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20140710 Christopher Mason ERCC 2.0 Workshop
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Mul$-‐pla(orm and cross-‐methodological reproducibility of transcriptome
profiling by RNA-‐seq in the ABRF Next-‐Genera$on Sequencing Study (ABRF-‐NGS)
and FDA’s SEQC Study
Christopher E. Mason, Ph.D. Assistant Professor
Department of Physiology and Biophysics & The Ins$tute for Computa$onal Biomedicine at
Weill Cornell Medical College and the Tri-‐Ins$tu$onal Program on Computa$onal Biology and Medicine Affiliate Fellow of the Informa$on Society Project, Yale Law School
July 10th, 2014 @mason_lab
0. Background
1. RNA Standards
2. Removing RNA-‐Seq Varia$on
3. Epitranscriptome
4. #JustSequenceIt
Exome: 2% of the genome?
Human Genome Organization
from Mason, State, and Moldin, Kaplan & Sadock’s Comprehensive Textbook of Psychiatry, 2009
ENCODE ac$ve elements
“These data enabled us to assign biochemical func$ons for 80% of the genome,
in par$cular outside of the well-‐studied protein-‐coding regions.”
“The ENCODE results were predicted by one of its authors to necessitate the rewri$ng of textbooks.” “We agree, many textbooks dealing with marke;ng, mass-‐media hype, and public rela;ons may well have to be rewriAen.”
GENCODE annota;on (v19) July 10th, 2014
Coding genes: 20,345 Noncoding genes: 22,883 Psuedogenes: 14,206
Other genes: 516 57,820 total
RNA-‐Seq and all its flavors create excitement
Differential expression by gene, exon, splice isoform, allele, & transcript!Algorithms: STAR, r-make, ASE, limma-voom, RSEM"
3!
reads"
alignments"
Sorted BAMs"
GENCODE "annotation"
queries "
Rea
ds/b
p"
Alignment on HPC nodes"
References!
hg19"
RefSeq"
miRBase"
rRNA"
Adapters"
Find ncRNAs and new genes!Algorithms: r-make, Aceview"
4!
Sequencing Data"
Gene fusion detection!Algorithms: r-make, Snowshoes"
5!
Genetic variation (SNVs and indels)!Algorithms: STAR/GATK, r-make"
2!
Predict polyA sites & gain/loss of miRNA binding sites!Algorithms: r-make, BAGET AlexaSeq, TargetScan"
6!
!Viruses/Bacteria/Other Algorithm: MG-RAST"
Remaining Reads! TCTGCTTTAGGATAGATCGATAGCTAGTTCAT CTGCTTTAGGATAGATCGATAGCTAGTTCATC!TCTGCTTTAGGATAGATCGATAGCTAGTTCAT!
7!
1!
RNA-‐seq = Love R-‐make tools
But! There is some noise
What is the source of the wiggles?
Genes
ESTs
Liver
Kidney
Adult Brain
Fetal Hippocampus
Fetal Amygdala
The Dirty Dozen: >= 12 Sources of Technical Noise in RNA-‐Seq (1) RNA integrity: Sample purity or degrada$on (2) Sample RNA complexity: polyA RNA, total RNA, miRNA (3) cDNA synthesis: random hexamer vs. polyA-‐primed (4) Library isola;on: Gel excision vs. column (5) Technical Errors: Machine, Site, Lane, Technician, Library Size (6) Amplifica;on Cycles or Methods: NuGen, Tn5, Phi29 (7) Input amount: (1, 10, 100, 1000 cells) (8) Algorithms: for alignment and assembly (9) Fragment size distribu;on: Paired-‐End, Single-‐End (adaptors) (10) Liga;on Efficiency: Mul$plexing/Barcoding and RNA ligases (11) Depth of Sequencing: cost/benefit point (12) RNA fragmenta;on: ca$on, enzyma$c
Which type of RNA? Type Abbreviation Function Organisms
7SK$RNA$ 7SK$ negatively$regulating$CDK9/cyclin$T$complex$ metazoansSignal$recognition$particle$RNA$ 7SRNA Membrane$integration$ All$organisms$
Antisense$RNA$ aRNA$ Regulatory All$organisms$CRISPR$RNA$ crRNA$ Resistance$to$parasites Bacteria$and$archaea$Guide$RNA$ gRNA$ mRNA$nucleotide$modification$ Kinetoplastid$mitochondria$
Long$noncoding$RNA$ lncRNA XIST$(dosage$compensation),$HOTAIR$(cancer) Eukaryotes$MicroRNA$ miRNA$ Gene$regulation$ Most$eukaryotes$
Messenger$RNA$ mRNA$ Codes$for$protein$ All$organismsPiwiRinteracting$RNA$ piRNA$ Transposon$defense,$maybe$other$functions$ Most$animals$
Repeat$associated$siRNA$ rasiRNA$ Type$of$piRNA;$transposon$defense$ Drosophila$Retrotransposon$ retroRNA selfRpropagation Eukaryotes$and$some$bacteria$Ribonuclease$MRP$ RNase$MRP$ rRNA$maturation,$DNA$replication$ Eukaryotes$Ribonuclease$P$ RNase$P$ tRNA$maturation$ All$organisms$Ribosomal$RNA$ rRNA$ Translation$ All$organisms
Small$Cajal$bodyRspecific$RNA$ scaRNA$ Guide$RNA$to$telomere$in$active$cells MetazoansSmall$interfering$RNA$ siRNA$ Gene$regulation$ Most$eukaryotes$
SmY$RNA$ SmY$ mRNA$transRsplicing$ Nematodes$Small$nucleolar$RNA$ snoRNA$ Nucleotide$modification$of$RNAs$ Eukaryotes$and$archaea$Small$nuclear$RNA$ snRNA$ Splicing$and$other$functions$ Eukaryotes$and$archaea$TransRacting$siRNA$ tasiRNA$ Gene$regulation$ Land$plants$Telomerase$RNA$ telRNA Telomere$synthesis$ Most$eukaryotes$
TransferRmessenger$RNA$ tmRNA$ Rescuing$stalled$ribosomes$ Bacteria$Transfer$RNA$ tRNA$ Translation$ All$organisms
Viral$Response$RNA$ viRNA AntiRviral$immunity C$elegansVault$RNA$ vRNA$ selfRpropagation Expulsion$of$xenobioticsY$RNA$ yRNA RNA$processing,$DNA$replication$ Animals$
And -‐ which one do we use? Technologies Bifurcate into two main realms:
Platform Instrument Template0Preparation Chemistry Avearge0Length Longest0ReadIllumina HiSeq2500 BridgePCR/cluster Rev.<Term.,<SBS 100 150
Illumina HiSeq2000 BridgePCR/cluster Rev.<Term.,<SBS 100 150
Illumina MiSeq BridgePCR/cluster Rev.<Term.,<SBS 250 300
GnuBio GnuBio emPCR HybFAssist<Sequencing 1000* 64,000*
Life<Technologies SOLiD<5500 emPCR Seq.<by<Lig. 75 100
LaserGen LaserGen emPCR Rev.<Term.,<SBS 25* 100*
Pacific<Biosciences RS Polymerase<Binding RealFtime 1800 15,000
454 Titanium emPCR PyroSequencing 650 1100
454 Junior emPCR PyroSequencing 400 650
Helicos Heliscope adaptor<ligation Rev.<Term.,<SBS 35 57
Intelligent<BioSystems MAXFSeq Rolony<amplification TwoFStep<SBS<(label/unlabell) 2x100 300
Intelligent<BioSystems MINIF20 Rolony<amplification TwoFStep<SBS<(label/unlabell) 2x100 300
ZS<Genetics N/A Atomic<Lableing Electron<Microscope N/A N/A
Halcyon<Molecular N/A N/A Direct<Observation<of<DNA N/A N/A
Platform Instrument Template0Preparation Chemistry Avearge0Length Longest0ReadIBM<DNA<Transistor N/A none Microchip<Nanopore N/A N/A
