Transcript Genome-wide characteristics of sequence coverage by next

NESCENT : NGS : Measuring expression
Jen Taylor
Bioinformatics Team
CSIRO Plant Industry
Measuring Expression
• What & Why
• What is expression and why do we care?
• How
• Platforms / Technology
• Closed approaches – Microarray
• Open approaches - Sequencing
• Experimental Design
• Analysis
• Biases
• Bioinformatics
• Statistical Issues and Analysis
• In action
• Workshop – Detection of Differential Expression
• Case Studies in Plant functional genomics
CSIRO. Nescent August 2011 - Measuring Expression
What is expression / transcriptome ?
mRNA
tRNA
rRNA
DNA
siRNA
microRNA
piRNA
tasiRNA
CSIRO. Nescent August 2011 - Measuring Expression
lncRNA
Beyond the Genome:
1995
Human Genome sequencing begins in earnest
“Mapping the Book of Life”
2000 - First Draft
= approx 140, 000 genes
2003 - Essential Completion
= 30, 000 – 40,000 genes ??
= 24, 195 genes !!!???
Commemorative stained glass window for F.C. Crick, designed by
Maria McClafferty.(Photograph: Paul Forster)
CSIRO. Nescent August 2011 - Measuring Expression
Gonville & Caius College, Cambridge, UK.
“The failure of the human genome”
“despite more than 700 genome-scanning publications and
nearly $100bn spent, geneticists still had not found more than a
fractional genetic basis for human disease “
Manolio et al., Nature, 2009
“The most likely explanation for why genes for common diseases
have not been found is that, with few exceptions, they do not
exist.
…., if inherited genes are not to blame for our commonest
illnesses, can we find out what is? “
Guardian, 2011
CSIRO. Nescent August 2011 - Measuring Expression
Beyond the Genome:
Gene Number ≠ Complexity
Complexity
Gene
Regulation
Transcriptome
CSIRO. Nescent August 2011 - Measuring Expression
Commemorative stained glass window for F.C. Crick, designed by
Maria McClafferty.(Photograph: Paul Forster)
Gonville & Caius College, Cambridge, UK.
Why the expression ?
High-throughput friendly
Genome
Predicts Biology
**
Regulatory
network
Transcriptome
Context dependent
Proteome
CSIRO. Nescent August 2011 - Measuring Expression
**Li et al., 2004
Measuring Expression ?
Parts Description
• Function?
• Interconnectedness?
CSIRO. Nescent August 2011 - Measuring Expression
Comparisons
• Population - level
• Between genomes
Measuring Expression ?
What are important members of a transcriptome?
mRNA
•
polyadenylated, coding
•
alternatively spliced
Noncoding RNA (small RNA)
•
varying lengths, functions (18 – 32 bases)
•
microRNA, siRNA, piRNA, tasiRNA, long non-coding RNA
“Dark” RNA
• transcription outside of annotated genes
• Non-polyadenylated
Anti-sense transcription
CSIRO. Nescent August 2011 - Measuring Expression
Measuring Expression ?
How does the transcriptome vary to give rise to phenotype ?
