20140710 Christopher Mason ERCC 2.0 Workshop

1. 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

2. 0.
Background
1. RNA
Standards
2. Removing
RNA-­‐Seq
Varia$on
3. Epitranscriptome
4. #JustSequenceIt

3. Exome:
2%
of
the
genome?

4.
Human Genome Organization
from
Mason,
State,
and
Moldin,
Kaplan
&
Sadock’s
Comprehensive
Textbook
of
Psychiatry,
2009

5. 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.”

6. “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.”

7. GENCODE
annota;on
(v19)
July
10th,
2014
Coding
genes:
20,345
Noncoding
genes:
22,883
Psuedogenes:
14,206
Other
genes:
516
57,820
total

8. RNA-­‐Seq
and
all
its
flavors
create
excitement

9. 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 "
Reads/bp"
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

10. But!
There
is
some
noise

11. What
is
the
source
of
the
wiggles?
Genes
ESTs
Liver
Kidney
Adult Brain
Fetal Hippocampus
Fetal Amygdala

12. 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

13. Which
type
of
RNA?
Type Abbreviation Function Organisms
7SK$RNA$ 7SK$ negatively$regulating$CDK9/cyclin$T$complex$ metazoans
Signal$recognition$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$organisms
PiwiRinteracting$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 Metazoans
Small$interfering$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$immunity C$elegans
Vault$RNA$ vRNA$ selfRpropagation Expulsion$of$xenobiotics
Y$RNA$ yRNA RNA$processing,$DNA$replication$ Animals$

14. And
-­‐
which
one
do
we
use?
Technologies
Bifurcate
into
two
main
realms:
Platform Instrument Template0Preparation Chemistry Avearge0Length Longest0Read
Illumina 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 Longest0Read
IBM<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

15. 0.
Genomes
1. RNA
Standards
2. Removing
RNA-­‐Seq
Varia$on
3. Epitranscriptome
4. #JustSequenceIt

17. 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

18. Es$mate
of
False
Junc$on
Rate
5’
3’
Exon-­‐Edge
DB
(3’-­‐5’
junc$ons)
2 3 41Exon # :
False
Exon-­‐Edge
DB
(3’-­‐3’
junc$ons)
False
Exon-­‐Edge
DB
(5’-­‐5’
junc$ons)
False
Exon-­‐Edge
DB
(5’-­‐3’
junc$ons)
5’
3’
5’
3’
5’
3’

19. 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

20. 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.

21. Samples
of
the
MAQC,
SEQC
and
ABRF-­‐NGS
Study

22. ß
SEQC
samples
(FDA)
A
B
A B
A
B
C
D
A B
A B
A BA B C D E F
A
B
A
B
A
B
A
B
E
F
Pacific Biosciences
RS
Life Technologies
Ion PGM
Life Technologies
Ion Proton
A:
10
cancer
cell
lines
B:
Brain
$ssues

23. A
B
A B
A
B
C
D
A B
A B
A BA B C D E F
A
B
A
B
A
B
A
B
E
F
Pacific Biosciences
RS
Life Technologies
Ion PGM
Life Technologies
Ion Proton
Cross-­‐pla(orm
experimental
design

24. A
B
A B
A
B
C
D
A B
A B
A BA B C D E F
A
B
A
B
A
B
A
B
E
F
Pacific Biosciences
RS
Life Technologies
Ion PGM
Life Technologies
Ion Proton
Cross-­‐Site
experimental
design

25. A
B
A B
A
B
C
D
A B
A B
A BA B C D E F
A
B
A
B
A
B
A
B
E
F
Pacific Biosciences
RS
Life Technologies
Ion PGM
Life Technologies
Ion Proton
Cross-­‐Protocol
experimental
design

26. 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 calling
Rapid 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 coverage
Extended read
lengths
Roche 454
Genome
Sequencer
FLX+
20 686 1 686 Mb 5,985.00 8.72 No Splice junction detection Read length
1
sequencing reaction reagents, at academic list price U.S. dollars, and does not include library preparation reagents, labor, data storage or analysis, equipment
or maintenance; 2
one 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.