NABsys N/A none Nanochannel N/A N/A
Bionanogenomics N/A anneal<7mers Nanochannel 10,000* 1,000,000*
Life<Technologies PGM emPCR SemiFconductor 150 300
Life<Technologies Proton emPCR SemiFconductor 120 240
Life<Technologies Proton<2 emPCR SemiFconductor 400* 800*
Genia N/A none Protein<nanopore<(aFhemalysin) N/A N/A
Oxford<Nanopore MinION none Protein<Nanopore 10,000 10,000*
Oxford<Nanopore GridION<2K none Protein<Nanopore 10,000 500,000*
Oxford<Nanopore GridION<8K none Protein<Nanopore 10,000 500,000*
*Values<are<estimates<from<companies<that<have<not<yet<released<actual<data
Optical<Sequencing
Electical<Sequencing
Table<1:<Types<of<HighFThroughput<Sequencing<Technologies
Mason CE, Porter SG, Smith TM. Adv Exp Med Biol. 2014
0. Genomes
1. RNA Standards
2. Removing RNA-‐Seq Varia$on
3. Epitranscriptome
4. #JustSequenceIt
The complexity of the transcriptome is vast
exon1 exon2 exon3
exon1-‐exon2 exon1-‐exon3 exon2-‐exon3
exon1-‐exon2-‐exon3
6 63 15
7 127 21
8 255 28
3 7 3
4 15 6
5 31 10
1 1 0
2 3 1
Exons Variants Junc;ons
2n-1
Exon 1 Exon 2 Exon 3
Exon 1 Exon 2 Exon 3
n(n-1) 2
Exon4
Exon4
exon4 exon1-‐ exon4 exon2-‐exon4 exon3-‐exon4
exon1-‐exon2-‐exon4 exon1-‐exon3-‐exon4 exon2-‐exon3-‐exon4
exon1-‐exon2-‐exon3-‐exon4
8x1083 theoretical transcript combinations 8x1080 atoms in the universe
(159 atoms/star, 111 stars/galaxy, 110 galaxies) Li and Mason, ARGHG, 2014
Es$mate of False Junc$on Rate
5’ 3’
Exon-‐Edge DB (3’-‐5’ junc$ons)
2 3 4 1 Exon # :
False Exon-‐Edge DB (3’-‐3’ junc$ons)
False Exon-‐Edge DB (5’-‐5’ junc$ons)
False Exon-‐Edge DB (5’-‐3’ junc$ons)
5’ 3’
5’ 3’
5’ 3’
FDA’s Sequencing Quality Control (SeQC) Consortium: Seq Standards from 323 members of
Government (FDA), Industry, Academia
>RNA sequence from pilot study based on MAQC samples and MAQC design: ~Pilot data from 2008-2012 MAQC Sample A/B, & ERCCs from: • 454 • Helicos • Illumina • Lifetech ~1000Gb of Illumina BodyMap data ~300Gb of experimental RNA-Seq
Associa;on of Biomolecular Research Facili;es (ABRF) Next Genera;on Sequencing (NGS) Study: Phase I: RNA-‐Seq and degraded RNA-‐Seq (2011-‐2013) Phase II: DNA-‐Seq and Hard-‐to-‐sequence regions (2014-‐2016) Phase III: Asteroid and Mar$an Surface Sequencing (2017-‐2050?) 1. Cross Pla(orm: HiSeq2000, 454, PGM, Proton, PacBio 2. Cross-‐Protocol: ribo-‐, direc$onal, non-‐direc$onal, degraded RNA 3. Cross-‐Site: 3 sites for each pla(orm, replicates at each site
Improve the detec$on and quan$fica$on of molecular signatures for use in basic, applied, and clinical research. Specifically, the ABRF-‐NGS group will test and characterize the myriad, rapidly evolving NGS techniques for RNA-‐Seq and DNA-‐Seq.
Samples of the MAQC, SEQC and ABRF-‐NGS Study
ß SEQC samples (FDA) AB
A B
A
BCD
A BA BA BA B C D E F
AB A
B
AB
ABEF
Pacific Biosciences RS
Life TechnologiesIon PGM
Life TechnologiesIon Proton
A: 10 cancer cell lines B: Brain $ssues
AB
A B
A
BCD
A BA BA BA B C D E F
AB A
B
AB
ABEF
Pacific Biosciences RS
Life TechnologiesIon PGM
Life TechnologiesIon Proton
Cross-‐pla(orm experimental design
AB
A B
A
BCD
A BA BA BA B C D E F
AB A
B
AB
ABEF
Pacific Biosciences RS
Life TechnologiesIon PGM
Life TechnologiesIon Proton
Cross-‐Site experimental design
AB
A B
A
BCD
A BA BA BA B C D E F
AB A
B
AB
ABEF
Pacific Biosciences RS
Life TechnologiesIon PGM
Life TechnologiesIon Proton
Cross-‐Protocol experimental design
All technologies have strengths & weaknesses
Vendor Instrument Version Run Time (hours)
Read Length (mean)
Reads per Run (million)
Yield per Run
Cost per Run ($)1
Cost per Mb
($)Paired-
end Application Strengths
Illumina HiSeq 2000 High Output 132 50 6,000 300 Gb2 18,725.00 0.06 Yes Gene expression; splice
junction detection; variant calling; fusions
Deep read counts for transcript
quantification
Illumina HiSeq 2500 High Output 132 50 6,000 300 Gb2 18,725.00 0.06 Yes as above as above
Illumina MiSeq v2 kit 39 250 30 7.5 Gb 982.75 0.13 Yes Splice junction detection; variant calling
Rapid transcript quantification and variant detection
Life Technologies PGM 318 chip 7.3 176 6 1.056 Gb 749.00 0.71 No Splice junction
detection; variant callingRapid transcript
quantification and variant detection
Life Technologies Proton Proton I
chip 2 to 4 81 70 5.67 Gb 834.00 0.15 No Gene expression; variant calling
Good read depth and length for transcript
quantification
Pacific Biosciences RS RS 0.5 to 2 1,289 0.03 38.67 Mb 136.38 3.53 No
Splice junction detection; full length
gene coverageExtended read
lengths
Roche 454Genome
Sequencer FLX+
20 686 1 686 Mb 5,985.00 8.72 No Splice junction detection Read length
1sequencing reaction reagents, at academic list price U.S. dollars, and does not include library preparation reagents, labor, data storage or analysis, equipment or maintenance; 2one sequencing run using 2 flowcells. Pacific Biosciences is calculated based on CCS reads; Gb: billion bases, Mb: million bases
Table 1. Summary of RNA-seq platforms as deployed in the ABRF-NGS Study.
I: Inter-‐pla(orm performance
Error models highly variable among pla(orms
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
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mism
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type
s per
cent
age
deletion insertion single base substitution
0%
2%
4%
6%
454ILM
NPACPGMPRO
platform
Inde
ls m
ismat
chs p
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ntag
e
a b
454: Roche 454 GS FLX+ ILMN: Illumina HiSeq 2000/2500 PAC: Pacific Biosciences RS I PGM: Ion Personal Genome Machine PRO: Ion Torrent Proton
Full length genebody coverage
Coverage across genebody (%)
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platform454,/013$&3*0352
sample$%&'
site&,3&�32/Y$/5901$)+6%
typeintact+56
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typesitesampleplatform
typesitesampleplatform
454 ILMN PAC PGM PRO
Note: PAC used CCS read only; PAC on average only cover 3k GENCODE genes.