Changes in Abundance
•
Abundance = Rate of Transcription – Rate of Decay
Changes in Function
•
Availability for function – polyadenylation, silencing, localisation
•
Suitability for function – alternate splicing
CSIRO. Nescent August 2011 - Measuring Expression
How to measure Expression
PLATFORMS / TECHNOLOGY
CSIRO. Nescent August 2011 - Measuring Expression
Measuring Expression : platforms
• Closed systems – microarray
• Probes immobilised on a substrate profile target species in the
transcriptome
CSIRO. Nescent August 2011 - Measuring Expression
CSIRO. Nescent August 2011 - Measuring Expression
Single and two colour arrays
Two colour
Single colour
Experimental
Control
Labelling
Labelling
Sample A
Hybridisation
Probe
Library
Array
Manufacture
CSIRO. Nescent August 2011 - Measuring Expression
Array
Scanning
Array profiling
Affymetrix Array
Targets
• Arabidopsis Genome
24,000
• C. elegans Genome
22,500
• Drosophila Genome
18, 500
• E. coli Genome
20, 366
• Human Genome U133 Plus
47,000
• Mouse Genome
39, 000
• Yeast Genome
• S.cerevisiae
5, 841
• S. pombe
5, 031
• Rat Genome
30, 000
• Zebrafish
14, 900
• Plasmodium / Anopheles
• P. faciparum
4,300
• A. gambiae
14,900
• Barley (25,500), Soybean (37,500 + 23,300 pathogen), Grape (15,700)
• Canine (21,700), Bovine (23,000)
• B.subtilis (5,000), S. aureus (3,300 ORFS), Xenopus (14, 400)
CSIRO. Nescent August 2011 - Measuring Expression
CSIRO. Nescent August 2011 - Measuring Expression
CSIRO. Nescent August 2011 - Measuring Expression
Closed System – Microarray
• Pros
•
•
•
•
High-throughput
Targeted profiling
Inexpensive – “population friendly”
Analytical methods are standardised
• Negative
• “Closed system” , novel = invisible
• Difficult to see allelle-specific expression
• Biases due to hybridisation
• SNPs
• Competitive and non-specific hybridisation
CSIRO. Nescent August 2011 - Measuring Expression
Open systems – RNA Sequencing
Technology:
• Illumina
• SOLiD, IonTorrent
• 454
Pros:
• Transcript discovery
• Allelic expression
• High resolution abundance measures
Cons:
• Analysis can be complex
• Expensive
• Sensitivity is sequencing depth dependent
CSIRO. Nescent August 2011 - Measuring Expression
RNA Sequencing
CSIRO. Nescent August 2011 - Measuring Expression
Mortazavi et al., 2008
RNASeq - Correspondence
• Range > 5 orders of
magnitude
• Better detection of low
abundance transcripts
CSIRO. Nescent August 2011 - Measuring Expression
Marioni et al., 2009
Platform Choice / Sample Preparation Choice
What do you want to profile ?
• Polyadenylated
• PolyA RNA extraction
• Small RNA (< 100 bases)
• Size filtering by gel
• Strand-specific
• RNA – Protein Interactions
• RNA Immunoprecipitation (IP)
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq - Workflow
Sample
Total RNA
PolyA RNA
Mapping to
Genome
Small RNA
Differential Expression
SNP detection
Transcript structure
Library
Construction
Secondary structure
Assembly to
Contigs
Sequencing
Base calling & QC
CSIRO. Nescent August 2011 - Measuring Expression
Targets or Products
Illumina RNASeq : TruSeq
CSIRO. Nescent August 2011 - Measuring Expression
Small RNA sequencing
Small RNA
smallRNA separation: PAGE
134
110
75
25
small RNA < 35bp
CSIRO. Nescent August 2011 - Measuring Expression
Strand - specificity
Using adaptors
Ligation : 3’ and 5’ adaptors added
sequentially
Using chemical modification
dUTP : Addition and removal after
selection
SMART : addition of C’s on 5’ end
CSIRO. Nescent August 2011 - Measuring Expression
Levin et al., 2010
CSIRO. Nescent August 2011 - Measuring Expression
Levin et al., 2010
Non-polyA methods
• Total RNA extraction
• Ribosomal RNA and tRNA > 95-97% of total RNA
• Ribosomal reduction methods
• Subtractive hybridisation with rRNA probes
• Exonuclease cleave of rRNA
• NuGen – “proprietary combination of reverse transcriptase and
primers in the Ovation RNA-Seq System”
• cDNA normalisation methods
• Partial digestion of any highly abundant species (Evrogen)
CSIRO. Nescent August 2011 - Measuring Expression
Platform Choice / Sample Preparation Choice
What do you want to profile ?
• Polyadenylated
• PolyA RNA extraction
• Small RNA (< 100 bases)
• Size filtering by gel
• Strand-specific
• RNA – Protein Interactions
• RNA Immunoprecipitation (IP)
CSIRO. Nescent August 2011 - Measuring Expression
• Non - PolyA
• rRNA reduction
EXPERIMENTAL DESIGN and ANALYSIS
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq Experimental Design
• Issues:
• sequencing depth - how much ?
• number of replicates – how many ?
