Transcript Next-Generation Sequencing

Functional Genomics with Next-Generation
Sequencing
Jen Taylor
Bioinformatics Team
CSIRO Plant Industry
Capacity and Resolution
• Next generation sequencing
• Increasing capacity leads to increased resolution
Eric Lander, Broad Institute
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How a Genome Works?
Parts Description
• Function?
• Interconnectedness?
Comparisons
• Population - level
• Between genomes
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Application domains
Reference genome
No Reference Genome
Partially sequenced
UNsequenced
“PUN Genomes”
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Impact of a Reference Genome
Sequence Data
Genome
Alignment
Assembly
Read Density
Contigs
Characterisation
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Applications of Next Generation Sequencing
• Profiling of Variation
•
•
•
•
• Discovery
Genetic variation
Transcript variation
Epigenetic variation
Metagenomic variation
•
•
•
•
Novel genomes
Novel genes
Novel transcripts
Small / long non-coding RNA
Today
RNA Sequencing (RNASeq)
• Coding and non-coding transcript profiling
• Dynamic and Context dependent
Epigenomics
• Genome-wide protein-DNA interactions, DNA modifications
• Heritable and reversible regulation of gene expression
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RNASeq
• Qualitative – transcript diversity
• Quantitative – transcript abundance
• Impact of NGS
• Observation of transcript complexity
• Transcript discovery
• Small / long non-coding RNA
• Analytical challenges
• Transcript complexity
• Compositional properties
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RNASeq
Sample
Total RNA
PolyA RNA
Reference
Analysis
Small RNA
Mapping to
Genome
Digital “Counts”
Library
Construction
Reads per kilobase per million
(RPKM)
PUN
Transcript structure
Assembly to
Contigs
Secondary structure
Sequencing
Base calling & QC
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Targets or Products
RNASeq – Transcript Complexity
Mapping :
• Reads with multiple locations
•Conserved domains ?
•Sequencing error ?
• Reads Spanning Exons
• Gapped alignments ?
• Sequencing error ?
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Erange Pipeline : Mortazavi et al.,
Nature Methods VOL.5 NO.7 JULY 2008
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
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RNASeq – Compositional properties
True Reads
Composition
• Sequence counts are a composition
of a fixed number of total sequence
reads
• Therefore they are sum-constrained
and not independent
RPKM
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• Large variations in component
numbers and sizes can produce
artefacts
RNASeq - Correspondence
• Good correspondence with :
• Expression Arrays
• Tiling Arrays
• qRT-PCR
• Range of up to 5 orders of magnitude
• Better detection of low abundance
transcripts
• Greater power to detect
• Transcript sequence polymorphism
• Novel trans-splicing
• Paralogous genes
• Individual cell type expression
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Reference Genome - RNASeq
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Reference Genome - RNASeq
Human Exome
Number of exons targeted: ~180,000 (CCDS database)
plus700+ miRNA(Sanger v13)
300+ ncRNA
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Epigenome
• Protein-DNA interactions [ChIPSeq]
• Nucleosome positioning
• Histone modification
• Transcription factor interactions
• Methylation [MethylSeq]
• Impact of NextGen
• Whole genome profiling
• Resolution
• Analytical challenges
• Systematic bias
• Unambiguous mapping
• Robust event calling
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Image : ClearScience
ChIPSeq
MNase
Linker Digest
Remove
Nucleosomes
Sequence &
Align
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ChIPSeq
MNase
Digest
Remove
Nucleosomes
Sequence &
Align
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ChipSeq methods
CisGenome
ERANGE
FindPeaks
F-Seq
GLITR
MACS
PeakSeq
QuEST
