1. Cancer
Systems
Biology:
RNA-­‐Seq
and
Differen;al
Expression
Analysis
Taking
advantage
of
a
Measurement
Revolu;on
July
25,
2013
Anne
DeslaLes
Mays
Wellstein/Riegel
Laboratory
Mentor:
Anton
Wellstein,
MD,
PhD
7/25/13
Wellstein/Riegel
Laboratory
1

2. Talk
Outline
• On
the
Shoulders
of
Giants
• Sequencing
Timeline
• RNASeq
for
Everyone
• RNA-­‐Sequencing
Details
• Differen;al
Expression
Analysis
• Causality
• Cancer
Therapeu;cs
Example
• Ask
Bigger
Ques;ons
–
Sequencing
Everything
7/25/13
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Laboratory
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3. 7/25/13
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Laboratory
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Rosalind
Franklin
“pioneered
use
of
x-­‐rays
to
create
images
of
unorganized
maLer
–
such
as
large
biological
molecules
–
not
just
single
crystals”
hLp://www.pbs.org/wgbh/aso/databank/entries/bofran.html
“Franklin
made
equipment
adjustments
to
produce
an
extremely
fine
beam
of
x-­‐rays.
She
extracted
finer
DNA
fibers
than
ever
before
and
arranged
them
in
parallel
bundles.
Studied
fibers’
reac;ons
to
humid
condi;ons.
…
allowed
her
to
discover
cruical
keys
to
DNA’s
structure….
Wilkins
shared
this
with
Watson
&
Crick
at
Cambridge
without
her
knowledge…”

4. 7/25/13
Wellstein/Riegel
Laboratory
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Sequencing
Timeline

5. 7/25/13
Wellstein/Riegel
Laboratory
5
Human
Sequencing
Timeline
Key
Technical
Advances:
Celera
Human
Sequence
done
in
one
loca;on
on
the
largest
super
computer
in
private
hands
at
that
;me

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Laboratory
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12. 7/25/13
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13. Cancer
Systems
Biology
Taking
advantage
of
measurement
revolu3on
Declining
sequencing
costs,
decreasing
compu3ng
costs
How
do
you
leverage
all
this
data?
GEO May 25, 2012
GEO June 25, 2013

14. Here
is
an
example
RNA-­‐Seq
Workflow
7/25/13
Wellstein/Riegel
Laboratory
14
Experimental
Design
Sample
Collec;on
Quality
Control
Read
Trimming
Differen;al
Analysis
Transcript
Iden;fica;on
Pathway
Analysis
Feature
Discovery
Sequencing

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hLp://rnaseq.uoregon.edu/index.html

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hLp://rnaseq.uoregon.edu/index.html

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hLp://rnaseq.uoregon.edu/index.html

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hLp://rnaseq.uoregon.edu/index.html

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hLp://rnaseq.uoregon.edu/index.html

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Laboratory
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hLp://rnaseq.uoregon.edu/index.html

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Laboratory
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hLp://rnaseq.uoregon.edu/index.html

22. Replicates:
Type
I
and
Type
II
errors
7/25/13
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Laboratory
22

23. Detec;ng
Signal
vs.
Noise
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Laboratory
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24. 7/25/13
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25. What
is
the
goal
of
the
sequencing
experiment?
7/25/13
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Laboratory
25

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Before
Library
Construc;on
1. Most
vendors
and
cores
will
assess
the
quality
of
the
RNA
before
sequencing
2. Important
to
determine
before
sequencing
begins
Garbage
–
in
==
Garbage
out
Before
library
construc;on,
RNA
quality
must
be
assessed

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RNA-­‐seq

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Three
steps
to
get
to
a
fresh
sequence
with
the
Illumina
Genome
Sequence
Analyzer
• Library
genera;on
• Cluster
genera;on
• Sequencing

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Laboratory
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Before
Library
Construc;on
1. Poly-­‐A
Selec;on
(Total
RNA
-­‐>
mRNA)
2. mRNA
fragmenta;on
3. First
strand
synthesis
(here
we
stop
if
we
want
to
maintain
strand
specificity
4. Second
strand
synthesis
Other
techniques
1. Ribozero
2. Ribominus
Library
Construc;on:
Messenger
RNA
are
Poly-­‐A
selected
from
Total
RNA,
fragmented
and
cDNA
synthesized