• Aims of the data :
• Transcriptome assembly / transcript characterisation
• Maximise depth
• Detection of differential expression (denovo or reference)
• Balance depth and replication
CSIRO. Sequencing Depth V.S. Number of Replicates
Library 1
Defining Replicates
Library 2
• Technical Replicates
Individual
Library 1
Lane 1
Individual 1
Library 2
Lane 2 Lane 3
• Biological Replicates
Lane 4
Library 3
Individual 2
Library 4
Library 1
Library 2
Multiplex
Lane 1
Lane 2
Lane 1
,
L1
L2
L3
L4
Depth =
2 x 100% lane / sample
CSIRO. Sequencing Depth V.S. Number of Replicates
100% lane / sample
25% lane / sample
CSIRO. Sequencing Depth V.S. Number of Replicates
Coverage Depth
CSIRO. Sequencing Depth V.S. Number of Replicates
Number of Replicates
# Reps
2
4
6
8
False P
0.03
0.03
0.03
0.03
0.03
0.03
False N
0.84
0.72
0.64
0.59
0.54
0.50
True P
0.16
0.28
0.36
0.41
0.46
0.50
True N
0.97
0.97
0.97
0.97
0.97
0.97

10
12
edgeR <= 0.01 , DESeq <= 0.01
More information in biological replicates than depth
For differential expression
CSIRO. Sequencing Depth V.S. Number of Replicates
RNASeq Analysis
• Overall Aim :
• To get an accurate measurement of transcript abundance, structure
and identity
• Biases and Compositions
• Alignment
• TopHat / Cufflinks
• Assembly
• ABySS
CSIRO. Nescent August 2011 - Measuring Expression
Assumptions
Every transcript / k-mer has equal chance of being sequenced
No. sequences observed ≈ transcript abundance
Gene A = z Reads / million
Gene B = y Reads / million
z=2xy
Gene A > Gene B
CSIRO. Nescent August 2011 - Measuring Expression
Length Bias
Oshlack and Wakefield, 2009
CSIRO. Nescent August 2011 - Measuring Expression
Alignment Bias
CSIRO. Nescent August 2011 - Measuring Expression
Alignment Bias
CSIRO. Nescent August 2011 - Measuring Expression
Sequencing Bias
CSIRO. Nescent August 2011 - Measuring Expression
Hansen et al., 2010
Bias
Every transcript / k-mer has equal chance of being sequenced
No. sequences observed ≈ transcript abundance
Gene A = z Reads / million / kb
Weighting schemas (e.g. Cufflinks) :
• Mapability
• kmer / fragment frequencies
CSIRO. Nescent August 2011 - Measuring Expression
Gene B = y Reads / million / kb
Bias
Every transcript / k-mer has equal chance of being sequenced
No. sequences observed ≈ transcript abundance
Sample A vs Sample B
Gene A1 = z Reads per million
z=2xy
CSIRO. Nescent August 2011 - Measuring Expression
Gene A2 = y Reads per million
Read density variability
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq – Compositional properties
Depth of Sequence
• Sequence count ≈ Transcript Abundance
• Majority of the data can be dominated by a
small number of highly abundant transcripts
• Ability to observe transcripts of smaller
abundance is dependent upon sequence
depth
• Fixed budget of reads
CSIRO. Nescent August 2011 - Measuring Expression
A simple example – compositional bias
Sequencing budget / depth: 4000 reads
sample I
A
B
C
D
Expected
counts
1000
1000
1000
1000
CSIRO. Nescent August 2011 - Measuring Expression
sample II
A
B
Expected
counts
2000
2000
Soil diversity by phylogenetic analysis - Phylum level
454-sequence analysis of bacterial 16S rRNA gene
~410,000 sequences
Recognized bacterial phyla
A
B
C
0%
20%
40%
60%
80%
100%
% distribution
CSIRO. Nescent August 2011 - Measuring Expression
A. Richardson, CSIRO
RNASeq Bioinformatics Analysis
• Aims:
• To get an accurate measurement of transcript abundance,
structure and identity
• Biases and Compositions
• Relative abundances NOT absolute
• Alignment
• TopHat
• Assembly
• ABySS
CSIRO. Nescent August 2011 - Measuring Expression
RNA Sequencing analysis
Sequence Data
Genome?