Inter-‐site normalized gene expression varia$on: CV and R2
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0
50
100
150
200
454−A454−B
ILMN−A
ILMN−B
PAC−A1
PAC−A2
PAC−A3
PAC−B1
PAC−B2
PAC−B3
PGM−A
PGM−B
PRO−A
PRO−B
Coe
ffici
ent o
f var
iatio
n (%
)
454 ILMN PAC PGM PROintra−platform
Aintra−platform
Binter−platform
Ainter−platform
B
0.920.970.910.96
0.850.980.950.95
0.820.880.850.840.930.890.750.820.780.860.920.92
0.00
0.25
0.50
0.75
1.00
454ILM
NPG
MPRO
454ILM
NPG
MPRO454−ILM
N454−PG
M454−PRO
ILMN−PG
MILM
N−PRO
PGM−PRO
454−ILMN
454−PGM
454−PROILM
N−PG
MILM
N−PRO
PGM−PRO
platform
Cor
rela
tion
coef
ficie
nts
0.950.940.940.960.970.970.96
0.00
0.25
0.50
0.75
1.00
A−AHA−ASA−AR
AH−AS
AH−AR
AR−AS
B−BR
sample
Cor
rela
tion
coef
ficie
nts
●
●
50000
100000
150000
200000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
dete
cted
junc
tions
● 454 ILMN PAC PGM PRO
● ●A B
0
25
50
75
454
ILMN
PAC
PGM
PRO
platform
junc
tions
per
milli
on b
ases
A Bknown novel
0
20000
40000
60000
1 2 3 4 5 3 4 5
occurrence among platforms
junc
tions
A B
●●
10000
20000
30000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
dete
cted
gen
es
● 454 ILMN PAC PGM PRO
● ●A B
gd e f
a b c
ILMN with lowest inter-‐site CV ILMN with highest inter-‐site R2 ILMN-‐PRO & PRO-‐PGM with highest R2
Genes detec$on is log-‐linear; Junc$on detec$on is length-‐dependent
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0
50
100
150
200
454−A454−B
ILMN−A
ILMN−B
PAC−A1
PAC−A2
PAC−A3
PAC−B1
PAC−B2
PAC−B3
PGM−A
PGM−B
PRO−A
PRO−B
Coe
ffici
ent o
f var
iatio
n (%
)
454 ILMN PAC PGM PROintra−platform
Aintra−platform
Binter−platform
Ainter−platform
B
0.920.970.910.96
0.850.980.950.95
0.820.880.850.840.930.890.750.820.780.860.920.92
0.00
0.25
0.50
0.75
1.00
454ILM
NPG
MPR
O
454ILM
NPG
MPR
O454−ILM
N454−PG
M454−PR
OILM
N−PG
MILM
N−PR
OPG
M−PR
O454−ILM
N454−PG
M454−PR
OILM
N−PG
MILM
N−PR
OPG
M−PR
O
platform
Cor
rela
tion
coef
ficie
nts
0.950.940.940.960.970.970.96
0.00
0.25
0.50
0.75
1.00
A−AH
A−AS
A−AR
AH−AS
AH−AR
AR−AS
B−BR
sample
Cor
rela
tion
coef
ficie
nts
●
●
50000
100000
150000
2000009.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
dete
cted
junc
tions
● 454 ILMN PAC PGM PRO
● ●A B
0
25
50
75
454
ILMN
PAC
PGM
PRO
platform
junc
tions
per
milli
on b
ases
A Bknown novel
0
20000
40000
60000
1 2 3 4 5 3 4 5
occurrence among platforms
junc
tions
A B
●●
10000
20000
30000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
dete
cted
gen
es
● 454 ILMN PAC PGM PRO
● ●A B
gd e f
a b c
Junc$ons detec$on efficiency highly variable; agreement common
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0
50
100
150
200
454−A454−B
ILMN−A
ILMN−B
PAC−A1
PAC−A2
PAC−A3
PAC−B1
PAC−B2
PAC−B3
PGM−A
PGM−B
PRO−A
PRO−B
Coe
ffici
ent o
f var
iatio
n (%
)
454 ILMN PAC PGM PROintra−platform
Aintra−platform
Binter−platform
Ainter−platform
B
0.920.970.910.96
0.850.980.950.95
0.820.880.850.840.930.890.750.820.780.860.920.92
0.00
0.25
0.50
0.75
1.00
454ILM
NPG
MPRO
454ILM
NPG
MPRO454−ILM
N454−PG
M454−PRO
ILMN−PG
MILM
N−PRO
PGM−PRO
454−ILMN
454−PGM
454−PROILM
N−PG
MILM
N−PRO
PGM−PRO
platform
Cor
rela
tion
coef
ficie
nts
0.950.940.940.960.970.970.96
0.00
0.25
0.50
0.75
1.00
A−AHA−ASA−AR
AH−AS
AH−AR
AR−AS
B−BR
sample
Cor
rela
tion
coef
ficie
nts
●
●
50000
100000
150000
200000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
dete
cted
junc
tions
● 454 ILMN PAC PGM PRO
● ●A B
0
25
50
75
454
ILMN
PAC
PGM
PRO
platform
junc
tions
per
milli
on b
ases
A Bknown novel
0
20000
40000
60000
1 2 3 4 5 3 4 5
occurrence among platforms
junc
tions
A B
●●
10000
20000
30000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
dete
cted
gen
es
● 454 ILMN PAC PGM PRO
● ●A B
gd e f
a b c
Note: Only use a subset of reads among pla(orms to normalize the scale
Most of the known junc$ons are shared by at least 3 pla(orms
Sequencing depth is important to discover low abundance transcripts
a b
Inter-‐pla(orm differen$al gene expression show 88-‐97% agreement
Shared sets of greater than 1000 genes are indicated in red, 100-‐999 yellow, <100 blue. Unique DEGs: 454 -‐ 3.0% POLYA -‐ 9.2% RIBO -‐ 8.8% PRO -‐ 11.9% PGM -‐ 3.9%
Propor$onal venn diagrams don’t always add clarity, but they are prewy
RIBO
POLYA
454
PGMPRO
1766
482
2046
202
69
105
2199
1104484158
678 76 1828
740
1010
401133
559
62
2036595
2792
1187
406
1791
212
57
99
2151
II: Inter-‐protocol quan$fica$ons • Illumina
– Poly-‐A selec$ons (POLYA) – Ribo-‐deple$on (RIBO)
Gene regions distribu$on varies between protocols
0"
5,000"
10,000"
15,000"
20,000"
25,000"
≥"2" ≥"4" ≥"8" ≥"16" ≥"32" ≥"64" ≥"128"
PolyA"
RiboDep"
Overlap"
0"
5,000"
10,000"
15,000"
20,000"
25,000"
30,000"
≥"2" ≥"4" ≥"8" ≥"16" ≥"32" ≥"64" ≥"128"
PolyA"
RiboDep"
Overlap"
!
c"
a"
d"
Num
ber o
f UTR
s!
!b"
Num
ber o
f Gen
es!
# of Mapped Reads!# of Mapped Reads!
Map
ping
Distr
ibutio
n (%
)!
Freq
uenc
y!
FPKM Difference: log2(RiboDep - PolyA)!