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32
cDNA
(single
or
double
stranded)
1. cDNA
is
blunt
end-­‐repaired
and
phosphorylated
(B.)
2. A-­‐base
added
to
prepare
for
indexed
adapter
liga;on
(C.)
Library
Construc;on:
End
repair
and
adenyla;on
results
in
adapter
liga;on
ready
constructs

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Laboratory
33
Index
adapter
liga;on
and
product
ready
for
amplifica;on
on
cBot
or
the
cluster
sta;on
1. Strand
specific
tags
are
added
to
the
A
base
–
ligate
index
adapter
(D)
2. Denature
and
amplify
for
final
product
(E)
Library
Construc;on:
Adapter
liga;on
results
in
cluster-­‐
genera;on-­‐ready
constructs

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Single
DNA
molecules
hybridize
to
the
lawn
of
oligos
graped
to
the
surface
of
the
flow
cell
1. Oligo
lawn
2. Oligos
hybridize
to
the
adapters
that
had
been
ligated
to
the
library
fragments
which
flow
through
the
cell
Cluster
Genera;on:
In
the
illumina
Cbot
system,
single
molecules
are
isothermally
amplified
in
a
flow
cell
to
prepare
them
for
sequencing

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Laboratory
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Bridge
amplifica;ons
resul;ng
in
100s
of
millions
of
unique
clusters
1. Each
fragment
is
clonally
amplified
through
a
series
of
extensions
and
isothermal
bridge
amplifica;ons
2. Reverse
strands
cleaved
and
washed
away
3. Ends
are
blocked
4. Sequencing
primer
hybridized
to
the
DNA
template
5. Libraries
are
ready
for
sequencing
Cluster
genera;on:
Bound
fragments
are
extended
to
make
copies
and
reverse
strands
cleaved
and
washed
away

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Laboratory
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4
fluorescently
labeled
reversibly
terminated
nucleo;des
1. Each
base
competes
for
addi;on
2. Natural
compe;;on
ensures
highest
accuracy
3. Aper
each
round
of
synthesis,
clusters
are
excited
by
a
laser
emiqng
a
color
that
iden;fies
the
newly
added
base
4. Fluorescent
label
and
blocking
group
are
removed
allowing
for
addi;on
of
next
nucleo;de
5. Proprietary
(Illumina)
chemistry
reads
a
base
in
each
cycle
6. Allows
for
accurate
sequencing
through
difficult
regions
such
as
homopolymers
and
repe;;ve
sequence
Sequencing:
100s
of
millions
of
clusters
sequenced
simultaneously

37. There
are
other
ways
to
Inquire
about
the
Transcriptome
• Array
Based
Technologies
– Affymetrix
– Agilent
– Known
genes
and
hybridiza;on
protocols
• Microarray
– 20,000+
array
experiments
on
a
single
platorm
– Edge
effects
– False
posi;ves
/
false
nega;ves
• Bead-­‐based
arrays
• Tiling
arrays
• SAGE
7/25/13
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Laboratory
37

38. What
is
unique
about
RNA-­‐Seq?
• Allows
you
to
discover
and
profile
the
en;re
transcriptome
of
any
organism
• No
probes
or
primers
to
design
• Novel
transcripts
• Novel
isoforms
• Alterna;ve
splice
sites
• Rare
transcripts
• cSNPS
–
all
of
this
in
one
experiment
7/25/13
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Laboratory
38

39. 7/25/13
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Aper
sequencing…
1. Quality
control
–
trim
your
reads
2. Count
Reads
• Align
to
genome
• Align
to
transcriptome
3. Interpret
Data
• Sta;s;cal
tests
(differen;al
expression
analysis)
• Visualiza;on
(mapped
reads)
• Pathway
analysis
Not
so
simple
–
big
data,
big
compute
requirements
Aper
sequencing,
we
must
then
perform
RNA-­‐Seq
Data
Analysis

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42. RNASeq flow chart – reference (steps 1-4): http://trinityrnaseq.sourceforge.net/genome_guided_trinity.html
Step 1: align-reads:
FASTQ
PE*
reads
Reference
Genome
Assembly
WGS
Exis;ng
Gene
models
(gt
files
w/
tss
ids)*
Gene
models
mapped
to
reference
gsnap
trimmoma;c
FASTQC
trimmed
PE*
reads
Quality
control
consensus
per
read
length
graphs
• Tss ids = transcription start site ids, in a gtf file format
• PE – paired end
• The gene models that are built with the pasa pipeline can be input to tophat
Shadeless
rectangle
An unshaded rectangle represents code to be run – a process
Shaded
rectangle
A shaded rectangle is a file or a graphic which may be an input and/
or an output
Legend
Gsnap
aligned
Bam
files
Dark
rectangle
Dark rectangle represents a file that can be displayed as a track in
crop-pedia
Align-reads: Gsnap is used to align reads to the
genome sequence.
samtools
Gsnap.CoordSorted.bam