27. I:
Inter-­‐pla(orm
performance

28. Error
models
highly
variable
among
pla(orms
0%
2%
4%
6%
454
ILMN
PAC
PGM
PRO
platform
Indelsmismatchspercentage
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

29. Full
length
genebody
coverage
Coverage across genebody (%)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
5'
platform
454
sample
site
Y
type
intact
type
site
sample
platformm
454
ILMN
PAC
PGM
PRO
Note:
PAC
used
CCS
read
only;
PAC
on
average
only
cover
3k
GENCODE
genes.

30. Inter-­‐site
normalized
gene
expression
varia$on:
CV
and
R2
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0
50
100
150
200
454−A
454−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
Coefficientofvariation(%)
454 ILMN PAC PGM PRO
intra−platform
A
intra−platform
B
inter−platform
A
inter−platform
B
0.92
0.97
0.91
0.96
0.85
0.98
0.95
0.95
0.82
0.88
0.85
0.84
0.93
0.89
0.75
0.82
0.78
0.86
0.92
0.92
0.00
0.25
0.50
0.75
1.00
454
ILMN
PGM
PRO
454
ILMN
PGM
PRO
454−ILMN
454−PGM
454−PRO
ILMN−PGM
ILMN−PRO
PGM−PRO
454−ILMN
454−PGM
454−PRO
ILMN−PGM
ILMN−PRO
PGM−PRO
platform
Correlationcoefficients
● 454 ILMN PAC PGM PRO
● ●A B
es
A B● 454 ILMN PAC PGM PRO
● ●A B
gd e f
a b
ILMN
with
lowest
inter-­‐site
CV
ILMN
with
highest
inter-­‐site
R2
ILMN-­‐PRO
&
PRO-­‐PGM
with
highest
R2

31. Genes
detec$on
is
log-­‐linear;
Junc$on
detec$on
is
length-­‐dependent
454−A
454−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
454
LMN
PGM
●
●
50000
100000
150000
200000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
detectedjunctions
● 454 ILMN PAC PGM PRO
● ●A B
●●
10000
20000
30000
9.0
9.5
10.0
10.5
11.0
bases sequenced (log10)
detectedgenes
● 454 ILMN PAC PGM PRO
● ●A B
d e

32. Junc$ons
detec$on
efficiency
highly
variable;
agreement
common
PRO
454
LMN
PGM
PRO
454−ILMN
454−PGM
454−PRO
LMN−PGM
LMN−PRO
PGM−PRO
454−ILMN
454−PGM
454−PRO
LMN−PGM
LMN−PRO
PGM−PRO
platform
A−AH
A−AS
A−AR
AH−AS
AH−AR
AR−AS
B−BR
sample
0
25
50
75
454
ILMN
PAC
PGM
PRO
platform
junctionspermillionbases
A B
known novel
0
20000
40000
60000
1
2
3
4
5
3
4
5
occurrence among platforms
junctions
A Bgf
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

33. Sequencing
depth
is
important
to
discover
low
abundance
transcripts

34. 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%

35. Propor$onal
venn
diagrams
don’t
always
add
clarity,
but
they
are
prewy
RIBO
POLYA
454
PGM
PRO
1766
482
2046
202
69
105
2199
1104
484158
678 76 1828
740
1010
401
133
559
62
20
36595
2792
1187
406
1791
212
57
99
2151

36. II:
Inter-­‐protocol
quan$fica$ons
• Illumina
– Poly-­‐A
selec$ons
(POLYA)
– Ribo-­‐deple$on
(RIBO)

37. Gene
regions
distribu$on
varies
between
protocols
a" b"MappingDistribution(%)!
0%
25%
50%
75%
100%
Sample
Mappingdistribution(%) intergenic mito exon 5utr intron ribo
a

38. Very
similar
gene
measurements
of
genes
(RPKM)
between
polyA
&
Ribo-­‐deple$on
Ribo0>PolyA+ PolyA>Ribo0+
RPKM%difference%(polyA%–%ribo0)%
Number%of%Genes%

