Methylation
RNA Polymerase (survey actively transcribe portions of the
genome)
– DNA polymerase (investigate DNA replication)
– DNA repair enzmes
ChIP-chip
• [Pre-sequencing technology]
• Do chromatin IP with YFA (Your Favorite Antibody)
• Take IP-purified DNA fragments, label & hybridize to a
microarray containing (putative) promoter (or TF binding)
sequences from lots of genes
• Estimate binding, relate to DNA binding of protein
targeted by antibody
–
–
–
–
limited to well annotated genomes
need to build special microarrays
suffers from hybridization bias
assumes all TF binding sites are known and correctly located on
genome
ChIP-seq
Immunoprecipitate
High-throughput sequencing
Map sequence tags
to genome
Release DNA
Alignment
– Place millions of short read sequence ‘tags’
(25-50 bp) on the genome
– Finds perfect, 1, and 2 mismatch alignments;
no indels (BWA)
– Aligns ~80% of PF tags to human/mouse
genome
– We parse alignment files to get only unique
alignments (removes 2%-5% of ‘multi-mapped’
reads)
ChIP-seq for
TF
(SISSRS software)
Jothi, et al. Genome-wide
identification of in vivo protein–
DNA binding sites from ChIP-Seq
data. NAR (2008), 36: 5221-31
Saturation
• How many sequence reads are needed to
find all of the binding targets in the genome?
• Look for plateau
120
100% = 15,291 peaks
100
80
60
40
Rozowsky, et al. Nature Biotech. Vol
27-1, Jan 2009.
20
0
20
30
40
50
60
70
80
90
Pol2 data: 11M reads vs. 12M control reads, peaks
found with MACS, data sub-sampled.
ChIP-seq Challenges
• We want to find the peaks (enriched regions =
protein binding sites on genome)
• Goals include: accuracy (location of peak on
genome), sensitivity, & reproducibility
• Challenges: non-random background, PCR
artifacts, difficult to estimate false negatives
• Very difficult to compare samples to find
changes in TF binding (many borderline peaks)
Peakfinding
• Find enriched regions on the genome
(high tag density) = “peaks”
– Enriched vs. what?
• A statistical approach assumes an evenly
distributed or randomly distributed background
– Poisson distribution of background is
obviously not true
– Any threshold is essentially arbitrary
Compare to Background
• Goal is to make ‘fold change’
measurements
• What is the appropriate background?
– Input DNA (no IP)
– IP with non-specific antibody (IgG)
[We mostly use input DNA]
• Must first identify “peak region” in
sample, then compare tag counts vs BG
MACS
• Zhang et al. Model-based Analysis of ChIP-Seq (MACS).
Genome Biol (2008) vol. 9 (9) pp. R137
• Open source Unix software (Python !)
– MACS improves the spatial resolution of binding sites through
combining the information of sequencing tag position and
orientation by using empirical models for the length of the
sequenced ChIP fragments
• (slides + and – strand reads toward center of fragment)
– MACS uses a dynamic Poisson distribution (local background
count in the control) to effectively capture local biases in the
genome sequence, allowing for more sensitive and robust
prediction
– Uses control to calculate “random” peaks, sets FDR rate.
Feng J, Liu T, Zhang Y. Using MACS to identify peaks from ChIP-Seq
data. Curr Protoc Bioinformatics. 2011 Jun;Chapter 2:Unit 2.14.
BED format
• BED format defines a genomic interval as positions on a reference
genome.
• An interval can be a anything with a location: gene, exon, binding site,
region of low complexity, etc.
• MACS outputs ChIP-seq peaks in BED format
• BED files can also specify color, width, some other formatting.
chromosome start
chr1
chr1
chr1
chr2
chr2
chr3
chr3
chr3
213941196
213942363
213943530
158364697
158365864
127477031
127478198
127479365
end
213942363
213943530
213944697
158365864
158367031
127478198
127479365
127480532
track name="ItemRGBDemo" description="Item RGB demonstration" itemRgb="On"
chr7 127471196 127472363 Pos1 0 + 127471196 127472363 255,0,0
chr7 127472363 127473530 Pos2 0 + 127472363 127473530 255,0,0
chr7 127473530 127474697 Pos3 0 + 127473530 127474697 255,0,0
chr7 127474697 127475864 Pos4 0 + 127474697 127475864 255,0,0
chr7 127475864 127477031 Neg1 0 - 127475864 127477031 0,0,255
chr7 127477031 127478198 Neg2 0 - 127477031 127478198 0,0,255
chr7 127478198 127479365 Neg3 0 - 127478198 127479365 0,0,255
chr7 127479365 127480532 Pos5 0 + 127479365 127480532 255,0,0
Remove Duplicates
• In some ChIP-seq samples, PCR
amplification of IP-enriched DNA
creates artifacts (highly duplicated
fragments)
• Huge differences depending on target of
antibody and amount of IP DNA
collected.