0%
25%
50%
75%
100%
5,%2ï$ï�5,%2ï$ï�5,%2ï$ï�5,%2ï$ï�5,%2ï%ï�5,%2ï%ï�5,%2ï%ï�5,%2ï%ï�5,%2ï&ï�5,%2ï&ï�5,%2ï&ï�5,%2ï&ï�5,%2ï'ï�5,%2ï'ï�5,%2ï'ï�5,%2ï'ï�32/<$ï$ï�32/<$�$ï�32/<$ï$ï�32/<$ï$ï�32/<$ï%ï�32/<$ï%ï�32/<$ï%ï�32/<$ï%ï�32/<$ï&ï�32/<$ï&ï�32/<$ï&ï�32/<$ï&ï�32/<$ï'ï�32/<$ï'ï�32/<$ï'ï�32/<$ï'ï�
Sample
Map
ping
distri
butio
n (%
)intergenic mito exon �XWU 5utr intron ribo
log2 RIBO - log2 POLYA
POLYARIBOOverlap
POLYARIBOOverlap
a
d
Very similar gene measurements of genes (RPKM) between polyA & Ribo-‐deple$on
Ribo0>PolyA+ PolyA>Ribo0+
RPKM%difference%(polyA%–%ribo0)%
Num
ber%o
f%Gen
es%
ENSEMBL Gene ID Gene Symbol Description Length PolyA Reads
RiboDep Reads
PolyA FPKM
RiboDep FPKM FPKM Diff
ENSG00000210082 J01415.4 Mt_rRNA 1559 17040155 133679955 18568.31 200404.74 7181836.43ENSG00000211459 J01415.24 Mt_rRNA 954 2232892 14445488 3976.16 35389.28 731413.11ENSG00000202198 RN7SK misc_RNA 331 3303 2818202 16.95 19899.03 719882.08ENSG00000258486 RN7SL1 antisense 300 3061 520624 17.33 4055.93 74038.60ENSG00000202364 SNORD3A snoRNA 216 718 186779 5.65 2020.98 72015.33ENSG00000202538 RNU472 snRNA 141 168 102560 2.02 1699.99 71697.97ENSG00000251562 MALAT1 lincRNA 8708 437102 4931496 85.27 1323.57 71238.30ENSG00000199916 RMRP misc_RNA 264 148 83019 0.95 734.96 7734.00ENSG00000201098 RNY1 misc_RNA 113 172 19210 2.59 397.32 7394.73ENSG00000238741 SCARNA7 snoRNA 330 53 50182 0.27 355.40 7355.13ENSG00000200795 RNU471 snRNA 141 35 18724 0.42 310.36 7309.94ENSG00000200087 SNORA73B snoRNA 204 336 23778 2.80 272.42 7269.62ENSG00000252010 SCARNA5 snoRNA 276 29 25379 0.18 214.91 7214.73ENSG00000199568 RNU5A71 snRNA 116 38 10224 0.56 205.99 7205.44ENSG00000252481 SCARNA13 snoRNA 275 145 24034 0.90 204.26 7203.36ENSG00000212232 SNORD17 snoRNA 237 102 20571 0.73 202.86 7202.13ENSG00000207008 SNORA54 snoRNA 123 8 9655 0.11 183.46 7183.35ENSG00000200156 RNU5B71 snRNA 116 28 8301 0.41 167.25 7166.84ENSG00000209582 SNORA48 snoRNA 135 38 9272 0.48 160.52 7160.04ENSG00000239002 SCARNA10 snoRNA 330 19 21801 0.10 154.40 7154.30ENSG00000254911 SCARNA9 antisense 353 501 19842 2.41 131.37 7128.96ENSG00000230043 TMSB4XP6 pseudogene 135 0 6272 0.00 108.58 7108.58ENSG00000239039 SNORD13 snoRNA 104 0 4521 0.00 101.60 7101.60ENSG00000208892 SNORA49 snoRNA 136 7 5352 0.09 91.97 791.89ENSG00000223336 RNU276P snRNA 190 22 6365 0.20 78.29 778.10
Supplemental Table 3 - Top 25 Genes with Highest Enrichment from Ribo-Depletion Preparation
polyA favors high expressors • Gencode14 • Unique genes: 55889
0
5,000
10,000
15,000
20,000
25,000
30,000
≥ 2 ≥ 4 ≥ 8 ≥ 16 ≥ 32 ≥ 64 ≥ 128
PolyA
RiboZero
0
5,000
10,000
15,000
20,000
25,000
30,000
≥ 2 ≥ 4 ≥ 8 ≥ 16 ≥ 32 ≥ 64 ≥ 128 Read count
Genome-‐level Gene Counts Gene Overlap
Read count
High-‐overlap DEGs and comparable accuracy
FDR: 0.001 FDR: 0.01 FDR: 0.05
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
FC: 1.5FC: 2
A−BA−CA−DB−CB−DC−D
A−BA−CA−DB−CB−DC−D
A−BA−CA−DB−CB−DC−D
comparison
Prop
ortio
n
type POLYA RIBO Sharedlibrary: POLYA−POLYA library: RIBO−RIBO
0200040006000
0200040006000
0200040006000
0200040006000
0200040006000
0200040006000
FC: 1.5FC: 1.5
FC: 1.5FC: 2
FC: 2FC: 2
FDR: 0.001FDR: 0.01
FDR: 0.05FDR: 0.001
FDR: 0.01FDR: 0.05
A−B
A−C
A−D
B−C
B−D
C−D
A−B
A−C
A−D
B−C
B−D
C−D
Sample
Num
ber o
f DEG
s
type up downa" b"
c" A−B
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:1.5
RIBO:0.83POLYA:0.8
A−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:1.5
RIBO:0.86POLYA:0.89
A−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:1.5
RIBO:0.8POLYA:0.78
B−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:1.5
RIBO:0.86POLYA:0.83
B−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:1.5
RIBO:0.82POLYA:0.82
C−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:1.5
RIBO:0.83POLYA:0.81
A−B
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:1.5
RIBO:0.83POLYA:0.81
A−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:1.5
RIBO:0.87POLYA:0.89
A−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:1.5
RIBO:0.8POLYA:0.78
B−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:1.5
RIBO:0.85POLYA:0.82
B−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:1.5
RIBO:0.82POLYA:0.82
C−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:1.5
RIBO:0.82POLYA:0.8
A−B
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:1.5
RIBO:0.83POLYA:0.81
A−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:1.5
RIBO:0.87POLYA:0.9
A−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:1.5
RIBO:0.79POLYA:0.77
B−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:1.5
RIBO:0.85POLYA:0.83
B−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:1.5
RIBO:0.83POLYA:0.83
C−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:1.5
RIBO:0.83POLYA:0.81
A−B
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:2
RIBO:0.84POLYA:0.82
A−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:2
RIBO:0.91POLYA:0.95
A−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:2
RIBO:0.84POLYA:0.82
B−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:2
RIBO:0.88POLYA:0.85
B−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:2
RIBO:0.86POLYA:0.88
C−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.05; FC:2
RIBO:0.84POLYA:0.82
A−B
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:2
RIBO:0.83POLYA:0.82
A−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:2
RIBO:0.91POLYA:0.95
A−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:2
RIBO:0.83POLYA:0.82
B−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:2
RIBO:0.88POLYA:0.85
B−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:2
RIBO:0.86POLYA:0.88
C−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.01; FC:2
RIBO:0.84POLYA:0.82
A−B
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:2
RIBO:0.83POLYA:0.82
A−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:2
RIBO:0.92POLYA:0.95
A−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:2
RIBO:0.82POLYA:0.81
B−C
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:2
RIBO:0.87POLYA:0.85
B−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:2
RIBO:0.86POLYA:0.88
C−D
False positive rate (1−Specificity)
True
pos
itive
rate
(Sen
sitivi
ty)
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
FDR: 0.001; FC:2
RIBO:0.85POLYA:0.83
FC: 1.5FDR: 0.001
FC: 1.5FDR: 0.01
FC: 1.5FDR: 0.05
FC: 2FDR: 0.001
FC: 2FDR: 0.01
FC: 2FDR: 0.05
0.0
0.2
0.4
0.6
0.8
A−B
A−C
A−D
B−C
B−D
C−D
A−B
A−C
A−D
B−C
B−D
C−D
A−B
A−C
A−D
B−C
B−D
C−D
A−B
A−C
A−D
B−C
B−D
C−D
A−B
A−C
A−D
B−C
B−D
C−D
A−B
A−C
A−D
B−C
B−D
C−D
comp
Exte
rnal
val
idat
ion
(MCC
)
RIBO POLYAc
Using TaqMan as external valida$on 812 reac$ons
III: Degraded RNA?