39. 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 7181836.43
ENSG00000211459 J01415.24 Mt_rRNA 954 2232892 14445488 3976.16 35389.28 731413.11
ENSG00000202198 RN7SK misc_RNA 331 3303 2818202 16.95 19899.03 719882.08
ENSG00000258486 RN7SL1 antisense 300 3061 520624 17.33 4055.93 74038.60
ENSG00000202364 SNORD3A snoRNA 216 718 186779 5.65 2020.98 72015.33
ENSG00000202538 RNU472 snRNA 141 168 102560 2.02 1699.99 71697.97
ENSG00000251562 MALAT1 lincRNA 8708 437102 4931496 85.27 1323.57 71238.30
ENSG00000199916 RMRP misc_RNA 264 148 83019 0.95 734.96 7734.00
ENSG00000201098 RNY1 misc_RNA 113 172 19210 2.59 397.32 7394.73
ENSG00000238741 SCARNA7 snoRNA 330 53 50182 0.27 355.40 7355.13
ENSG00000200795 RNU471 snRNA 141 35 18724 0.42 310.36 7309.94
ENSG00000200087 SNORA73B snoRNA 204 336 23778 2.80 272.42 7269.62
ENSG00000252010 SCARNA5 snoRNA 276 29 25379 0.18 214.91 7214.73
ENSG00000199568 RNU5A71 snRNA 116 38 10224 0.56 205.99 7205.44
ENSG00000252481 SCARNA13 snoRNA 275 145 24034 0.90 204.26 7203.36
ENSG00000212232 SNORD17 snoRNA 237 102 20571 0.73 202.86 7202.13
ENSG00000207008 SNORA54 snoRNA 123 8 9655 0.11 183.46 7183.35
ENSG00000200156 RNU5B71 snRNA 116 28 8301 0.41 167.25 7166.84
ENSG00000209582 SNORA48 snoRNA 135 38 9272 0.48 160.52 7160.04
ENSG00000239002 SCARNA10 snoRNA 330 19 21801 0.10 154.40 7154.30
ENSG00000254911 SCARNA9 antisense 353 501 19842 2.41 131.37 7128.96
ENSG00000230043 TMSB4XP6 pseudogene 135 0 6272 0.00 108.58 7108.58
ENSG00000239039 SNORD13 snoRNA 104 0 4521 0.00 101.60 7101.60
ENSG00000208892 SNORA49 snoRNA 136 7 5352 0.09 91.97 791.89
ENSG00000223336 RNU276P snRNA 190 22 6365 0.20 78.29 778.10
Supplemental Table 3 - Top 25 Genes with Highest Enrichment from Ribo-Depletion Preparation

40. 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

41. 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−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
comparison
Proportion
type POLYA RIBO Shared
b"
B−C
itivity)
.81.0
FDR: 0.05; FC:1.5
B−D
itivity)
.81.0
FDR: 0.05; FC:1.5
C−D
itivity)
.81.0
FDR: 0.05; FC:1.5
FC: 1.5
FDR: 0.001
FC: 1.5
FDR: 0.01
FC: 1.5
FDR: 0.05
FC: 2
FDR: 0.001
FC: 2
FDR: 0.01
FC: 2
FDR: 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
Externalvalidation(MCC)
RIBO POLYAc
Using
TaqMan
as
external
valida$on
812
reac$ons

42. III:
Degraded
RNA?

43. 0.0
0.5
1.0
1.5
2.0
0
10
20
30
40
50
60
70
80
90
100
Gene (%, 5'−3')
Coverage(%)
UHR−RIN0 UHR−RIN3 UHR−RIN6 UHR−RIN9

44. 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!
5!
!
Viruses/Bacteria/Other"
De Novo Assembly of Remaining Reads!
TCTGCTTTAGGATAGATCGATAGCTAGTTCAT
CTGCTTTAGGATAGATCGATAGCTAGTTCATC!
TCTGCTTTAGGATAGATCGATAGCTAGTTCAT!
6!
reads"
alignments"
Sorted
BAMs"
annotations"
queries "
Reads/bp"
Alignment on HPC nodes"
References!
hg19"
RefSeq"
miRBase"
rRNA"
Adapters"
Find ncRNAs and new genes!
4!
1!
Sequencing Data"
Many
areas
could
be
impacted

45. Entropy
is
usually
a
source
of
fear

46. "FEAR is
the
main
source
of
superstition,
and
one
of
the
main
sources
of
cruelty.
To
conquer
fear
is
the
beginning
of
wisdom."
–
Bertrand
Russell
HBR−RIN9
HBR−RIN0
UHR−RIN0
UHR−RIN3
UHR−RIN9
UHR−RIN6
HBR−RIN9
HBR−RIN0
UHR−RIN0
UHR−RIN3
UHR−RIN9
UHR−RIN6
0.6 0.7 0.8 0.9 1
Value
Color Key