43. RNA
Alterna;ve
Splicing:
Why
you
need
gapped
aligners
7/25/13
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Laboratory
43

44. RNASeq flow chart – reference (steps 1-4): http://trinityrnaseq.sourceforge.net/genome_guided_trinity.html
Step 2: assemble-reads:
Prep_rnaseq_
alignments_for
genome_assisted_
assembly.pl
• Tss ids = transcription start site ids, in a gtf file format
• PE – paired end
• The gene models that are built with the pasa pipeline can be input to tophat
Shadeless
rectangle
An unshaded rectangle represents code to be run – a process
Shaded
rectangle
A shaded rectangle is a file or a graphic which may be an input and/
or an output
Legend
Dark
rectangle
Dark rectangle represents a file that can be displayed as a track in
crop-pedia
assemble-reads: Trinity is used to assemble the RNA-Seq reads in each
partition. This can be done in a massiviely parallel manner, typically requiring
little RAM as compared to whole de novo RNA-Seq assemblies, and can be
executed using standard hardware.
The firs step (pre_rnaseq_alignments_for genome_assisted_assembly.pl –
partitions the reads according to covered regions
Gsnap.CoordSorted.bam
Find
Dir_*
-­‐name
“*reads”
>
read_files.list
Read_files.list
GG_write_trinity_
cmds.pl
ParaFly
Trinity_GG.cmds
Find
Dir_*
-­‐name
“*inity.fasta”
–exec
cat
{}
|
Inchworm_accession_incrementer.pl
>
Trinity_GG.fasta
Trinity_GG.fasta

45. RNASeq flow chart – reference (steps 1-4): http://trinityrnaseq.sourceforge.net/genome_guided_trinity.html
Steps 3 and 4: align-transcripts and assemble-transcript alignments
Launch_PASA_pipeline.pl
• Tss ids = transcription start site ids, in a gtf file format
• PE – paired end
• The gene models that are built with the pasa pipeline can be input to tophat
Shadeless
rectangle
An unshaded rectangle represents code to be run – a process
Shaded
rectangle
A shaded rectangle is a file or a graphic which may be an input and/
or an output
Legend
Dark
rectangle
Dark rectangle represents a file that can be displayed as a track in
crop-pedia
Trinity_GG.fasta
Pasa_databasename
.pasa_assemblies.denovo_
transcript_isoforms.gt
Pasa_databasename
.pasa_assemblies.denovo_
transcript_isoforms.bed
Pasa_databasename
.pasa_assemblies.denovo_
transcript_isoforms.gff3
Pasa_databasename
.pasa_assemblies.denovo_
transcript_isoforms.fasta

46. RNASeq flow chart – Step 5 – Tuxedo Suite – using the output of the trinity-genome-guided assembly and the pasa and
keygene annotation pipelines à call tuxedo suite (in parallel with then calling the abundancy estimator RSEM
• Tss ids = transcription start site ids, in a gtf file format
• PE – paired end
• The gene models that are built with the pasa pipeline can be input to tophat
Shadeless
rectangle
An unshaded rectangle represents code to be run – a process
Shaded
rectangle
A shaded rectangle is a file or a graphic which may be an input and/
or an output
Legend
Dark
rectangle
Dark rectangle represents a file that can be displayed as a track in
crop-pedia
Gff3
(gene
model)
Gff3togt
(convert
to
gt
format
Gt
(gene
model)
tophat
Calls
Bow;e2
Junc;ons.bed
Accepted.hits.
sam

47. RNASeq Quantitation and Differential Analysis
• Tss ids = transcription start site ids, in a gtf file format
• PE – paired end
• The gene models that are built with the pasa pipeline can be input to tophat
Shadeless
rectangle
An unshaded rectangle represents code to be run – a process
Shaded
rectangle
A shaded rectangle is a file or a graphic which may be an input and/
or an output
Legend
Quantitation (matrix file with counts per isoform) Model building/Differential analysis
Trinity.fasta
Dark
rectangle
Dark rectangle represents a file that can be displayed as a track in
crop-pedia
Tuxedo suite
Trinity genome guided assembly
Abundance
es;ma;on
RSEM
Transcripts
.gt/.gff*
trimmed
PE*
reads
RSEM.isoform.
results
Limma
Model
Design/contrast
matrix
building
randomForest
pcAlg
Genie3.R
DREAM4
Accepted.hits.
sam
cuffdiff2
• Transcript annotation file produced by cufflinks, cuffcompare or other
source
• Counts and read group tracking files also created
Isoforms.fpkm_tracking
Genes.fpkm.tracking
Cds.fpkm.tracking
Tss_groups.fpkm.tracking
Isoform_exp.diff
Gene_exp.diff
Tss_group_exp.diff
Cds_exp.diff

