RNA-seq: Quantifying the
Transcriptome
Alisha Holloway, PhD
Gladstone Bioinformatics Core Director
What is RNA-seq?
Use of high-throughput sequencing technologies
to assess the RNA content of a sample.
Why do an RNA-seq experiment?
• Detect differential expression
• Assess allele-specific expression
• Quantify alternative transcript
usage
• Discover novel genes/transcripts,
gene fusions
• Profile transcriptome
• Ribosome profiling to measure
translation
Why do an RNA-seq experiment?
• Detect differential expression
• Assess allele-specific expression
• Quantify alternative transcript
usage
• Discover novel genes/transcripts,
gene fusions
• Profile transcriptome
• Ribosome profiling to measure
translation
Skelly et al. 2011
Why do an RNA-seq experiment?
• Detect differential expression
• Assess allele-specific expression
• Quantify alternative transcript
usage
• Discover novel genes/transcripts,
gene fusions
• Profile transcriptome
• Ribosome profiling to measure
translation
Why do an RNA-seq experiment?
• Detect differential expression
• Assess allele-specific expression
• Quantify alternative transcript
usage
• Discover novel genes/transcripts,
gene fusions
• Profile transcriptome
• Ribosome profiling to measure
translation
Why do an RNA-seq experiment?
• Detect differential expression
• Assess allele-specific expression
• Quantify alternative transcript
usage
• Discover novel genes/transcripts,
gene fusions
• Profile transcriptome
• Ribosome profiling to measure
translation
Pluripotent
Stem Cell
Cardiogenic Cardiac
Cardiomyocytes
Mesoderm
Precursors
Why do an RNA-seq experiment?
• Detect differential expression
• Assess allele-specific expression
• Quantify alternative transcript
usage
• Discover novel genes/transcripts,
gene fusions
• Profile transcriptome
• Ribosome profiling to measure
translation
More later!
Ingolia et al. 2009, Weissman Lab
Choose the right technology
RNA-seq
Microarray
ID novel genes, transcripts, &
exons
Well vetted QC and analysis
methods
Greater dynamic range
Less bias due to genetic
variation
Repeatable
Well characterized biases
Quick turnaround from
established core facilities
Currently less expensive
No species-specific
primer/probe design
More accurate relative to qPCR
Many more applications
RNA-seq dynamic range and accuracy
RNA-seq vs. Affy
Marioni et al. 2008
RNA-seq vs. Taqman qPCR
© 2010 NuGen
Which sequencing technology to use?
Illumina
Pac-Bio
Read length
100 bp paired end
2500 bp avg
Throughput
200 million read
pairs/lane
<1%
1 million reads/
SMRT cell
15% total, most are
indels, 4% SNP
$7-8k/sample
Error rate
Cost
Accessibility
Uses
$900/sample
USCF, UC-Davis, BGI; UC-Davis; no
many kits available kits/protocols
available
DE, ASE, Alternative Characterize
splicing
transcriptome
When to use Pac-Bio
The wet lab side…briefly
New methods to keep info on strand of template too.
Plan it well.
• Experimental design
– Biological replicates
– Reference genome?
– Good gene annotation?
•
•
•
•
Read depth
Barcoding
Read length
Paired vs. single-end
Biological
variation
Technical
variation
Plan it well.
• Experimental design
– Biological replicates
– Reference genome?
– Good gene annotation?
•
•
•
•
Read depth
Barcode/Index
Read length
Paired vs. single-end
Plan it well.
• Experimental design
– Biological replicates
– Reference genome?
– Good gene annotation?
•
•
•
•
Read depth
Barcode/Index
Read length
Paired vs. single-end
How much data do we need?
• ~15-20K genes expressed in a tissue | cell line.
• Genes are on average 3KB
• For 1x coverage using 100 bp reads, would need
600K sequence reads
• In reality, we need MUCH higher coverage to
accurately estimate gene expression levels.
• 30-50 million reads
Plan it well.