47. Can
we
remove
supers$$on?
HEAT
99oC
for
10
minutes
SONICATION
6
x
55
seconds
at
5%DC,
I
intensity
3
100
c/b
RNAse
RNAse
A
1
ng/uL
Un$l
RIN
<2

48. Degraded
RNA
looks
great!
Coverage across genebody (%)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
5'
platform
454
sample
site
Y
type
intact
type
site
sample
platformm
454
ILMN
PAC
PGM
PRO

49. Degraded
RNA
highly
correlates
with
intact
RNA
gene
expression
atform intra−platform
B
inter−platform
A
inter−platform
B
0.96
0.85
0.98
0.95
0.95
0.82
0.88
0.85
0.84
0.93
0.89
0.75
0.82
0.78
0.86
0.92
0.92
PRO
454
ILMN
PGM
PRO
454−ILMN
454−PGM
454−PRO
ILMN−PGM
ILMN−PRO
PGM−PRO
454−ILMN
454−PGM
454−PRO
ILMN−PGM
ILMN−PRO
PGM−PRO
platform
0.95
0.94
0.94
0.96
0.97
0.97
0.96
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
Correlationcoefficients
c
B
A B
A
B
C
D
A
B
Pacific Biosciences
RS
Life Tech
Ion P
(AR)
(AS)
(AH)
(BR)
Illumina
Ribo-­‐deple$on
protocol

50. 0.
Genomes
1. RNA
Standards
2. Removing
RNA-­‐Seq
Varia$on
3. Epitranscriptome
4. #JustSequenceIt

51. How
Sequencing
Quality
affect
Inter-­‐site
gene
expression
quan$fica$on?
SEQC
study

52. Ameliora$ng
Inter-­‐site
Varia$on
SEQC
samples
Each 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

53. Ameliora$ng
Inter-­‐site
Varia$on
SEQC
samples
Each site has
4 replicates
ILM1 ILM2
Replicate 5
ILM3
Replicate 5
ILM4 ILM5
Replicate 5
ILM6
Replicate 5 libraries
were vendor supplied
SEQC
study

54. Determine
sequencing
varia$on
sources
SEQC
study

55. Nucleo$de
frequency
bias
from
library
prepara$on
●M5 ILM6
A G C T
23
24
25
26
27
20
40
60
80
100
20
40
60
80
100
20
40
60
80
100
20
40
60
80
100
Position in read
Nucleotidefrequency(%)
ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
1 5
site
1 2 3 4 5
● ● ● ● ● ●ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
site
1 2 3 4 5
● ● ● ● ● ●ILM1 ILM2 ILM3 ILM4 ILM5 ILM6
f
SEQC
study
Replicate 5 libraries
were vendor supplied
<-­‐
5th
replicate
of
ILM2
and
ILM3
<-­‐
1th
replicate
of
ILM2
<-­‐
1th
replicate
of
ILM3

56. Sample
A
vs.
A
=
the
Answer
should
be
zero
DEGs;
SVA
can
remove
false
posi$ve
DEGs
te
0.957
0.958
0.958
0.971
0.973
0.970
ILM2−ILM6
ILM3−ILM4
ILM3−ILM5
ILM3−ILM6
ILM4−ILM5
ILM4−ILM6
ILM5−ILM6
1 2
1
BBBBBB
B
B
B
BBB
B
B
BBBB
BBBB
B
BBBB
●
●
●
●
●
●
ILM1
ILM2
ILM3
ILM4
ILM5
ILM6
0
2500
5000
7500
10000
0
2500
5000
7500
10000
0
2500
5000
7500
10000
0
2500
5000
7500
10000
ABCD
ILM1−ILM2
ILM1−ILM3
ILM1−ILM4
ILM1−ILM5
ILM1−ILM6
ILM2−ILM3
ILM2−ILM4
ILM2−ILM5
ILM2−ILM6
ILM3−ILM4
ILM3−ILM5
ILM3−ILM6
ILM4−ILM5
ILM4−ILM6
ILM5−ILM6
Comparison
FalsepositiveDEGs
Without SVA With SVA
c
Illumina
SEQC
study
Leek
JT,
Storey
JD.PLoS
Genet.2007
Compare
the
same
sample
across
sites