0.0
0.5
1.0
1.5
2.0
0 10 20 30 40 50 60 70 80 90
100
Gene (%, 5'−3')
Cov
erag
e (%
)
UHR−RIN0 UHR−RIN3 UHR−RIN6 UHR−RIN9
Genetic variation (SNVs, indels, CNVs)!2!
Differential expression by gene, exon, splice isoform, allele, & transcript!3!
Predict gene fusions, polyA sites and alterations to miRNA binding sites!
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4!
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Entropy is usually a source of fear
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0
50
100
150
200
454−A454−B
ILMN−AILMN−BPAC−A1PAC−A2PAC−A3PAC−B1PAC−B2PAC−B3PGM−APGM−BPRO−APRO−B
Coeff
icien
t of v
ariati
on (%
)
454 ILMN PAC PGM PROintra−platform
Aintra−platform
Binter−platform
Ainter−platform
B
0.920.970.910.96
0.850.980.950.95
0.820.880.850.840.930.890.750.820.780.860.920.92
0.00
0.25
0.50
0.75
1.00
454ILMNPGMPRO
454ILMNPGMPRO454−ILMN454−PGM454−PRO
ILMN−PGMILMN−PROPGM−PRO454−ILMN454−PGM454−PRO
ILMN−PGMILMN−PROPGM−PRO
platform
Corre
lation
coeff
icien
ts
0.950.940.940.960.970.970.96
0.00
0.25
0.50
0.75
1.00
A−AHA−ASA−AR
AH−ASAH−ARAR−ASB−BR
sample
Corre
lation
coeff
icien
ts
●
●
50000
100000
150000
200000
9.0 9.5
10.0
10.5
11.0bases sequenced (log10)
detec
ted ju
nctio
ns
● 454 ILMN PAC PGM PRO
● ●A B
0
25
50
75
454
ILMN
PAC
PGM
PRO
platform
juncti
ons p
er mi
llion b
ases
A Bknown novel
0
20000
40000
60000
1 2 3 4 5 3 4 5
occurrence among platforms
juncti
ons
A B
●●
10000
20000
30000
9.0 9.5
10.0
10.5
11.0
bases sequenced (log10)
detec
ted ge
nes
● 454 ILMN PAC PGM PRO
● ●A B
gd e f
a b c
AB
A B
A
BCD
A BA BA BA B C D E F
AB A
B
AB
ABEF
Pacific Biosciences RS
Life TechnologiesIon PGM
Life TechnologiesIon Proton
(AR) (AS) (AH) (BR)
Illumina Ribo-‐deple$on protocol
0. Genomes
1. RNA Standards
2. Removing RNA-‐Seq Varia$on
3. Epitranscriptome
4. #JustSequenceIt
How Sequencing Quality affect Inter-‐site gene expression
quan$fica$on?
SEQC study
Ameliora$ng Inter-‐site Varia$on SEQC
samplesEach site has 4 replicates
ILM1 ILM2
Replicate 5
ILM3
Replicate 5
ILM4 ILM5
Replicate 5
ILM6
Replicate 5 libraries were vendor supplied
SEQC study
HiSeq 2000
Ameliora$ng Inter-‐site Varia$on SEQC
samplesEach site has 4 replicates
ILM1 ILM2
Replicate 5
ILM3
Replicate 5
ILM4 ILM5
Replicate 5
ILM6
Replicate 5 libraries were vendor supplied
SEQC study
Determine sequencing varia$on sources SEQC study
Nucleo$de frequency bias from library prepara$on
0.0
0.5
1.0
0 25 50 75 100
Percent of genebody (5'...>3')
Perc
enta
ge o
f rea
ds
1
5
ILM1
ILM2
ILM3
ILM4
ILM5
ILM6
A B C D
3.5
4.0
4.5IL
M1
ILM
2IL
M3
ILM
4IL
M5
ILM
6IL
M1
ILM
2IL
M3
ILM
4IL
M5
ILM
6IL
M1
ILM
2IL
M3
ILM
4IL
M5
ILM
6IL
M1
ILM
2IL
M3
ILM
4IL
M5
ILM
6
site
Max
imum
GC
read
%
1 2 3 4 5
● ● ● ● ● ●ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
A G C T
23
24
25
26
2720 40 60 80 10
020 40 60 80 10
020 40 60 80 10
020 40 60 80 10
0
Position in read
Nucle
otid
e fre
quen
cy (%
)
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
1 5
A B C D
31
33
35
37
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
site
Coef
ficie
nt o
f var
iatio
n(g
eneb
ody
%)
1 2 3 4 5
● ● ● ● ● ●ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
A B C D
2.0
2.5
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
ILM
1IL
M2
ILM
3IL
M4
ILM
5IL
M6
site
Aver
age
base
erro
r rat
e (%
)
1 2 3 4 5
● ● ● ● ● ●ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●
●●●●●●
●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●
●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●
●●
●●●●●
●●●●●●●●●●●
●●●●
●
●
●
●
●
●
●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●
●●●●●●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●
●●●●●●●
●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●
●●●●●●●●●
●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0
1
2
3
4
GC content (0% to 100%)
Perc
enta
ge o
f rea
ds
● ● ● ● ● ●ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
● 1 5
e
da b c
f
SEQC study Replicate 5 libraries
were vendor supplied
<-‐ 5th replicate of ILM2 and ILM3
<-‐ 1th replicate of ILM2
<-‐ 1th replicate of ILM3
Sample A vs. A = the Answer should be zero DEGs; SVA can remove false posi$ve DEGs
inter−site
0.97
20.
960
0.97
50.
973
0.97
60.
955
0.97
10.
968
0.96
90.
957
0.95
80.
958
0.97
10.
973
0.97
0
0.00
0.25
0.50
0.75
1.00
ILM
1−IL
M2
ILM
1−IL
M3
ILM
1−IL
M4
ILM
1−IL
M5
ILM
1−IL
M6
ILM
2−IL
M3
ILM
2−IL
M4
ILM
2−IL
M5
ILM
2−IL
M6
ILM
3−IL
M4
ILM
3−IL
M5
ILM
3−IL
M6
ILM
4−IL
M5
ILM
4−IL
M6
ILM
5−IL
M6
Site
Cor
rela
tion
coef
ficie
nts
−2 −1 0 1 2
−0.5
0.0
0.5
Dimension 1
Dim
ensi
on 2
AAAAAAAAA
AAAAAAAAAAAAA
A
AAAA BBB
BBBBB
B
BBBBBBBBB
BBBB
B
BBBB
CCCCCCCCCCCCCCCCCCCCCCCCCCC
DDDDDDDDDDDDDDDDDDDDDDDDDDD
●
●
●
●
●
●
ILM1ILM2ILM3ILM4ILM5ILM6
0250050007500
10000
0250050007500
10000
0250050007500
10000
0250050007500
10000
AB
CD
ILM
1−IL
M2
ILM
1−IL
M3
ILM
1−IL
M4
ILM
1−IL
M5
ILM
1−IL
M6
ILM
2−IL
M3
ILM
2−IL
M4
ILM
2−IL
M5
ILM
2−IL
M6
ILM
3−IL
M4
ILM
3−IL
M5
ILM
3−IL
M6
ILM
4−IL
M5
ILM
4−IL
M6
ILM
5−IL
M6
Comparison
Fals
e po
sitiv
e D
EGs
Without SVA With SVA
a
b
cIllumina
SEQC study Leek JT, Storey JD.PLoS Genet.2007
Compare the same sample across sites
SVA remove false posi$ve DEGs Validated by PGM data from ABRF-‐NGS study
inter−site
0.97
20.