• Experimental design
– Biological replicates
– Reference genome?
– Good gene annotation?
•
•
•
•
Read depth
Barcode/Index
Read length
Paired vs. single-end
180-200 million reads / lane
Run 3-4 samples / lane
Plan it well.
• Experimental design
– Biological replicates
– Reference genome?
– Good gene annotation?
•
•
•
•
Read depth
Barcoding
Read length
Paired vs. single-end
Uniq seq = 4read length
Read length
Unique seq
25
1.1x1015
50
1.3x1030
100
1.6x1060
~60 million coding bases
in vertebrate genome
Plan it well.
• Experimental design
– Biological replicates
– Reference genome?
– Good gene annotation?
•
•
•
•
Read depth
Barcoding
Read length
Paired vs. single-end
Power of paired-end reads
• Huge impact on read mapping
– Pairs give two locations to determine whether
read is unique
• Critical for estimating transcript-level
abundance
– Increases number of splice junction spanning
reads
Distribution of cDNA fragment sizes
200 bp
100 bp
• Size select 200-300 bp
cDNA fragments
• Reads are usually 50 bp
• Inner mate distance
would be 100-200 bp on
average.
Ends map to different locations
Paired end sequencing allows you to reliably call and remove duplicates.
How do you make sense of this pile of
data?
• QC
• Alignment
• Analysis
– Stats
– Hypothesis tests
– Combine with other data
• Visualization
QC
• FastQC
– Before alignment
– http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
• RNA-SeQC
– After alignment
– https://confluence.broadinstitute.org/display/CGATools/RNA-SeQC
• Proportion of reads that mapped uniquely
– Remove duplicates; likely due to PCR amp.
• Assess ribosomal RNA and possible contaminant
content
– human RNA (if not human samples)
– Mycoplasma (if cell lines)
Base quality over reads
High quality
Red: high density
Blue: low density
Low quality
Position along read
Mapped reads summary
Sample Name
Total Records
Optical duplicates
PCR duplicates
% duplication
Unmapped reads
Uniquely mapping reads
Read-1 uniquely mapping
Read-2 uniquely mapping
Concordant reads pairs mapped
Reads map to “+”
Reads map to “-”
Non-splice reads
Splice reads
Annotation of reads
Distribution of reads over gene body
Normalized by gene length
Then what?
• Align reads to the genome
– Easy(ish) for genomic sequence
– Difficult for transcripts with splice junctions
Alignment Algorithms
• Bowtie (Langmead et al 2009) – BWT, multiseed heuristic alignment
• BWA (Li and Durbin 2009) – BWT, Smith-Waterman alignment
• SOAP2 (Li et al. 2009) – BWT, proprietary alignment algorithm
• BFAST (Homer at al. 2009, based on BLAT) – multiple indexes, finds
candidate alignment locations using seed and extend, followed by a
gapped Smith-Waterman local alignment for each candidate
http://en.wikipedia.org/wiki/List_of_sequence_alignment_software
Burrows-Wheeler Transform (compression to reduce memory footprint)
Alignment tools for splice junction
mapping
•
•
•
•
Tophat
MapSplice
SpliceMap
HMMsplicer
Tophat
• Map reads to transcriptome using Bowtie
• Map to genome to discover novel exons
– or start here if no annotation available
• Split reads to smaller segments; map to
genome to discover novel splice junctions
• Report best alignment for each read
Trapnell et al. Bioinformatics 2009; Trapnell et al. Nature Protocols 2012
MapSplice & SpliceMap
• Tag alignment (user chooses aligner)
– Break reads into segments
– Map reads
– Unmapped segments considered for splice
junction mapping based on location of partner
segment
– Merge segments from read for final alignment
• Assess splice junction quality
Wang et al. NAR 2010, Au et al. NAR 2010
HMMsplicer
Local Source!
Dimon et al. PLoS One 2010
Compare tools for splice junction
mapping
Splice Junction Mapper
Junction
s
Prop.