57. SVA
remove
false
posi$ve
DEGs
Validated
by
PGM
data
from
ABRF-­‐NGS
study
te
0.957
0.958
0.958
0.971
0.973
0.970
ILM2−ILM6
ILM3−ILM4
ILM3−ILM5
ILM3−ILM6
ILM4−ILM5
ILM4−ILM6
ILM5−ILM6
1 2
1
BBBBBB
B
B
B
BBB
B
B
BBBB
BBBB
B
BBBB
●
●
●
●
●
●
ILM1
ILM2
ILM3
ILM4
ILM5
ILM6
0
2500
5000
7500
10000
0
2500
5000
7500
10000
0
2500
5000
7500
10000
0
2500
5000
7500
10000
ABCD
ILM1−ILM2
ILM1−ILM3
ILM1−ILM4
ILM1−ILM5
ILM1−ILM6
ILM2−ILM3
ILM2−ILM4
ILM2−ILM5
ILM2−ILM6
ILM3−ILM4
ILM3−ILM5
ILM3−ILM6
ILM4−ILM5
ILM4−ILM6
ILM5−ILM6
Comparison
FalsepositiveDEGs
Without SVA With SVA
c
Illumina
PGM
−2 −1 0 1
−0.50.00.51.01.5
Dimension 1
Dimension2
A.1
A.2 A.3
A.4
B.1
B.2
B.3
B.4
A.1
A.2
B.1
B.2
A.1
A.2
B.1B.2
●
●
●
PGM1
PGM2
PGM3
P
P
P
P
P
P
site
● ● ●PGM1 PGM2 PGM3
1 2 3 4
P
P
P
P
P
site
A
● ● ●PGM1 PGM2 PGM3
1 2 3 4
comp: A comp: B
0
50
100
150
200
PGM1−PGM2
PGM1−PGM3
PGM2−PGM3
PGM1−PGM2
PGM1−PGM3
PGM2−PGM3
Comparison
FalsepositiveDEGs
Without SVA With SVA
1
1
NumberofDEGs
d e f
SEQC
study
Li
et
al.,
in
press,
Nat.
Biotech.,
2014
ABRF-­‐NGS
study

58. 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
…

59. 0.
Genomes
1. RNA
Standards
2. Removing
RNA-­‐Seq
Varia$on
3. Epitranscriptome
4. #JustSequenceIt

