rna-seq: a high-resolution view of the transcriptome
TRANSCRIPT
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Sean Davis, M.D., Ph.D.Genetics Branch, Center for Cancer Research
National Cancer InstituteNational Institutes of Health
RNA-seq: A high-resolutionView of the Transcriptome
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Normal Karyotype
Tumor Karyotype
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The Central Dogma
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phenotype
Gene Copy Number
Sequence Variation
Chromatin Structure and
Function
Gene Expression
Transcriptional Regulation
DNA Methylation
Patient and Population
Characteristics
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+
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=
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Your Nature Paper
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High Throughput SequencingAKA, NGS
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DNA(0.1-1.0 ug)
Single molecule arraySample preparation
Cluster growth5’
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1 2 3 7 8 94 5 6
Image acquisition Base calling
T G C T A C G A T …
Sequencing
Illumina SBS TechnologyReversible Terminator Chemistry Foundation
© Illumina, Inc.http://www.illumina.com/technology/sequencing_technology.ilmnhttp://seqanswers.com/forums/showthread.php?t=21
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Single end vs paired end sequencing
Illumina Paired-end sequencingPaired-end: useful for RRBS, essential for RNA-seq, not useful for ChIP-
seq
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What comes out of the machine: short reads in fastq format
@D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1CTCCTGGAAAACGCTTTGGTAGATTTGGCCAGGAGCTTTCTTTTATGTAAATTG+D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1[^^cedeefee`cghhhfcRX`_gfghf^bZbecg^eeb[caef`ef^a_`eXa@D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1TCCANCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTC+D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1ab_eBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB@D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1TACAAGTGCAGCATCAAGGAGCGAATGCTCTACTCCAGCTGCAAGAGCCGCCTC+D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1_[_ceeec[^eeghdffffhh^efh_egfhfgeec_fbafhhhhd`caegfheh@D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1GAAGGAGAGAAGGGGAGGAGGGCGGGGGGCACCTACTACATCGCCCTCCACATC+D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1\^_accceg`gga`f[fgcb`Ucgfaa_LVV^[bbbbbRWW`W^Y[_[^bbbbb@D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1GTGGCCGATTCCTGAGCTGTGTTTGAGGAGAGGGCGGAGTGCCATCTGGGTAGC+D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1aa_eeeeegggggihhiiifgeghfeghbgcghifiidg^dbgggeeeee`dcd@D3B4KKQ1_0166:8:1101:2358:2174#CGATGT/1CTGACCTGGGTCCTGTGGTGCTCAGCCTTTTGAAGATGCCAGAAAAATACGTCG+D3B4KKQ1_0166:8:1101:2358:2174#CGATGT/1\^_cccccg^Y`ega`fg`ebegfhd^egghhghfffhghdhbfffhhhfgfcf
QS to int In R:as.integer(charToRaw(‘e'))-33
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Pair end sequencings_8_1_sequence.txt.gz s_8_2_sequence.txt.gz
@D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1CTCCTGGAAAACGCTTTGGTAGATTTGGCCAGGAGCTTTCTTTTATGTAAATTG+D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1[^^cedeefee`cghhhfcRX`_gfghf^bZbecg^eeb[caef`ef^a_`eXa@D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1TCCANCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTC+D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1ab_eBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB@D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1TACAAGTGCAGCATCAAGGAGCGAATGCTCTACTCCAGCTGCAAGAGCCGCCTC+D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1_[_ceeec[^eeghdffffhh^efh_egfhfgeec_fbafhhhhd`caegfheh@D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1GAAGGAGAGAAGGGGAGGAGGGCGGGGGGCACCTACTACATCGCCCTCCACATC+D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1\^_accceg`gga`f[fgcb`Ucgfaa_LVV^[bbbbbRWW`W^Y[_[^bbbbb@D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1GTGGCCGATTCCTGAGCTGTGTTTGAGGAGAGGGCGGAGTGCCATCTGGGTAGC+D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1aa_eeeeegggggihhiiifgeghfeghbgcghifiidg^dbgggeeeee`dcd
@D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/2GGCATATTTAACAGCATTGAACAGAATTCTGTGTCCTGTAAAAAAATTAGCTTA+D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/2a__aaa`ce`cgcffdf_acda^ea]befffbeged`g[a`e_caaac]cb`gb@D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/2TTGAGGCTGTTGTCATACTTCTCATGGTTCACACCCATGACGAACATGGGGGCG+D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/2a__eeeeeggegefhhhiiihhhhhiieghhhghhiiffhiififhhiihegic@D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/2CGGGGTGCACCTCGTCGTAGAGGAACTCTGCCGTCAGCTCTGCCCCATCGCCAA+D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/2^__ee__cge`cghghhfgddgfgi]ehhfffff^ec[beegidffhhfhadba@D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/2CTTAGTCTCAGTTTTCCTCCAGCAGCCTGAGGAAACTCAAAGGCACAGTTCCCA+D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/2_abeaaacg^g^eghhhhgafghhdfghfedeghfiiicfbgdHYagfeecggf@D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/2TAGGCTCAAAGTCTAACGCCAATCCCGAACCTGGGCATCTGTACACACACACAC+D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/2abbeceeegggcghiihiihhhhiifhiiiiihiiiiiiihegh`eggfebfhg