960
0.97
50.
973
0.97
60.
955
0.97
10.
968
0.96
90.
957
0.95
80.
958
0.97
10.
973
0.97
0
0.00
0.25
0.50
0.75
1.00
ILM
1−IL
M2
ILM
1−IL
M3
ILM
1−IL
M4
ILM
1−IL
M5
ILM
1−IL
M6
ILM
2−IL
M3
ILM
2−IL
M4
ILM
2−IL
M5
ILM
2−IL
M6
ILM
3−IL
M4
ILM
3−IL
M5
ILM
3−IL
M6
ILM
4−IL
M5
ILM
4−IL
M6
ILM
5−IL
M6
Site
Cor
rela
tion
coef
ficie
nts
−2 −1 0 1 2
−0.5
0.0
0.5
Dimension 1
Dim
ensi
on 2
AAAAAAAAA
AAAAAAAAAAAAA
A
AAAA BBB
BBBBB
B
BBBBBBBBB
BBBB
B
BBBB
CCCCCCCCCCCCCCCCCCCCCCCCCCC
DDDDDDDDDDDDDDDDDDDDDDDDDDD
●
●
●
●
●
●
ILM1ILM2ILM3ILM4ILM5ILM6
0250050007500
10000
0250050007500
10000
0250050007500
10000
0250050007500
10000
AB
CD
ILM
1−IL
M2
ILM
1−IL
M3
ILM
1−IL
M4
ILM
1−IL
M5
ILM
1−IL
M6
ILM
2−IL
M3
ILM
2−IL
M4
ILM
2−IL
M5
ILM
2−IL
M6
ILM
3−IL
M4
ILM
3−IL
M5
ILM
3−IL
M6
ILM
4−IL
M5
ILM
4−IL
M6
ILM
5−IL
M6
Comparison
Fals
e po
sitiv
e D
EGs
Without SVA With SVA
a
b
cIllumina PGM
−2 −1 0 1
−0.5
0.0
0.5
1.0
1.5
Dimension 1
Dim
ensi
on 2
A.1A.2 A.3
A.4
B.1B.2B.3B.4
A.1
A.2
B.1B.2
A.1A.2
B.1B.2
●
●
●
PGM1PGM2PGM3
A B
4.5
5.0
5.5
6.0
PGM
1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3
siteM
axim
um G
C re
ad %
)
● ● ●PGM1 PGM2 PGM3
1 2 3 4
A B
56789
1011
PGM
1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3
siteAver
age
base
erro
r rat
e (%
)
● ● ●PGM1 PGM2 PGM3
1 2 3 4
A B
26
28
30
PGM
1
PGM
2
PGM
3
PGM
1
PGM
2
PGM
3
site
Coe
ffici
ent o
f var
iatio
n(g
eneb
ody
%)
● ● ●PGM1 PGM2 PGM3
1 2 3 4
comp: A comp: B
0
50
100
150
200
PGM
1−PG
M2
PGM
1−PG
M3
PGM
2−PG
M3
PGM
1−PG
M2
PGM
1−PG
M3
PGM
2−PG
M3
Comparison
Fals
e po
sitiv
e D
EGs
Without SVA With SVA
0
2500
5000
7500
10000
12500
PGM
1
PGM
2
PGM
3
Comparison
Num
ber o
f DEG
s
Without SVA With SVA
0.0
0.1
0.2
0.3
0.4
PGM
1
PGM
2
PGM
3
Site
MC
C
Without SVA With SVA
d e f g
a b c
SEQC study Li et al., in press, Nat. Biotech., 2014 ABRF-‐NGS study
NIST, SEQC, ABRF, ENCODE, others • Provide reference data
resources • Best prac$ces for
– gene quan$fica$on, – isoform characteriza$on, – dynamic range comparisons, – managing inter-‐site and intra-‐
site varia$on, – analysis pipelines, and – cross-‐pla(orm tes$ng of
transcriptome hypotheses • To address some other aspects
of RNA-‐seq, including – variant detec$on, – allele-‐specific expression, – RNA edi$ng, and gene fusions.
• And more …
0. Genomes
1. RNA Standards
2. Removing RNA-‐Seq Varia$on
3. Epitranscriptome
4. #JustSequenceIt
Which transcriptome is important?
All … RNA bases.
The four-‐base genome is just the beginning
The four-‐base genome is just the beginning
There are many RNA-‐mods as well:
Chuan He
Abbreviation Chemical0namem1acp3Ψ 1'methyl'3'(3'amino'3'carboxypropyl)5pseudouridine
m1A 1'methyladenosinem1G 1'methylguanosinem1I 1'methylinosinem1Ψ 1'methylpseudouridinem1Am 1,2''O'dimethyladenosinem1Gm 1,2''O'dimethylguanosinem1Im 1,2''O'dimethylinosinem2A 2'methyladenosine
ms2io6A 2'methylthio'N6'(cis'hydroxyisopentenyl)5adenosinems2hn6A 2'methylthio'N6'hydroxynorvalyl5carbamoyladenosinems2i6A 2'methylthio'N6'isopentenyladenosinems2m6A 2'methylthio'N6'methyladenosinems2t6A 2'methylthio'N6'threonyl5carbamoyladenosines2Um 2'thio'2''O'methyluridines2C 2'thiocytidines2U 2'thiouridineAm 2''O'methyladenosineCm 2''O'methylcytidineGm 2''O'methylguanosineIm 2''O'methylinosineΨm 2''O'methylpseudouridineUm 2''O'methyluridineAr(p) 2''O'ribosyladenosine5(phosphate)Gr(p) 2''O'ribosylguanosine5(phosphate)acp3U 3'(3'amino'3'carboxypropyl)uridinem3C 3'methylcytidinem3Ψ 3'methylpseudouridinem3U 3'methyluridinem3Um 3,2''O'dimethyluridineimG'14 4'demethylwyosines4U 4'thiouridine
chm5U 5'(carboxyhydroxymethyl)uridinemchm5U 5'(carboxyhydroxymethyl)uridine5methyl5esterinm5s2U 5'(isopentenylaminomethyl)'52'thiouridineinm5Um 5'(isopentenylaminomethyl)'52''O'methyluridineinm5U 5'(isopentenylaminomethyl)uridinenm5s2U 5'aminomethyl'2'thiouridinencm5Um 5'carbamoylmethyl'2''O'methyluridinencm5U 5'carbamoylmethyluridine
cmnm5Um 5'carboxymethylaminomethyl'52''O'methyluridinecmnm5s2U 5'carboxymethylaminomethyl'2'thiouridinecmnm5U 5'carboxymethylaminomethyluridinecm5U 5'carboxymethyluridinef5Cm 5'formyl'2''O'methylcytidinef5C 5'formylcytidinehm5C 5'hydroxymethylcytidineho5U 5'hydroxyuridine
mcm5s2U 5'methoxycarbonylmethyl'2'thiouridinemcm5Um 5'methoxycarbonylmethyl'2''O'methyluridinemcm5U 5'methoxycarbonylmethyluridinemo5U 5'methoxyuridinem5s2U 5'methyl'2'thiouridine
mnm5se2U 5'methylaminomethyl'2'selenouridinemnm5s2U 5'methylaminomethyl'2'thiouridinemnm5U 5'methylaminomethyluridinem5C 5'methylcytidinem5D 5'methyldihydrouridinem5U 5'methyluridineτm5s2U 5'taurinomethyl'2'thiouridineτm5U 5'taurinomethyluridinem5Cm 5,2''O'dimethylcytidinem5Um 5,2''O'dimethyluridinepreQ1 7'aminomethyl'7'deazaguanosinepreQ0 7'cyano'7'deazaguanosinem7G 7'methylguanosineG+ archaeosineD dihydrouridineoQ epoxyqueuosinegalQ galactosyl'queuosineOHyW hydroxywybutosine
