nescent : ngs : measuring expression jen taylor bioinformatics team csiro plant industry

NESCENT : NGS : Measuring expression

Jen Taylor

Bioinformatics Team

CSIRO Plant Industry

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


What is expression / transcriptome ?

mRNA

rRNAtRNA

siRNAmicroRNA

piRNA

tasiRNA lncRNA

DNA


Commemorative stained glass window for F.C. Crick, designed by Maria McClafferty.(Photograph: Paul Forster)

Gonville & Caius College, Cambridge, UK.

Beyond the Genome:

1995

Human Genome sequencing begins in earnest

“Mapping the Book of Life”

2000 - First Draft

2003 - Essential Completion

= approx 140, 000 genes

= 30, 000 – 40,000 genes ??

= 24, 195 genes !!!???


“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


Commemorative stained glass window for F.C. Crick, designed by Maria McClafferty.(Photograph: Paul Forster)

Gonville & Caius College, Cambridge, UK.

Beyond the Genome:

Gene Number ≠ Complexity

Co

mp

lexityRegulation

Gene

Transcriptome


Why the expression ?

High-throughput friendly

Context dependent

Regulatory

network

Predicts Biology

Transcriptome

Genome

Proteome

**Li et al., 2004

**


Measuring Expression ?

Parts Description• Function?

• Interconnectedness?

Comparisons• Population - level• Between genomes



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



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


How to measure Expression

PLATFORMS / TECHNOLOGY


Measuring Expression : platforms

• Closed systems – microarray• Probes immobilised on a substrate profile target species in the

transcriptome


Single and two colour arrays

Labelling

Two colour

Control

Experimental

Probe Library

Array

Labelling

Single colour

Sample A

Array Manufacture

Hybridisation

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)


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


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


RNA Sequencing

Mortazavi et al., 2008


RNASeq - Correspondence

• Range > 5 orders of magnitude

• Better detection of low abundance transcripts

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)


RNASeq - Workflow

Library Construction

Sample

Total RNA

PolyA RNA

Small RNA

Sequencing

Base calling & QC

Mapping to Genome

Assembly to Contigs

Differential Expression

SNP detection

Transcript structure

Secondary structure

Targets or Products


Illumina RNASeq : TruSeq


Small RNA sequencing

Small RNA

25

75

110

smallRNA separation: PAGE

small RNA < 35bp

134


Strand - specificity

Using adaptors Using chemical modification

SMART : addition of C’s on 5’ end

Ligation : 3’ and 5’ adaptors added sequentially

Levin et al., 2010

dUTP : Addition and removal after selection

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)


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)

• Non - PolyA• rRNA reduction


EXPERIMENTAL DESIGN and ANALYSIS

• 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

RNASeq Experimental Design


Defining Replicates

• Technical Replicates • Biological Replicates

Library 1

Lane 1

Individual

Library 2

Lane 2 Lane 3 Lane 4 Lane 1

,Individual 1

Lane 2

Individual 2

Library 1 Library 2

Depth = 2 x 100% lane / sample 100% lane / sample

Lane 1

Library 4

Multiplex

Library 3

Library 2

Library 1

L1

L2

L3

L4

25% lane / sample


Coverage Depth


Number of Replicates

edgeR <= 0.01 , DESeq <= 0.01

More information in biological replicates than depth

For differential expression

# Rep

s

2 4 6 8 10 12

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


RNASeq Analysis

• Overall Aim :• To get an accurate measurement of transcript abundance, structure

and identity

• Biases and Compositions

• Alignment• TopHat / Cufflinks

• Assembly• ABySS


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 = 2 x y

Gene A > Gene B


Length Bias

Oshlack and Wakefield, 2009


Alignment Bias


Sequencing Bias

Hansen et al., 2010


Bias



Gene A = z Reads / million / kb Gene B = y Reads / million / kb

Weighting schemas (e.g. Cufflinks) :

• Mapability

• kmer / fragment frequencies


Bias



Gene A1 = z Reads per million Gene A2 = y Reads per million

z = 2 x y

Sample A vs Sample B


Read density variability


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


A simple example – compositional bias

AA

BB

sample II

Sequencing budget / depth: 4000 reads

AA

DDCCBB

sample IExpected counts

1000

1000

1000

1000

2000

Expected counts

2000


Soil diversity by phylogenetic analysis - Phylum level

C

B

A

Recognized bacterial phyla

0% 20% 40% 60% 80% 100%

% distribution

454-sequence analysis of bacterial 16S rRNA gene~410,000 sequences

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


RNA Sequencing analysis

Sequence Data

Alignment

Read Density


SNPs

Transcript Characterisation

Assembly

Contigs

Genome?


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


TopHat

Trapnell et al., 2009; Roberts et al., 2011

Multimapping : ≤10 sites

Assembly : consensus ‘island’ exon


TopHat / Cufflinks

Trapnell et al., 2009; Roberts et al., 2011

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.


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


RNA Sequencing analysis

Sequence Data

Alignment

Read Density


SNPs

Transcript Characterisation

Assembly

Contigs

Genome?


Denovo transcriptome assembly

• ABySS• MIRA• Trinity• Velvet• AllPaths• Soap-denovo• Euler• CABOG• Edena• SHARCGS• VCAKE• SSAKE• CAP3

• 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

• 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

Miller et al., 2010


Assembly – Kmer graphs

Spurs

• Sequencing error

Bubbles

• Sequencing error

• Polymorphism

Frayed Rope / Cycles

• Repeats

Miller et al., 2010


ABySS & TransABySS

• User specifies k

• Optimal k depends on sequencing depth


ABySS & TransABySS

• Sequencing depth is relative to transcript abundance• Iterate over multiple k and merge

• Contigs contained within a large contig are “buried”


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%)


RNASeq Bioinformatics Analysis

• Aims:• To get an accurate measurement of transcript abundance,

structure and identity

• Biases and Compositions• Relative abundances NOT absolute

• Alignment

• Assembly


STATISTICAL ISSUES


Measuring Expression – Statistical Issues

• Data elements

• Normalisation

• Detection of Differential Expression


Count Data : of what ?


Count Data : of what ?

Garber et al., 2011


Statistical analysis of RNASeq

• Count data• Distribution is positively skewed, not normal• Between sample variability in counts - normalisation


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


Normalisation

• Assume the majority of log ratios = 0 [No change]

Robinson and Oshlack, 2010

TMM : Trimmed Mean of M values (log ratios)

Adjust TMM to be equal between samples


DE genes with and without TMM normalization


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


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


RNA Seq

• Variance also depends on the mean

Anders, 2010


RNASeq Model

The total counts for a transcript in sample j from condition c :

cjcj vss 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?


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


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

Contact UsPhone: 1300 363 400 or +61 3 9545 2176

Email: [email protected] Web: www.csiro.au

Thank you

Plant IndustryJennifer M TaylorBionformatics Leader

Phone: +61 2 62464929Email: [email protected]

Acknowledgements

Jose RoblesStuart StephenHua YingAndrew Spriggs

Alexie Pa

NESCENT Funding

nescent : ngs : measuring expression jen taylor bioinformatics team csiro plant industry

Documents

expression transcriptome

human genome sequencing

inherited genes

function polyadenylation

function availability

maria mcclafferty

abundance abundance

parts description function