Alignment
Assembly
Read Density
Contigs
Differential Expression
SNPs
Transcript Characterisation
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq – Alignment Considerations
Reads with multiple locations
• Discard / Random Allocation
• Clustering - local coverage
• Weighting
Reads Spanning Exons
• Make and align to exon junction libraries
• Denovo junction detection
Summarisation of counts
• Exons
• Transcript boundaries
• Inferred read boundaries
CSIRO. Nescent August 2011 - Measuring Expression
TopHat
Multimapping : ≤10 sites
Assembly : consensus ‘island’ exon
CSIRO. Nescent August 2011 - Measuring Expression
Trapnell et al., 2009; Roberts et al., 2011
TopHat / Cufflinks
Heuristics :
• “Correct” errors in low coverage areas
• Grabs 45 bp either side of islands to capture
splice sites
• Collapse small islands
• Looks for junctions within larger islands, highly
covered
Cufflinks :
• calculates the probability of observing a
certain fragment within a given transcript given
surrounding fragments.
CSIRO. Nescent August 2011 - Measuring Expression
Trapnell et al., 2009; Roberts et al., 2011
Alignment
• Great if you have a fully annotated, reference
• Okay.. If you have a partially annotated reference
• “Different” if you have a big bunch of ESTs
Options:
• Align to a neighbouring genome or EST library
• Denovo transcriptome assembly
Tools:
• ABySS, Mira, Trinity, HT-Seq, SAMtools
CSIRO. Nescent August 2011 - Measuring Expression
RNA Sequencing analysis
Sequence Data
Genome?
Alignment
Assembly
Read Density
Contigs
Differential Expression
SNPs
Transcript Characterisation
CSIRO. Nescent August 2011 - Measuring Expression
Denovo transcriptome assembly
•
•
•
•
•
•
•
•
•
•
•
•
•
ABySS
MIRA
Trinity
Velvet
AllPaths
Soap-denovo
Euler
CABOG
Edena
SHARCGS
VCAKE
SSAKE
CAP3
CSIRO. Nescent August 2011 - Measuring Expression
• Will run on reasonable computer
resources for large genomes
• (e.g. < 1 TB of RAM)
• Paired end data handling
• Platform flexible
• Handles haplotype complexity and
polyploid genomes
Denovo transcriptome assembly
•
•
•
•
•
•
•
•
•
•
•
•
•
ABySS
MIRA
Trinity
Velvet
AllPaths
Soap-denovo
Euler
CABOG
Edena
SHARCGS
VCAKE
SSAKE
CAP3
CSIRO. Nescent August 2011 - Measuring Expression
• Will run on reasonable
computer resources for large
genomes
• (e.g. < 1 TB of RAM)
• Handles paired end data
• Handles data from all platforms
• Handles haplotype complexity
and polyploid genomes
Assembly – Kmer graphs
K=4
CSIRO. Nescent August 2011 - Measuring Expression
Miller et al., 2010
Assembly – Kmer graphs
Spurs
• Sequencing error
Bubbles
• Sequencing error
• Polymorphism
Frayed Rope / Cycles
• Repeats
CSIRO. Nescent August 2011 - Measuring Expression
Miller et al., 2010
Assembly – Kmer graphs
Spurs
• Sequencing error
Bubbles
• Sequencing error
• Polymorphism
Frayed Rope / Cycles
• Repeats
CSIRO. Nescent August 2011 - Measuring Expression
Miller et al., 2010
ABySS & TransABySS
• User specifies k
• Optimal k depends on
sequencing depth
CSIRO. Nescent August 2011 - Measuring Expression
ABySS & TransABySS
• Sequencing depth is relative to transcript abundance
• Iterate over multiple k and merge
• Contigs contained within a large contig are “buried”
CSIRO. Nescent August 2011 - Measuring Expression
Assessing assembly quality ?