• “Complexity” of the library
PCR ‘stacks’
• Always in F-R pairs, ~200 bp apart
% of Duplicates varies
Different IP Targets
• Huge difference between Transcription Factors
and Histone modification as targets of IP
TF
• sequence-specific
binding motifs
• few thousand sites
• binding region ~50bp
• oriented tags
• yes/no binding
• promoters or
enhancers
Histone Mods
• not sequence-specific
• tens to hundreds of
thousands of sites
• large binding region
(~2kb)
• tags not oriented
• signal may be scaled
• associated w/ almost
all transcribed genes
Mikkelsen, Lander, et al. Genome-wide maps of chromatin
state in pluripotent and lineage-committed cells. Nature
(2007) 448: 553-562.
H3K4me3
Normalization
• How to compare lanes with different
numbers of reads?
• Will bias fold-change calculations
• Simple method – set all counts in ‘peak’
regions as per million reads
– This does not work well for >2x differences in
read counts.
Evaluation
• Peaks near promoters of known genes
(TSS)
• Generally a high %
• As parameters become less stringent, more
peaks are found, % near TSS declines
• Estimate false positive rate
•
•
•
•
Pure statistical (Poisson or Monte Carlo)
Compare 2 bg sampels (QuEST)
Reverse sample & bg (MACS)
Can’t estimate false negative rate – don’t
know ‘true’ number of binding sites
Evaluation
• Overlap with ChIP-chip data
• What is an overlap?
• What % overlap is good?
• Reproducibility
•
•
•
•
•
Need to define (we use overlap of 1 bp)
Very important for biological conclusions
Essential for comparisons of diff. conditions
Must have replicate samples!!
Trade off: reproducibility vs. sensitivity
• Synthetic data
• Allows calculation of sensitivity & specificity
• How similar to real data? (All synth has bias)
Histone modification (H3K4) ChIP-seq
Composite image of sequence reads at promoters of all
RefSeq genes.
The Use of Next Generation Sequencing
to Study Transcriptomes:
RNA-seq
RNA-seq Measures the Transcriptome
• Takes advantage of the rapidly dropping cost of
Next-Generation DNA sequencing
• Measures gene expression in true genome-wide
fashion (all the RNA)
• Also enables detection of mutations (SNPs),
alternative splicing, allele specific expression, and
fusion genes
• More accurate and better dynamic range than
Microarray
• Can be used to detect miRNA, ncRNA, and other
non-coding RNA
RNA-seq Measures the Transcriptome
• Takes advantage of the rapidly dropping cost of
Next-Generation DNA sequencing
• Measures gene expression in true genome-wide
fashion (all the RNA)
• Also enables detection of mutations (SNPs),
alternative splicing, allele specific expression, and
fusion genes
• More accurate and better dynamic range than
Microarray
• Can be used to detect miRNA, ncRNA, and other
non-coding RNA
NA sequencing is superior to other gene
g methods
RNA-seq vs. qPCR
ge
Accuracy and
Sensitivity
Depth of Coverage
• With the Illumina HiSeq producing >200
million reads per sample, what depth of
coverage is needed for RNA-seq?
• Can we multiplex several samples per lane and
save $$ on sequencing?
• For expression profiling (and detection of
differentially expressed genes), probably yes,
2-4 samples per lane is practical
100 million reads, 81% of genes FPKM ≥ 0.05
Each additional 100 million reads detects ~3% more genes
Toung, et al. Genome Res. 2011 June; 21(6): 991–998..
Illumina mRNA Sequencing
RNA-Seq: Method
Random primer PCR
AAAA AAAAAAA AAAAAA
Poly-A selection
Fragment &
size-select
cDNAsample
Relative # of reads
Illumina Sequencing
Genome position
Sample prep can create 3’ or 5’ bias
no bias
5’ bias
(strand oriented protocol)
RNA−Seq Coverage vs. Transcript Position
All_Reads
in file 1365−PM−36−accepted_hits.reorder.pcsort.bam
RNA−Seq Coverage vs. Transcript Position
All_Reads
in file 1365−PM−33−accepted_hits.reorder.pcsort.bam
1.0
0.5
Normalized Coverage
0.8
0.6
Normalized Coverage
0.0
0.0
0.2
0.4
1.0
0.5
0.0
Normalized Coverage
1.0
1.2
1.5
RNA−Seq Coverage vs. Transcript Position
All_Reads
in file T3.rnaseq.reorder.rg.pcsort.bam
1.5
3’ bias
(poly-A selection)
(low coverage at ends
of transcript)
0
20
40
60
Normalized Distance Along Transcript
80
100
0
20
40
60
Normalized Distance Along Transcript
80
100
0
20
40
60
Normalized Distance Along Transcript
80
100
Detect Small RNAs
Many ncRNAs are often transcribed from the same strand as mRNA
– depends on sample prep method
Poly-A + DSN
Total RNA + DSN
RNA-seq informatics workflow:
•
•
•
•
•
•
•
genome mapping
splice junction fragments
(predict novel junctions/exons)
counts
normalize
differential expression
gene lists
Oshlack et al. Genome Biology 2010, 11:220
RNA-seq Alignment Challenges
• Using RNA-seq for gene expression requires
counting sequence reads per gene
• Must map reads to genes – but this is a more
difficult problem than mapping reads to a
reference genome
• Introns create big gaps in alignment
• Small reads mean many short overlaps at one end or
the other of intron gaps
• What to do with reads that map to introns or outside
exon boundaries?