Junctions
Bowtie map to known transcriptome (control)
SpliceMap
Tophat – no annotation
Tophat – annotation, no novel junctions
Tophat – annotation, novel junctions
23,037
21,990
20,196
22,544
22,599
0.955
0.877
0.979
0.981
Difficult to get a false positive rate because you don’t know what your annotation is missing.
Transcript Assignment
Aligned contiguous and
spliced reads
Build graph to connect
neighboring concordant
alignments
Traverse graph to
assemble variants
Assembled isoforms
Martin & Wang, Nature Reviews Genetics 2011
Transcript Assignment &|Abundance
Tools
• Assembly uses annotation
– Cufflinks
– MISO
• De novo assembly
– Cufflinks
– Trans-ABySS
– Trinity
– Maker
Cufflinks
• Constructs the parsimonious set of transcripts
that explain the reads observed. Basically,
finds a minimum path cover on the DAG.
• Derives a likelihood for the abundances of a
set of transcripts given a set of fragments.
• FPKM – fragments per kb of exon per million
fragments mapped.
Trapnell, Pachter
MISO
• Mixture of Isoforms
• Bayesian – treats expression level of set of
isoforms as random variable and estimates a
distribution over the values of this variable.
• Gives confidence intervals for expression
estimates and measures of DE as Bayes factors
Burge Lab @ MIT
Bias Correction and Normalization
• Random hexamer bias
(Hansen et al. 2010)
– From PCR or RT primers
– Re-estimate read counts to
account for bias
• Resources for normalization
– Bullard et al. 2010
– http://www.rnaseqblog.com/dataanalysis/which-methodshould-you-use-fornormalization-of-rna-seqdata/
Differential Expression
• Goal: determine whether observed difference
in read counts is greater than would be
expected due to random variation.
• If reads independently sampled from
population, reads would follow multinomial
distribution appx by Poisson
Pr(X = k) =
le
k -l
k!
Differential Expression
• BUT! We know that the count data show more
variance than expected
• Overdispersion problem mitigated by using
the negative binomial distribution, which is
determined by mean and variance
Kij @ NB(mij , s )
2
ij
Sample j, gene i
Differential Expression
• Binomial test
– Old Cuffdiff or if no replicates
• Negative binomial
– DESeq – estimate variance using all genes with
similar expression levels
– Cuffdiff – sim to DESeq, but incorp fragment
assignment uncertainty simultaneously
– EdgeR - moderate variance over all genes
• T-test
Use replicates to capture biological
variation
KO
KO1 KO2 KO3 WT1 WT2 WT3 Pool
gene RPM RPM RPM RPM RPM RPM RPM
Hk2
65
299
211
0.633
9.29E-03
Col1a1 171 162 178 326 160 324
511
810
0.301
1.23E-13
1095
655
0.011
5.16E-23
Slc2a1
162
78
59
59
87
WT
Pool Replicate
Pool
RPM adj Pval adj Pval
569 286 240 160 278 217
• Counts per sample have overlapping distribution
• Pooled counts show difference between KO and WT
• Biological variance is ignored in pooled analysis.
Differential Expression
Old cuffdiff
Some biology, finally?
• How have gene expression patterns have
changed during the course of differentiation?
• Which genes are specific to certain cell types?
• What can we learn about what those coexpressed genes do?
Clusters of co-expressed genes
• Use unsupervised
clustering to group genes
by expression pattern
• Use gene ontology
information to determine
which kinds of genes are in
each group
• Reveal novel associations
and gene types
Clusters of co-expressed genes
Pluripotency/stem cell: Nanog, Oct4
Mesoderm/cell fate commitment: Mesp1, Eomes
Cardiac precursors: Isl1, Mef2c, Wnt2
Cardiac structure/function: Actc1, Ryr2, Tnni3
Thanks for listening!
Alisha Holloway
Gladstone Institutes
Bioinformatics Core
alisha.holloway@gladstone.ucsf.edu