• Comparisons between assembly algorithms
• Contig summary statistics
• Comparisons to known resources (e.g. ESTs)
Trial on Rice Transcriptome:
• 120 Million 75 bp single end Illumina reads – embryo
• ABySS :
• Number of contigs = 6, 804
• Contig length range = 38 – 2,818 [mean = 203]
• Database comparisons :
• Rice public cDNA sequences : 67, 393
• Contigs with high quality matches to cDNA : 6,555 (96%)
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq Bioinformatics Analysis
• Aims:
• To get an accurate measurement of transcript abundance,
structure and identity
• Biases and Compositions
• Relative abundances NOT absolute
• Alignment
• Assembly
CSIRO. Nescent August 2011 - Measuring Expression
STATISTICAL ISSUES
CSIRO. Nescent August 2011 - Measuring Expression
Measuring Expression – Statistical Issues
• Data elements
• Normalisation
• Detection of Differential Expression
CSIRO. Nescent August 2011 - Measuring Expression
Count Data : of what ?
CSIRO. Nescent August 2011 - Measuring Expression
Count Data : of what ?
CSIRO. Nescent August 2011 - Measuring Expression
Garber et al., 2011
Statistical analysis of RNASeq
• Count data
• Distribution is positively skewed, not normal
• Between sample variability in counts - normalisation
CSIRO. Nescent August 2011 - Measuring Expression
Normalization is required
Two scenarios :
1. Different sizes of total
reads (library size)
2. Fixed library size,
subset of highly
expressed reads in 1
sample.
Both reduce sequencing
budget available for the
majority of transcripts
CSIRO. Nescent August 2011 - Measuring Expression
Normalisation
• Assume the majority of log ratios = 0 [No change]
TMM : Trimmed Mean of M values (log ratios)
Adjust TMM to be equal between samples
CSIRO. Nescent August 2011 - Measuring Expression
Robinson and Oshlack, 2010
DE genes with and without TMM
normalization
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq data – Poisson Distributions
• Poisson distributions are used when things are counted
• The probability of seeing n events in a fixed time or space
• The number of lions on a 1 day safari
• The number of raindrops on a tennis court
• The number of flying elephants in a year
• Requires λ : rate of events
• Variance = mean = λ
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq data – Negative Binomial
• RNASeq data is more variable than Poisson
• Variance > mean = λ
• Less prominent for large mean
• Over-dispersed Poisson
Noise types
• Shot noise
• Unavoidable, prominent for low mean
• Technical noise
• Small, hopefully, can be managed
• Biological noise
• Sample differences
CSIRO. Nescent August 2011 - Measuring Expression
RNA Seq
• Variance also depends on the mean
CSIRO. Nescent August 2011 - Measuring Expression
Anders, 2010
RNASeq Model
The total counts for a transcript in sample j from condition c :
s j c  s j vc 
2
Library
normalisation
Mean Value
Fitted Variance
(overdispersion)
For a given gene , test for a difference in counts between conditions.
Is mean c1 + mean c2 statistically different to mean c1 + mean c1?
CSIRO. Nescent August 2011 - Measuring Expression
RNASeq DE Testing
•
•
•
•
•
DESeq – Anders and Huber, 2010
EdgeR – Robinson et al., 2009 – R
BaySeq – Hardcastle and Kelley, 2010 – R
DEGSeq – Wang et al., 2010 – R
NBP - Di et al., 2011
• LOX – Zhang et al., 2010
• Infers expression measures allowing for incorporation of noise from
different methodologies in the one experimental design
CSIRO. Nescent August 2011 - Measuring Expression
Measuring Expression
• What & Why
• What is expression and why do we care?
• How
• Platforms / Technology
• Closed approaches – Microarray
• Open approaches - Sequencing
• Experimental Design
• Analysis
• Biases
• Bioinformatics
• Statistical Issues and Analysis
• In action
• Workshop – Detection of Differential Expression
• Case Studies in Plant functional genomics
CSIRO. Nescent August 2011 - Measuring Expression
Acknowledgements
Plant Industry
Jennifer M Taylor
Bionformatics Leader
Phone: +61 2 62464929
Email: [email protected]
Jose Robles
Stuart Stephen
Hua Ying
Andrew Spriggs
Alexie Pa
NESCENT Funding
Thank you
Contact Us
Phone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au