• What about overlapping genes?
TopHat
RNA-seq can be
used to directly
detect alternatively
spliced mRNAs.
Map reads to exons & junctions
TopHat
The seed and extend alignment used to match reads to possible splice sites.
Trapnell C et al. Bioinformatics 2009;25:1105-1111
Real data generally support existing annotation
Data from Costa lab
RNA-seq informatics
• Filter out rRNA, tRNA, mitoRNA
• Align to genome
• Find splice junction fragments (join exon
boundaries)
• Differential expression
• Alternatively spliced transcripts
• Novel genes/exons
• Sequence variants (SNPs, indels,
translocations)
• Allele-specific expression
Count Reads per gene
• Need a reference genome with exon
information
• How to count partial alignments, novel splices
etc?
• Simple or complex model?
– Simple: HTSeq-count
– Complex: Cufflinks
• Normalization methods affect the count very
dramatically
HTSeq-count
A simple Python tool.
Relies entirely on an accurate annotation of genes and exons in GFF file.
Cufflinks Isoform Models
Differential Expression
ADM
Data from Costa Lab
Normalization
• Differential Expression (DE) requires comparison
of 2 or more RNA-seq samples.
• Number of reads (coverage) will not be exactly
the same for each sample
• Problem: Need to scale RNA counts per gene to
total sample coverage
• Solution – divide counts per million reads
• Problem: Longer genes have more reads, gives
better chance to detect DE
• Solution – divide counts by gene length
• Result = RPKM
(Reads Per KB per Million)
Better Normalization
• RPKM assumes:
• Total amount of RNA per cell is constant
• Most genes do not change expression
• RPKM is invalid if there are a few very highly
expressed genes that have dramatic change in
expression (dominate the pool of reads)
• Better to use “Upper Quartile” (75th percentile) or
“Quantile” normalization
• Different normalization methods give different
results (different DE genes & different p-value
rankings)
Statistics of DE
• mRNA levels are variable in cells/tissues/organisms
over time/treatment/tissue etc.
• Like microarrays, need replicates to separate
biological variability from experimental variability
• If there is high experimental variability, then
variance within replicates will be high, statistical
significance for DE will be difficult to find.
• Best methods to discover DE are coupled with
sophisticated approaches to normalization
• Best to ignore very low expressing genes: RPKM<1
Popular DE Statistical methods
• Cufflinks-Cuffdiff
• part of TopHat software suite – easy to use
• Uses FPKM normalization
• complex model for counting reads among splice variants
– can be set to ignore novel variants
• Estimates variance in log fold change for each gene using permutations
• finds the most DE genes, high false positive rate
• edgeR
• requires raw count data, does its own normalization
• Estimates standard deviation (dispersion) with a weighted combination of
individual gene (gene-wise) and global measures
• Statistical model is Negative Binomial distribution (has a dispersion parameter)
• Fisher’s Exact test (for 2-sample), or generalized linear model (complex design)
• acceptable tradeoff of sensitivity and specificity
• Many others: DESeq, SAMseq, baySeq.
• Many rather inconclusive benchmarking studies
DE genes by different methods
Differentially expressed genes
70% of DE genes validated by qPCR
Data from Meruelo Lab
Alternative Splicing
Data from Costa Lab
Good SNP
data from Zavadil lab
Novel Genomes
• RNA-seq can be used to annotate genomes –
gene discovery, exon mapping.
data from Desplan lab
Summary
• ChIP-seq experimental methods
• Transcription factors and epigenetics
• Alignment and data processing
• Finding peaks: MACS algorithm
• Annotation
• RNA-seq experimental methods
• Alignment challenges (splice sites)
• TopHat
• Counting reads per gene
• Normalization
• HTSeq-count and Cufflinks
• Statistics of differential expression for RNA-seq
Next Lecture: Signal Processing