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How
much
RNA-­‐sequencing
data?
1. 20
million
paired
end
reads
~
2
GB
of
data
2. 100
million
paired
end
reads
~
10
GB
of
data
How
much
computa;on
power?
1. More
memory,
more
processors,
less
;me
it
takes
to
compute
2. Outsource
the
analysis,
s;ll
will
need
to
store
the
results
somewhere
Amazon
web
services
S3
storage
EC
elas;c
cloud
on
demand
computa;onal
facility
Georgetown
University
High
Performance
Computer
Core
matrix.georgetown.edu
UPENN
Galaxy
services
How
much
RNA-­‐sequencing
data,
how
much
computa;on
power
and
where
do
you
go
to
compute?

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49
A
growing
number
of
tools
enable
RNA-­‐Seq
analysis

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Laboratory
50
What
percentage
of
reads
are
covered?
What
percentage
of
reads
are
mapped?
3’
Bias
on
transcript
reads
1. 60-­‐80%
of
reads
are
mapped
2. Highest
percentage
or
3’
end
of
reads
are
mapped
3. Reads
need
to
be
quality
trimmed
Mapping
tools
bias
exons
to
known
genes

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51
Galaxy
is
a
web
based
tool
commiLed
to
enable
a
researcher
(more
than
just
for
RNA-­‐Seq)

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52

53. How
to
visualize
mapped
results?
• UCSC
Genome
Browser
(Gbrowse)
• Integrated
Genome
Browser
(IGB)
• Integrated
Genome
Viewer
(IGV)
Many
shared
formats,
reading
many
of
the
outputs
generated
by
the
programs,
ability
to
generate
ones
own
tracks
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54. 7/25/13
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54
Scale
chr21:
DNase Clusters
Multiz Align
Human mRNAs
K562 CTCF Int 1
K562 Pol2 Int 1
HeLaS3 Pol2 Int 1
GM12878 1
H1-hESC 1
K562 1
HeLa-S3 1
HepG2 1
GM12878
H1-hESC
K562
HeLa-S3
HepG2
HUVEC
GM12878 Pk
H1-hESC Pk
K562 Pk
HeLa-S3 Pk
50 kb hg19
23,600,000 23,650,000
C7 Random
C7 Targeted
Transcription Factor ChIP-seq from ENCODE
SwitchGear Genomics Transcription Start Sites
H3K27Ac Mark (Often Found Near Active Regulatory Elements) on 7 cell lines from ENCODE
RefSeq Genes
Human ESTs That Have Been Spliced
Digital DNaseI Hypersensitivity Clusters in 125 cell types from ENCODE
Vertebrate Multiz Alignment & Conservation (46 Species)
UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)
Simple Nucleotide Polymorphisms (dbSNP 137) Found in >= 1% of Samples
Individual matches for article Przybylski2010
Sequences in Articles: PubmedCentral and Elsevier
SNPs in Publications
Human mRNAs from GenBank
Regulatory elements from ORegAnno
Chromatin Interaction Analysis Paired-End Tags (ChIA-PET) from ENCODE/GIS-Ruan
DNA Methylation by Reduced Representation Bisulfite Seq from ENCODE/HudsonAlpha
CpG Methylation by Methyl 450K Bead Arrays from ENCODE/HAIB
Chromatin Interactions by 5C from ENCODE/Dekker Univ. Mass.
HWI-ST1129:97:D0LRDACXX:6:2208:3356:23592_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2208:3356:23592_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2204:15017:145130_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2204:15017:145130_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2107:8319:79365_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2107:8319:79365_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2107:12368:117403_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2107:12368:117403_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2208:7212:116648_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2208:7212:116648_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2205:11321:72079_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:1203:1649:66972_1:N:0:CTCTCA
HWI-ST1129:97:D0LRDACXX:6:1203:1649:66972_2:N:0:CTCTCA
HWI-ST1129:97:D0LRDACXX:6:2106:11187:101221_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2106:11187:101221_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2102:8052:88370_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2102:8052:88370_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2108:5000:141429_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2108:5000:141429_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:1303:16417:184679_2:N:0:CACTCC
HWI-ST1129:97:D0LRDACXX:6:1303:16417:184679_1:N:0:CACTCC
HWI-ST1129:97:D0LRDACXX:6:2106:18235:74385_1:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2106:18235:74385_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2201:15196:5280_2:N:0:CACTCA
HWI-ST1129:97:D0LRDACXX:6:2201:15196:5280_1:N:0:CACTCA
HWI-ST1129:299:C18KJACXX:6:1305:12160:63303_1:N:0:ATCACG
HWI-ST1129:299:C18KJACXX:6:1102:19732:75986_1:N:0:ATCACG
HWI-ST1129:299:C18KJACXX:6:1305:12160:63303_2:N:0:ATCACG
HWI-ST1129:299:C18KJACXX:6:1102:19732:75986_2:N:0:ATCACG
KCEBPB
LMafK_(ab50322)
KTAL1_(SC-12984)
KCEBPB KKYY1
KTBP
KE2F4
KTAF1
KELF1_(SC-631)
KPol2-4H8
KHEY1
KE2F6_(H-50)
KCEBPB
KTFIIIC-110
ggNFKB
GgPU.1
GBATF
GIRF4_(M-17)
GBCL11A
GgPU.1
gPU.1 KCEBPB
DA743484
BF207587
Delgado-Olguin2004
Layered H3K27Ac
100 _
0 _
Mammal Cons
K562 CTCF Sig 1
K562 Pol2 Sig 1
HeLaS3 Pol2 Sig 1