… …
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RNA-seq protocol schematic
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Our First Experiment
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Overview of BAC in the Genome
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Sequencing a BAC
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Sequence Coverage
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Repeats
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Repeats
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Repeats are not created equal
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Approaches to RNA-seq
Nature Biotech (2010) 28, 421-423
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Alignment
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RNA-seq Alignment
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Run Time
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Alignment Yield
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Splice Read Placement Accuracy
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Impact on Transcript Assembly
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Transcript Quantification
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Models for RNA-seq
• Count-based models• Multi-reads (isoform resolution)• Paired-end reads (include length resolution
step)• Positional bias along transcript length• Sequence bias
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Read Counting
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Mortazavi, 2008, NMeth
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L. Pachter (2011) arXiv:1104.3889v
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Sequence Bias--priming
Hansen (2010), NAR
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Sample-specific Sequence Bias
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Models for RNA-seq
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Result of Quantification
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Clustering and Visualization
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Hierarchical ClusteringGene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
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Hierarchical ClusteringGene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
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Hierarchical ClusteringGene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
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Hierarchical ClusteringGene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
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Distance Metrics
Euclidean distance
Manhattan distance
Minkowski distance (generalized distance)
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Distance Metrics• Correlation
– maximum value of 1 if X and Y are perfectly correlated– minimum value of -1 if X and Y are exactly opposite– d(X,Y) = 1 – rxy
• Many, many others• Choice of distance metric can be driven by
underlying data (eg., binary data, categorical data, outliers, etc.)
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Example of Distance Metric Choice
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Example• dat = matrix(rnorm(10000),ncol=20)• dat[1:100,1:10] = dat[1:100,1:10]+1• hclust• dist• as.dist(1-cor)
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Differential Expression
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MA Plot
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DE False Positive Rates
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DE Evaluation
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DE Software Runtime
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RNA-seq workflow as proposed by Anders et al. in Nature Protocols
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MA Plot
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Fusion Gene Detection
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Fusion gene schematic
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Fusion Detection
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False Positive Fusion Detection
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Experimental Design
• What are my goals?– Differential expression?– Transcriptome assembly?– Identify rare, novel trancripts?
• System characteristics?– Large, expanded genome?– Intron/exon structures complex?– No reference genome or transcriptome
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Experimental Design
• Technical replicates– Probably not needed due to low technical variation
• Biological replicates– Not explicitly needed for transcript assembly– Essential for differential expression analysis– Number of replicates often driven by sample
availability for human studies– More is almost always better
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Links of Interest
• http://bioconductor.org• http://biostars.org• http://www.rna-seqblog.com/• https://genome.ucsc.edu/ENCODE/• http://www.ncbi.nlm.nih.gov/gds/
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Visualizing Splicing