60. Which
transcriptome
is
important?
All
…
RNA
bases.

62. The
four-­‐base
genome
is
just
the
beginning

63. The
four-­‐base
genome
is
just
the
beginning

64. There
are
many
RNA-­‐mods
as
well:
Chuan
He

65. Abbreviation Chemical0name
m1
acp3
Ψ 1'methyl'3'(3'amino'3'carboxypropyl)5pseudouridine
m1
A 1'methyladenosine
m1
G 1'methylguanosine
m1
I 1'methylinosine
m1
Ψ 1'methylpseudouridine
m1
Am 1,2''O'dimethyladenosine
m1
Gm 1,2''O'dimethylguanosine
m1
Im 1,2''O'dimethylinosine
m2
A 2'methyladenosine
ms2
io6
A 2'methylthio'N6
'(cis'hydroxyisopentenyl)5adenosine
ms2
hn6
A 2'methylthio'N6
'hydroxynorvalyl5carbamoyladenosine
ms2
i6
A 2'methylthio'N6
'isopentenyladenosine
ms2
m6
A 2'methylthio'N6
'methyladenosine
ms2
t6
A 2'methylthio'N6
'threonyl5carbamoyladenosine
s2
Um 2'thio'2''O'methyluridine
s2
C 2'thiocytidine
s2
U 2'thiouridine
Am 2''O'methyladenosine
Cm 2''O'methylcytidine
Gm 2''O'methylguanosine
Im 2''O'methylinosine
Ψm 2''O'methylpseudouridine
Um 2''O'methyluridine
Ar(p) 2''O'ribosyladenosine5(phosphate)
Gr(p) 2''O'ribosylguanosine5(phosphate)
acp3
U 3'(3'amino'3'carboxypropyl)uridine
m3
C 3'methylcytidine
m3
Ψ 3'methylpseudouridine
m3
U 3'methyluridine
m3
Um 3,2''O'dimethyluridine
imG'14 4'demethylwyosine
s4
U 4'thiouridine
chm5
U 5'(carboxyhydroxymethyl)uridine
mchm5
U 5'(carboxyhydroxymethyl)uridine5methyl5ester
inm5
s2
U 5'(isopentenylaminomethyl)'52'thiouridine
inm5
Um 5'(isopentenylaminomethyl)'52''O'methyluridine
inm5
U 5'(isopentenylaminomethyl)uridine
nm5
s2
U 5'aminomethyl'2'thiouridine
ncm5
Um 5'carbamoylmethyl'2''O'methyluridine
ncm5
U 5'carbamoylmethyluridine
cmnm5
Um 5'carboxymethylaminomethyl'52''O'methyluridine
cmnm5
s2
U 5'carboxymethylaminomethyl'2'thiouridine
cmnm5
U 5'carboxymethylaminomethyluridine
cm5
U 5'carboxymethyluridine
f5
Cm 5'formyl'2''O'methylcytidine
f5
C 5'formylcytidine
hm5
C 5'hydroxymethylcytidine
ho5
U 5'hydroxyuridine
mcm5
s2
U 5'methoxycarbonylmethyl'2'thiouridine
mcm5
Um 5'methoxycarbonylmethyl'2''O'methyluridine
mcm5
U 5'methoxycarbonylmethyluridine
mo5
U 5'methoxyuridine
m5
s2
U 5'methyl'2'thiouridine
mnm5
se2
U 5'methylaminomethyl'2'selenouridine
mnm5
s2
U 5'methylaminomethyl'2'thiouridine
mnm5
U 5'methylaminomethyluridine
m5
C 5'methylcytidine
m5
D 5'methyldihydrouridine
m5
U 5'methyluridine
τm5
s2
U 5'taurinomethyl'2'thiouridine
τm5
U 5'taurinomethyluridine
m5
Cm 5,2''O'dimethylcytidine
m5
Um 5,2''O'dimethyluridine
preQ1 7'aminomethyl'7'deazaguanosine
preQ0 7'cyano'7'deazaguanosine
m7
G 7'methylguanosine
G+
archaeosine
D dihydrouridine
oQ epoxyqueuosine
galQ galactosyl'queuosine
OHyW hydroxywybutosine
I inosine
imG2 isowyosine
k2
C lysidine
manQ mannosyl'queuosine
mimG methylwyosine
m2
G N2
'methylguanosine
m2
Gm N2
,2''O'dimethylguanosine
m2,7
G N2
,7'dimethylguanosine
m2,7
Gm N2
,7,2''O'trimethylguanosine
m2
2G N2
,N2
'dimethylguanosine
m2
2Gm N2
,N2
,2''O'trimethylguanosine
m2,2,7
G N2
,N2
,7'trimethylguanosine
ac4
Cm N4
'acetyl'2''O'methylcytidine
ac4
C N4
'acetylcytidine
m4
C N4
'methylcytidine
m4
Cm N4
,2''O'dimethylcytidine
m4
2Cm N4
,N4
,2''O'trimethylcytidine
io6
A N6
'(cis'hydroxyisopentenyl)adenosine
ac6
A N6
'acetyladenosine
g6
A N6
'glycinylcarbamoyladenosine
hn6
A N6
'hydroxynorvalylcarbamoyladenosine
i6
A N6
'isopentenyladenosine
m6
t6
A N6
'methyl'N6
'threonylcarbamoyladenosine
m6
A N6
'methyladenosine
t6
A N6
'threonylcarbamoyladenosine
m6
Am N6
,2''O'dimethyladenosine
m6
2A N6
,N6
'dimethyladenosine
m6
2Am N6
,N6
,2''O'trimethyladenosine
o2yW peroxywybutosine
Ψ pseudouridine
Q queuosine
OHyW undermodified5hydroxywybutosine
cmo5
U uridine55'oxyacetic5acid
mcmo5
U uridine55'oxyacetic5acid5methyl5ester
yW wybutosine
imG wyosine
Table515'5List5of5Base5Modifications5Covered5by5Claims
m6A
is
just
1
of
the
110
known
RNA
modifica$ons
from
the
RNA
Modifica$on
Database