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57. What
do
RNA-­‐Seq
reads
look
like
for
GAPDH?
Repeat
masked
allowing
1/2
mismatched
bases
blat’d
reads
viewed
in
IGB
6.7.2

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RNA-­‐Seq
Differen;al
Expression
analysis

59. What
does
GAPDH
look
like
in
terms
of
quan;ta;on?
TOTAL
BM
HPP
RPKM
3SEQ
Counts
BLAT
Reads
RPKM
3SEQ
Counts
BLAT
Reads
CD34
0.7
340
230
8
8
14
BST1
19.7
5374
31
31
CD133
0.2
173
176
16
16
33
THY1
0
7
4
4
A12
1
0
A5
0
0
ALK
0
9
24
0
0
3
B9
0
0
C1
0
0
C2
0
0
C7
0
0
E7
0
0
E9
2
0
F6
0
0
G12
0
0
GAPDH
3013.2
727831
356289
120.8
5559
2670
H3
0
0
Blat
read
raw
counts
ra;o
==
3Seq
counts
ra;o
~=
130
to
1
RPKM
ra;o
~=
24.3

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RNA-­‐Seq
Quan;fica;on
Challenge:
A
problem
that
exists
with
RNA-­‐Seq
data
that
doesn’t
exist
with
array
data:
Longer
transcripts
produce
more
reads
than
shorter
transcripts
One
solu;on
to
account
for
this
is
RPKM
(FPKM
used
by
Cufflinks)
RPKM
=
10^9
x
C
/
NL,
which
is
really
just
simply
C/N
C(gene)=
the
number
of
mappable
reads
that
fall
onto
a
gene's
exons
N=
total
number
of
mappable
reads
in
the
experiment
L(gene)=
the
sum
of
the
exons
in
base
pairs.
Wold
(2008)
RPKM
–
reads
per
kilo
base
per
million
CPM
–
counts
per
million

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RNA-­‐Seq
Quan;fica;on
Challenge:
DESeq
Method
uses
the
geometric
mean
of
counts
in
all
samples
DESeq
Method:
Construct
a
"reference
sample"
by
taking,
for
each
gene,
the
geometric
mean
of
the
counts
in
all
samples.
To
get
the
sequencing
depth
of
a
sample
rela;ve
to
the
reference,
calculate
for
each
gene
the
quo;ent
of
the
counts
in
your
sample
divided
by
the
counts
of
the
reference
sample.
Now
you
have,
for
each
gene,
an
es;mate
of
the
depth
ra;o.
Simply
take
the
median
of
all
the
quo;ents
to
get
the
rela;ve
depth
of
the
library.
'es;mateSizeFactors'
func;on
of
DESeq
package
does
this
calcula;on.

