Lecture 8 Understanding Transcription RNA-seq analysis ... · Understanding Transcription RNA-seq...
Transcript of Lecture 8 Understanding Transcription RNA-seq analysis ... · Understanding Transcription RNA-seq...
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Lecture 8 Understanding Transcription
RNA-seq analysis
Foundations of Computational Systems Biology David K. Gifford
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Lecture 8 – RNA-seq Analysis
• RNA-seq principles – How can we characterize mRNA isoform
expression using high-throughput sequencing?
• Differential expression and PCA – What genes are differentially expressed, and how
can we characterize expressed genes?
• Single cell RNA-seq – What are the benefits and challenges of working
with single cells for RNA-seq?
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RNA-Seq characterizes RNA molecules
A B C Gene in genome
A B C pre-mRNA or ncRNA
transcription
A B C
splicing
A C
export to cytoplasm
mRNA
nucleus
cytoplasm
High-throughput sequencing of RNAs at various stages of processing
Slide courtesy Cole Trapnel Courtesy of Cole Trapnell. Used with permission.
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ET Wang et al. Nature 000, 1-7 (2008) doi:10.1038/nature07509
Pervasive tissue-specific regulation of alternative mRNA isoforms.
Courtesy of Macmillan Publishers Limited. Used with permission.Source: Wang, Eric T., Rickard Sandberg, et al. "Alternative Isoform Regulation inHuman Tissue Transcriptomes." Nature 456, no. 7221 (2008): 470-6.4
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RNA-Seq: millions of short reads from fragmented mRNA
Pepke et. al. Nature Methods 2009
Extract RNA from cells/tissue
+ splice junctions!
Courtesy of Macmillan Publishers Limited. Used with permission.Source: Pepke, Shirley, Barbara Wold, et al. "Computation for ChIP-seq and RNA-seq Studies." Nature Methods 6 (2009): S22-32.
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Mapping RNA-seq reads to a reference genome reveals expression
Sox2
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Smug1
Reads over exons
Junction reads (split between exons)
RNA-seq reads map to exons and across exons
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Two major approaches to RNA-seq analysis
1. Assemble reads into transcripts. Typical issues with coverage and correctness.
2. Map reads to reference genome and identify isoforms using constraints
• Goal is to quantify isoforms and determine significance of differential expression
• Common RNA-seq expression metrics are Reads per killobase per million reads (RPKM) or Fragments per killobase per million (FPKM)
exon 1 exon 2 exon 3
Short sequencing reads, randomly sampled from a
transcript
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Aligned reads reveal isoform possibilities
A B C
A B C A B C
A B C A B C
identify candidate exons via genomic mapping
Generate possible pairings of exons
Align reads to possible junctions
Courtesy of Cole Trapnell. Used with permission.
Slide courtesy Cole Trapnell
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A
B
C
D
E Isoform FractioT1 ψψ11""
T2 ψψ22""T3 ψψ33""
T4 ψψ44""
n
We can use mapped reads to learn the isoform mixture ψ"
Slide courtesy Cole Trapnell
Courtesy of Cole Trapnell. Used with permission.
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Common reads Common reads
Detecting alternative splicing from mRNA-Seq data Isoforms Inclusion reads
Exclusion reads Given a set of reads, estimate:
= Distribution of isoforms
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P(Ri | T=Tj) – Excluded reads
If a single ended read or read pair Ri is structurally incompatible with transcript Tj, then
Ri
Tj
P(R = Ri |T = Tj ) = 0
Intron in Tj
Courtesy of Cole Trapnell. Used with permission.
Slide courtesy Cole Trapnell
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P(Ri | T=Tj) – Single end reads
Ri
Tj
Cufflinks assumes that fragmentation is roughly uniform. The probability of observing a fragment starting at a specific position Si in a transcript of length lj is:""
P(S = Si |T = Tj ) =1l j
Transcript length lj
starting position in transcript, Si
Courtesy of Cole Trapnell. Used with permission.
Slide courtesy Cole Trapnell
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P(Ri | T=Tj) – Paired end reads
Assume our library fragments have a length distribution described by a probability density F.. Thus, the probability of observing a particular paired alignment to a transcript:""
Tj
Implied fragment length lj(Ri)
Ri
P(R = Ri |T = Tj ) =F(l j (Rj ))
l j
Courtesy of Cole Trapnell. Used with permission.
Slide courtesy Cole Trapnell
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Estimating Isoform Expression
• Find expression abundances ψ1,…,ψn for a set of isoforms T1,…,Tn
• Observations are the set of reads R1,…,Rm
• Can estimate mRNA expression of each isoform
Ψ =Ψ
argmaxL(Ψ | R)
L(Ψ | R)∝P(R |Ψ)P(Ψ)
using total number of reads that map to a gene and ψ
P(R |Ψ) = Ψ jP(R = Ri |T = Tj )j=0
n∑
i=0
m∏
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Case study: myogenesis
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Courtesy of Cole Trapnell. Used with permission.
Cufflinks identified 116,839 distinct transcribed fragments (transfrags)
Nearly 70% of the reads in 14,241 matching transcripts
Tracked 8,134 transfrags across all time points, 5,845 complete matches to UCSC/Ensembl/VEGA
Tracked 643 new isoforms of known genes across all points
Transcript categories, by coverage
Slide courtesy Cole Trapnell
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Case study: myogenesis
• ~25% of transcripts have light sequence coverage, and are fragments of full transcripts
• Intronic reads, repeats, and other artifacts are numerous, but account for less than 5% of the assembled reads.