I inosineimG2 isowyosinek2C lysidine
manQ mannosyl'queuosinemimG methylwyosinem2G N2'methylguanosinem2Gm N2,2''O'dimethylguanosinem2,7G N2,7'dimethylguanosinem2,7Gm N2,7,2''O'trimethylguanosinem2
2G N2,N2'dimethylguanosinem2
2Gm N2,N2,2''O'trimethylguanosinem2,2,7G N2,N2,7'trimethylguanosineac4Cm N4'acetyl'2''O'methylcytidineac4C N4'acetylcytidinem4C N4'methylcytidinem4Cm N4,2''O'dimethylcytidinem4
2Cm N4,N4,2''O'trimethylcytidineio6A N6'(cis'hydroxyisopentenyl)adenosineac6A N6'acetyladenosineg6A N6'glycinylcarbamoyladenosinehn6A N6'hydroxynorvalylcarbamoyladenosinei6A N6'isopentenyladenosine
m6t6A N6'methyl'N6'threonylcarbamoyladenosinem6A N6'methyladenosinet6A N6'threonylcarbamoyladenosine
m6Am N6,2''O'dimethyladenosinem6
2A N6,N6'dimethyladenosinem6
2Am N6,N6,2''O'trimethyladenosineo2yW peroxywybutosineΨ pseudouridineQ queuosine
OHyW undermodified5hydroxywybutosinecmo5U uridine55'oxyacetic5acidmcmo5U uridine55'oxyacetic5acid5methyl5ester
yW wybutosineimG wyosine
Table515'5List5of5Base5Modifications5Covered5by5Claims
m6A is just 1 of the 110 known RNA modifica$ons from the RNA Modifica$on Database
m6A marks are widespread in cancer cells’ RNA
Meyer et al., Cell, 2012
A new method: MeRIP-‐Seq
Meyer et al., Cell, 2012
Conserva$ons of signal and sites in orthologous genes
Meyer et al., Cell, 2012
Peaks also show strong conserva$on
Meyer, 2012
Many puta$ve roles for m6A in RNA
Paz-‐Yaakov et al, 2010
Saletore, Chen-‐Kiang, and Mason, RNA Biology, 2013.
RNA modifica$ons give a new layer of cellular regula$on
N
NN
N
NH2
O
OHO
O
RNA
RNA
N
NN
N
HN
O
OHO
ORNA
RNA
H2C
OH
N
NN
N
HN
O
OHO
ORNA
RNA
CH
O
N
NN
N
HN
O
OHO
ORNA
RNA
COH
O
N
NN
N
HN
O
OHO
O
RNA
RNA
CH3
A m6A
ca6A f6A hm6A
O
H HO
H OH
H2O
Mettl3
FTO/ALKBH5
O2, FeII, -KG
FTO/ALKBH5
O2, FeII, -KG
Li and Mason, ARGHG, 2014
New layer to study!
Direct Detec$on of Methyla$on on PacBio
70.5 71.0 71.5 72.0 72.5 73.0 73.5 74.0 74.50
100
200
300
400
Fluo
resc
ence
inte
nsity
(a.u
.)
Time (s)
104.5 105.0 105.5 106.0 106.5 107.0 107.5 108.0 108.50
100
200
300
400
Fluo
resc
ence
inte
nsity
(a.u
.)
Time (s)
C
T G A T C G T A C
mA A G T C T A A
G C C A A A A
Approach: Kine$c detec$on of methylated bases during SMRT DNA sequencing
Example: N6-‐methyladenosine (mA)
SMRT RNA Sequencing – Assay Design
I. RNA template (purple) is hybridized to a DNA primer (orange) and immobilized on the bottom of a zero-mode waveguide (ZMW) in the presence of fluorescently-labeled nucleotide analogues.
II. After the addition of HIV RT to the solution, the polymerase binds to the RNA template at the 3’-end of DNA primer.
III. The first nucleotide analogue can bind to the active site of the polymerase resulting in a fluorescent pulse with a color characteristic to the type of nucleotide (i.e. A, C, G, or T). With HIV RT, the rates of nucleotide binding, dissociation and incorporation have not been optimized to a state where each binding event would result in an incorporation event (image IV.). Consequently, nucleotide analogues often dissociate before they can be incorporated resulting in a series of like pulses before the polymerase can translocate to the next position and bind the nucleotide analogue complementary to the next nucleotide in RNA template.
IV. Incorporation of the nucleotide and translocation to the next nucleotide on RNA template.
First demonstration SMRT RNA Sequencing – Expected Signal
A. Example RNA template is shown in black. DNA primer is shown in orange. The first cDNA strand synthesized by HIV RT during SMRT RNA Sequencing is color-coded (A – yellow, C – red, G – gree, T – blue). The first cDNA strand is complementary to the unhybridized section of the example RNA template and can be used to determine the sequence of that section.
B. A typical time trace obtained in a ZMW with RNA template immobilized on the bottom surface. One can observe a succession of blocks of like-colored pulses corresponding to the sequence of cDNA strand (blocks are indicated by lines above the trace).
C. As mentioned on Slide 3 – Point III., due to the kinetics of the current HIV RT, we observe multiple binding events at each RNA template position before nucleotide incorporation and translocation to the next position.
A
B
C
Saletore et al., Genome Biology, 2012
m6A is dis$nct from A
Saletore et al., Genome Biology, 2012
Epigenome
Epitranscriptome
Epiproteome (PTM)
DNA RNA Protein transcrip$on transla$on
RT
ACGT
viRNA
I N H E R I T A N C E or T R A N S M I S S I O N
prions
iDNA
mC,hmC, 8oxoG, m6A
(sn/sno/g)RNA (t/r/tm)RNA
(lnc/nc/mi/piwi/si/vi)RNA
scRNA
RbP
Ribozymes Prions
iRNA iProtein
mC,hmC, 8oxoG, m6A, … m6A, 5’G,polyA, … Acet, Phos, Citr, SUMO, …
from Saletore et al., Genome Biology, 2012
0. Genomes
1. RNA Standards
2. Removing RNA-‐Seq Varia$on
3. Epitranscriptome
4. #JustSequenceIt
Which genome is important?
All … Cells.