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Pepke et al., 2009
MethylSeq using Bisulfite conversion
Cytosine
Uracil
Bisulfite
conversion
PCR
Bisulfite
conversion
5-methylcytosine
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Thymine
PCR
5-methylcytosine
Cytosine
Limited publications from BS-Seq
• Mammals
• Methylation predominant occurs at CpG site
• Several publications in human
• One publications in mouse
• Plants
• Methylation occurs at CG, CHH, CHG sites
• Two publications in arabidopsis
H = A, G, T
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Problems of mapping BS-seq reads
• Reduced sequence complexity
Watson
>>A Cm G T T C T C C A G T C>>
>>A C G T T T T
Bisulfite
conversion
T T A G
T T>>
>>A Cm G T T T T T T A G T T>>
Cm methylated
C
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Un-methylated
Problems of mapping BS-seq reads
• Increased search space
Watson >>
Crick <<
A Cm G T T C T C C A G T C >>
T G Cm A A G A G G T C A G <<
Bisulfite
conversion
BSW >>
BSC <<
TGCmAAGAGGTTAG <<
BSCR >>
BSC <<
ACG TTCTCCAAGA >>
TGCmAAGAGGTTAG <<
ACmGTTTTTTAGTT >>
PCR
BSW >>
BSWR <<
ACmGTTTTTTAGTT >>
TG CAAAAAATCAA >>
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ELAND
• Mapping reads to genome sequences
• Mapping reads to two converted genome
sequences
• Cross match for reads mapping to multiple
positions in converted genomes
• Mapping results were combined to generate methylation
information
• Eland only allows 2 mismatches.
Lister et al. Cell (2008)
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BSMAP
• Based on HASH table seeding algorithm
Xi and Li BMC Bioinformatics (2009)
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Re-mapping of Lister’s data using BSMAP
Raw Reads
Methods
Uniquely
Mapped Reads
Unique and
Nonclonal
Reads
Unique and
nonclonal
reads%
Eland
55,805,931
39,113,599
27.03%
BSMAP
67,975,425
48,498,687
35.52%
144,704,372
Lister et al. Cell (2008)
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Methylation pattern throughout chromosomes
Arabidopsis Chromosome 3
1.0
Watson
Methylation Level / 50Kb
CG
Crick
0.80
Watson
CHG
Crick
0.20
Watson
CHH
Crick
Position
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Partially / Unsequenced Genomes
Options for dealing with partial or unsequenced genomes
• Wait for or generate the genome sequence
• ‘Borrow’ a reference genome from a phylogenetic neighbour
• Take a deep breath and ‘do denovo’
• Denovo Genome
• Denovo Transcriptome
Gene Annotation
DNA or RNA Sequence
Data
Partial
Assembly
Partial Sequence
Database
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Genetic Variation
Transcript Variation
Non-coding RNA
Plant Genomes – Haploid Size
Human
Arabidopsis
Rice
Potato
Wheat
Sugarcane
Cotton
Barley
Diameter proportional to genome haploid genome size
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Plant Genomes – Total Size
Human
Cotton
Wheat
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Barley
Sugarcane
Denovo RNA Seq
• Why transcriptome ?
• Large genome sizes with high repeat content are difficult to
assemble
• Transcriptomes more constant size
• Enriched for functional content
• Aims :
• Transcript discovery
• Small /long non-coding RNA profiling
• Analytical challenges
• Assembly – ABySS, Velvet, Euler-SR
• Comparisons between non-discrete, overlapping transcripts
• Annotation
• Ploidy
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Summary – Impacts and Challenges
• RNASeq
•
•
•
•
Increased resolution
Increased power for transcript complexity and variation
Analytical challenges – transcript complexity, compositional bias
Large gains in small and long non-coding RNA profiling
• Epigenomics
• ChipSeq and MethylSeq
• Genome-wide with resolution
• Robust event calling is challenging
• Denovo transcriptomics
• Attractive option for large, repeat rich genomes
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Acknowledgements
CSIRO PI Bioinformatics Team
Andrew Spriggs
Stuart Stephen
Emily Ying
Jose Robles
Michael James
CSIRO Biostatistics
David Lovell
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