66. m6A
marks
are
widespread
in
cancer
cells’
RNA
Meyer
et
al.,
Cell,
2012

67. A
new
method:
MeRIP-­‐Seq
Meyer
et
al.,
Cell,
2012

68. Conserva$ons
of
signal
and
sites
in
orthologous
genes
Meyer
et
al.,
Cell,
2012

69. Peaks
also
show
strong
conserva$on
Meyer,
2012

70. Many
puta$ve
roles
for
m6A
in
RNA
Paz-­‐Yaakov
et
al,
2010
Saletore,
Chen-­‐Kiang,
and
Mason,
RNA
Biology,
2013.

72. 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
O
RNA
RNA
H2
C
OH
N
NN
N
HN
O
OHO
O
RNA
RNA
C
H
O
N
NN
N
HN
O
OHO
O
RNA
RNA
C
OH
O
N
NN
N
HN
O
OHO
O
RNA
RNA
CH3
A m6A
ca6A f6A hm6A
O
H H
O
H OH
H2O
Mettl3
FTO/ALKBH5
O
2
, Fe
II
, -KG
FTO/
ALKBH5
O
2
, Fe
II
, -KG
Li
and
Mason,
ARGHG,
2014

73. New
layer
to
study!

74. Direct
Detec$on
of
Methyla$on
on
PacBio
70.5 71.0 71.5 72.0 72.5 73.0 73.5 74.0 74.5
0
100
200
300
400
Fluorescence
intensity(a.u.)
Time (s)
104.5 105.0 105.5 106.0 106.5 107.0 107.5 108.0 108.5
0
100
200
300
400
Fluorescence
intensity(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)

75. 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.

76. 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

77. Saletore
et
al.,
Genome
Biology,
2012

78. m6A
is
dis$nct
from
A
Saletore
et
al.,
Genome
Biology,
2012

79. 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

80. 0.
Genomes
1. RNA
Standards
2. Removing
RNA-­‐Seq
Varia$on
3. Epitranscriptome
4. #JustSequenceIt

81. Which
genome
is
important?
All
…
Cells.

82. −5 0 5
−6−4−2024
Comp.1
Comp.2
C01
C02
C03
C04
C05
C06
C07
C08
C09
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
C33
C34
C35
C36
C37
C38
C39
C40
C41C42
C43
C44
C45
C46
C47
C48
C49
C50
C51
C52
C53
C54
C55
C56
C57
C58
C59
C60
C61
C62
C63
C64
C65
C66
C67
C68
C69
C70
C71
C72
C73
C74
C75
C76
C77
C78
C79
C80
C81
C82
C83
C84
C85
C86
C87
C88
C89
C90
C91
C92
C93
C94
C95
C96
Principal
Component
1
Principal
Component
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
MGST3
UROD
PSMA5
PSMB2
DHRS3
L.XKR8
HBXIP
S100A11
R.spk7mid
RPS27
PSMD4
MAST2
YBX1
SLC39A1
UBR4
B4GALT3
S.8
CNIH4
NCSTN
SRM
RNF220
WDR77
TRAPPC3
RPL11
VPS72
ILF2
NUDC
S.4
PARP1
PRDX6
BCRABL
HIST2H2BE
YARS
PI4KB
UBE2Q1
VAMP3
KIFAP3
SF3A3
TPR
DIRAS3
GNPAT
ZMPSTE24
HSD17B7
IPO13
KIF2C
CAPZA1
FAF1
MRPL9
SDHB
AHCYL1
NOC2L
HMGN2
L.AXIN2
GNL2
SFRS4
CREG1
ETV1
SOX5
MEAF6
S.2
DTL
MARCKSL1
MRPS15
JAK1
GNB1
S.10
PTCH1
ALDH9A1
RPA2
KHDRBS1
SFPQ
ARPC5
TXNIP
UFC1
RABGGTB
S.5
KIAA0907
CDC20
PRPF3
KPNA6
RPL22
ACTB
WASF2
ATP5F1
S.6
KIF14
LRRC47
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

83. Which
genome
is
important?
All
…
Species.

84. nhprtr.org
Pipes,
Li,
Bozinoski
et
al.,
NAR,
2013

85. 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.

86. 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.

87. 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

88. 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

89. Chimpanzee
de
novo
assembled
transcriptome
fully
recovers
most
genes
in
current
annota$on
Chimpanzee De Novo Assembled Transcriptome
Proportion of gene covered by transcriptome
Numberofgenes
0.0 0.2 0.4 0.6 0.8 1.0
020004000600080001000012000

90. Which
genome
is
important?
All
…
Interac$ng
Elements.

91. We
need
more
Longitudinal,
Integra$ve
Systems
Biology

94. 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

95. These People are Awesome
@mason_lab

96. 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