−5 0 5
−6−4
−20
24
Comp.1
Com
p.2 C01
C02
C03
C04C05
C06C07
C08
C09
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32C33
C34
C35
C36
C37
C38
C39
C40
C41C42
C43
C44
C45
C46
C47
C48
C49
C50
C51
C52
C53
C54
C55
C56
C57
C58C59
C60
C61
C62
C63
C64C65
C66
C67
C68
C69
C70
C71
C72
C73
C74
C75
C76
C77
C78
C79C80C81
C82
C83
C84
C85C86
C87
C88
C89
C90
C91
C92
C93
C94
C95
C96
Principal Component 1
Principal Com
pone
nt 2
C05
C12
C29
C33
C44
C78
C88
C35
C03
C19
C16
C20
C72
C38
C83
C01
C67
C60
C42
C52
C32
C86
C02
C91
C09
C46
C80
C11
C58
C64
C56
C77
C06
C23
C15
C90
C62
C65
C43
C48
C84
C34
C53
C69
C37
C82
C28
C76
C87
C61
C92
C85
C47
C96
C50
C59
C39
C66
C93
C26
C27
C54
C71
C07
C22
C14
C81
C08
C41
C55
C73
C70
C51
C75
C24
C94
C25
C10
C17
C04
C68
C13
C63
C18
C57
C40
C74
C36
C79
C30
C49
C21
C45
C95
C31
C89
MGST3URODPSMA5PSMB2DHRS3L.XKR8HBXIPS100A11R.spk7midRPS27PSMD4MAST2YBX1SLC39A1UBR4B4GALT3S.8CNIH4NCSTNSRMRNF220WDR77TRAPPC3RPL11VPS72ILF2NUDCS.4PARP1PRDX6BCRABLHIST2H2BEYARSPI4KBUBE2Q1VAMP3KIFAP3SF3A3TPRDIRAS3GNPATZMPSTE24HSD17B7IPO13KIF2CCAPZA1FAF1MRPL9SDHBAHCYL1NOC2LHMGN2L.AXIN2GNL2SFRS4CREG1ETV1SOX5MEAF6S.2DTLMARCKSL1MRPS15JAK1GNB1S.10PTCH1ALDH9A1RPA2KHDRBS1SFPQARPC5TXNIPUFC1RABGGTBS.5KIAA0907CDC20PRPF3KPNA6RPL22ACTBWASF2ATP5F1S.6KIF14LRRC47
0
5
10
15
20
25
30
Check Cells
Sort Cells, Load plate
Library prep. & RNA-‐Seq
Lyse, cDNA
AML-‐related genes’ expression (RPKM
)
Purified Cell (1-‐96)
Single-‐cell RNA-‐seq reveals extraordinary heterogeneity
Leukemia Sample
Which genome is important?
All … Species.
nhprtr.org Pipes, Li, Bozinoski et al., NAR, 2013
Prosimian
Human
Chimpanzee
Gorilla
Rhesus Macaque
Japanese Macaque
Cynomolgus Macaque
Pig-tailed Macaque
Vervet
Sooty Mangabey
Common Marmoset
Squirrel Monkey
Ring-tailed Lemur
Mouse Lemur
Olive Baboon
New World Monkeys
Old World
Hominoids
~70 mya
35-40
23-25
6
8
9
10
14 species selected for phylogenetic & pharmacogenomic significance:
• Samples from Washington, Oregon, Yerkes, and Southwestern Na$onal Primate Research Centers; the Duke Lemur Center; the MD Anderson Cancer Center, and Covance, Inc.
21 Matching “BodyMap” Dissected Tissues Brain Immune/Sex Soma
Cerebellum Bone Marrow Adipose
Frontal Cortex Lymph Node Colon
Hippocampus Spleen Heart
Hypothalamus Thymus Kidney
Pituitary Thyroid Liver
Temporal Lobe Ovary Lung
Tes$s Skeletal Muscle
Whole Blood Phase I (2010 -‐2012) RNA extracted from each $ssue, pooled, sequenced on two FCs (HS2K, GAIIx) for overall annota$on.
Phase II (2012 – 2014): RNA extracted from 12 $ssues, individually sequenced (1/2 lane HS2K), for $ssue-‐specific annota$on.
AceView Genes Expressed
Species /isolate
Number of genes
expressed
Known human genes
Un-‐annotated human genes
BAB 52065 22376 29689
CMC 50073 21916 28157
CMM 50155 21974 28181
JMI 49253 21785 27468
PTM 50863 22084 28779
RMC 49845 21888 27957
RMI 49922 21913 28009
Jean Thierry-‐Mieg
Species Library # input sequences (billion)
# con;gs Total length (Mb)
N25 (bp)
N50 (bp)
N75 (bp)
Longest con;g (bp)
Baboon TOT 0.149 658,581 281 1,029 434 276 35,170
UDG 1.543 1,131,951 844 3,534 1,368 479 131,395
Chimpanzee UDG 1.465 987,615 1,433 6,356 3,806 1,666 47,873
Cynomologus Macaque Chinese
RNA 1.676 911,282 864 4,769 2,316 706 59,032
UDG 1.630 990,604 1,055 5,479 2,822 875 122,916
Cynomolgus Macaque Mauri;an
RNA 1.078 1,142,531 929 4,368 1,813 525 30,364
TOT 0.166 526,723 200 657 377 266 36,976
UDG 1.177 1,015,657 834 4,360 1,927 532 22,239
Gorilla UDG 1.256 732,336 1,122 5,878 3,680 1,804 33,526
Japanese Macaque RNA 1.863 703,246 738 5,010 2,687 898 21,620
Marmoset TOT 0.253 332,782 118 545 357 261 14,561
UDG 1.659 814,235 476 2,206 785 359 122,518
Pig-‐tailed Macaque RNA 1.725 969,993 1,044 5,189 2,807 916 25,056
UDG 1.573 1,301,087 1,222 5,015 2,265 668 32,637
Rhesus Macaque Chinese
RNA 1.210 923,017 836 4,694 2,230 639 32,796
UDG 1.310 969,421 850 4,638 2,114 594 34,020
Rhesus Macaque Indian
RNA 3.200 703,246 738 5,010 2,687 898 21,620
UDG 1.410 1,051,149 833 3,966 1,644 519 68,269
Ring-‐tailed Lemur UDG 1.403 611,678 822 6,169 3,630 1,468 33,401
Sooty Mangabey UDG 1.635 1,188,472 1,466 6,431 3,483 1,116 30,331
Chimpanzee de novo assembled transcriptome fully recovers most genes in current annota$on Chimpanzee De Novo Assembled Transcriptome
Proportion of gene covered by transcriptome
Num
ber o
f gen
es
0.0 0.2 0.4 0.6 0.8 1.0
02000
4000
6000
8000
10000
12000
Which genome is important?
All … Interac$ng Elements.
We need more Longitudinal, Integra$ve Systems Biology
ScoA Kelly – ISS for one year
Mark Kelly – Earth control
Telomere Length
DNA Muta$ons & Structural Varia$on
DNA Hydroxy-‐methyla$on
Chroma$n
RNA expression & RNA Methyla$on Proteomics
An$body Titers
Cytokines
DNA Methyla$on
B-‐cells / T-‐cells
Targeted and Global Metabolomics Microbiome
Cogni$on
Vasculature
These People are Awesome @mason_lab
Gratitude to Many People and Places
Illumina Gary Schroth Marc Van Oene
Univ. Chicago Yoav Gilad
Yale University Nenad Sestan Sherman Weissman FDA/SEQC Leming Shi NIH/UDP/NCBI Jean and Danielle Thierry-Mieg William Gahl
Baylor Jeff Rogers
MSKCC Christina Leslie Ross Levine
PKI/Geospiza Todd Smith
Mason Lab Noah Alexander Marjan Bozinoski Dhruva Chandramohan Sagar Chhangawala Jorge Gandara Fran Garrett-Bakelman Dyala Jaroudi Sheng Li Cem Meyden Lenore Pipes Darryl Reeves Yogesh Saletore Priyanka Vijay Paul Zumbo
Cornell/WCMC Jason Banfelder Scott Blanchard Selina Chen-Kiang Olivier Elemento Yariv Houvras Samie Jaffrey Ari Melnick Margaret Ross Adam Siepel Epigenomics Core
Katze Lab/NHPRT Michael Katze Bob Palermo Xinxia Pan
Icahn/MSSM Eric Schadt, Andrew Kasarskis, Joel Dudley, Ali Bashir, Bobby Sebra
NASA Kosuke Fujishima Lynn Rothschild
ABRF George Grills Scott Tighe Don Baldwin
UMMS Maria E Figueroa
AMNH George Amato Mark Sidall
U-Minnesota Ran Blekhman
@mason_lab
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