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novel isoformrepeatother
Reads per bp
Tra
nscrip
ts
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Transcript categories, by coverage
Courtesy of Cole Trapnell. Used with permission.
Slide courtesy Cole Trapnell
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Lecture 8 – RNA-seq Analysis
• RNA-seq principles – How can we characterize mRNA isoform
expression using high-throughput sequencing?
• Differential expression and PCA – What genes are differentially expressed, and how
can we characterize expressed genes?
• Single cell RNA-seq – What are the benefits and challenges of working
with single cells for RNA-seq?
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Scaling RNA-seq data (DESeq)
• i gene or isoform • j sample (experiment) • m number of samples • Kij number of counts for isoform i in experiment j • sj sampling depth for experiment j (scale factor)
js =mediani
ijK1m
ivKv=1m∏( )
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Model for RNA-seq data (DESeq)
• i gene or isoform p condition • j sample (experiment) p(j) condition of sample j • m number of samples • Kij number of counts for isoform i in experiment j • qip Average scaled expression for gene i condition p
ijK ~ NB ijµ , ij2σ( )
ij2σ = ijµ + j
2s pv ip( j )q( )ijµ = ip( j )q js
ipq =1
# of replicatesijKjsj in replicates
∑
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2 2σ ij = µ ij + s jvp(qip( j ))
Orange Line – DESeq Dashed Orange – edgeR Purple - Poission
Courtesy of the authors. License: CC-BY.Source: Anders, Simon, and Wolfgang Huber. "Differential Expression Analysis for Sequence Count Data." Genome Biology 11, no. 10 (2010): R106.
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Significance of differential expression using test statistics
• Hypothesis H0 (null) – Condition A and B identically express isoform i with random noise added
• Hypothesis H1 – Condition A and B differentially express isoform
• Degrees of freedom (dof) is the number of free parameters in H1 minus the number of free parameters in H0; in this case degrees of freedom is 4 – 2 = 2 (H1 has an extra mean and variance).
• Likelihood ratio test defines a test statistic that follows the Chi Squared distribution
iT = 2 log P( iAK |H1)P( iBK |H1)P iAK , iBK |H0( )
P (H0) ≈1−ChiSquaredCDF (T i | dof )27
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Courtesy of the authors. License: CC-BY.Source: Anders, Simon, and Wolfgang Huber. "Differential Expression Analysis for Sequence Count Data." Genome Biology 11, no. 10 (2010): R106.
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Hypergeometric test for overlap significance
N – total # of genes 1000 n1 - # of genes in set A 20 n2 - # of genes in set B 30 k - # of genes in both A and B 3
P k( ) =
n1k
!
"#
$
%& N − n1
n2− k
!
"#
$
%&
Nn2
!
"#
$
%&
P x ≥ k( ) = P(i)i=k
min(n1,n2)∑
0.017 0.020
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Lecture 8 – RNA-seq Analysis
• RNA-seq principles – How can we characterize mRNA isoform
expression using high-throughput sequencing?
• Differential expression and PCA – What genes are differentially expressed, and how
can we characterize expressed genes?
• Single cell RNA-seq – What are the benefits and challenges of working
with single cells for RNA-seq?
36
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Courtesy of Fluidigm Corporation. Used with permission.
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Single-cell RNA-Seq of LPS-stimulated bone-marrow-derived dendritic cells reveals extensive transcriptome heterogeneity.
AK Shalek et al. Nature 000, 1-5 (2012) doi:10.1038/nature12172
Courtesy of Macmillan Publishers Limited. Used with permission.Source: Shalek, Alex K., Rahul Satija, et al. "Single-cell Transcriptomics Reveals Bimodality in Expression and Splicing in Immune Cells." Nature (2013).38
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Analysis of co-variation in single-cell mRNA expression levels reveals distinct maturity states and an antiviral cell circuit.
AK Shalek et al. Nature 000, 1-5 (2012) doi:10.1038/nature12172
Courtesy of Macmillan Publishers Limited. Used with permission.Source: Shalek, Alex K., Rahul Satija, et al. "Single-cell Transcriptomics Reveals Bimodality in Expression and Splicing in Immune Cells." Nature (2013).39
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Analysis of co-variation in single-cell mRNA expression levels reveals distinct maturity states and an antiviral cell circuit.
AK Shalek et al. Nature 000, 1-5 (2012) doi:10.1038/nature12172
Courtesy of Macmillan Publishers Limited. Used with permission.Source: Shalek, Alex K., Rahul Satija, et al. "Single-cell Transcriptomics Reveals Bimodality in Expression and Splicing in Immune Cells." Nature (2013).40
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RNA-seq library complexity can help qualify cells for analysis
Michal Grzadkowski © Michal Grzadkowski. All rights reserved. This content is excluded from our Creative Commons license. For more information, see http://ocw.mit.edu/help/faq-fair-use/.
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RNA-seq library complexity can help qualify cells for analysis
Michal Grzadkowski © Michal Grzadkowski. All rights reserved. This content is excluded from our Creative Commons license. For more information, see http://ocw.mit.edu/help/faq-fair-use/.
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FIN
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MIT OpenCourseWarehttp://ocw.mit.edu
7.91J / 20.490J / 20.390J / 7.36J / 6.802J / 6.874J / HST.506J Foundations of Computational and Systems BiologySpring 2014
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