62. DESeq:
an
R
package
that
works
with
Raw
Counts
to
determine
genes
differen;ally
expressed
across
samples
• Simon
Anders
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66. Given
a
list
of
differen;ally
expressed
Genes
now
enrichment
analysis
should
be
performed
• Enrichment
analysis
allows
the
researcher
to
leverage
documented
experiments
which
provide
evidence
for
genes
roles
in
pathways
and
func;ons
that
enable
the
researcher
to
determine
the
results
and
significance
of
their
experiments
• DAVID
– Gene
ontology
– Func;onal
ontology
• Revigo
– Output
of
David
may
be
placed
in
REVIGO
for
further
interpreta;on
and
sta;s;cal
explora;on
of
significance
of
discovered
sets
of
genes
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67. Using
differen;ally
expressed
genes,
biological
pathways
should
be
explored
• Differen;ally
expressed
genes
are
put
into
programs
such
as
pathway
studio
or
ingenuity
• Shortest
path
programs
and
• Canonical
pathway
analysis
• Enables
a
researcher
to
reverse
engineer
the
pathways
expressed
in
the
course
of
a
healthy
response
to
a
diseased
response
• Ideally
a
pathway
reveals
the
observed
phenotype
–
connec;ng
the
expressed
gene
expression
program
with
the
phenotype
–
genotype
–
gene
expression
program
to
phenotype
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68. RNA-­‐Sequencing:
What
is
it
good
for?
• Transcript
Annota;on
– Muta;on
iden;fica;on
– Isoform
determina;on
– Alterna;ve
Splice
Varia;on
• Differen;al
Gene
Expression
– Phenotypically
segrega;ng
experiments
– Allows
us
to
get
at
the
How
in
looking
at
the
response
of
an
organism
within
a
par;cular
cell
popula;on
to
events
– Good
and
careful
design
will
allow
us
to
unfold
the
dynamics
of
this
response
and
iden;fy
targets
for
altering
disease
responses
to
improve
ones
chances
of
surviving
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hLp://bayes.cs.ucla.edu/home.htm

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74
Acknowledgements
Dr.
Anton
Wellstein
Dr.
Anna
Riegel
Dr.
Marcel
Schmidt
Dr.
Elena
Tassi
The
en;re
lab:
Elena,
Virginie,
Ghada,
Ivana,
Eveline,
Khalid,
Eric
the
en;re
Wellstein/Riegel
laboratory
My
CommiLee
Dr.
Yuri
Gusev
Dr.
Anatoly
Dritschilo
Dr.
Michael
Johnson
Dr.
Christopher
Loffredo
Dr.
Habtom
Ressom
Dr.
Terry
Ryan
(external
commiLee
member)
High
Performance
Core
Group,
Steve
Moore,
especially
Woonki
Chung
Amazon
Cloud
Services
Dr.
Ann
Loraine,
UNC,
IGB
Developer
Brian
Haas,
Author
Trinity
Suite

75. Some
Resources
• hLp://rnaseq.uoregon.edu/index.html
• hLp://dx.doi.org/10.1038/npre.2010.4282.1
(DESeq)
• hLp://galaxy.psu.edu/
• hLp://seqanswers.com/
• hLp://www.broadins;tute.org/igv/
• hLp://bioviz.org/igb/index.html
• hLp://www.illumina.com
• hLp://www.otogene;cs.com
• hLp://www.dnanexus.com
• hLp://bioconductor.org/packages/2.12/bioc/html/limma.html
• hLp://trinityrnaseq.sourceforge.net/
• hLp://trinityrnaseq.sourceforge.net/genome_guided_trinity.html
• hLp://cufflinks.cbcb.umd.edu/
• hLp://brb.nci.nih.gov/BRB-­‐ArrayTools.html
• hLp://www.modernatx.com/
7/25/13
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Laboratory
75

76. Systems
Biology
History
(wikipedia)
• Systems
biology
roots
found
in
– Quan;ta;ve
modeling
of
enzyme
kine;cs
– Mathema;cal
modeling
of
popula;on
growth
– Simula;ons
to
study
neurophysiology
– Control
theory
and
cyberne;cs
• Theorists
– Ludwig
von
Bertalanffy
–
General
Systems
Theory
– Alan
Lloyd
Hodgkin
and
Andrew
Fielding
Huxley
–
constructed
a
mathema;cal
model
that
explained
poten;al
propaga;ng
along
the
axon
of
a
neuron
cell
– Denis
Nobel
–
first
computer
model
of
the
heart
Pacemaker
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77. Scien;fic
knowledge
is
limited
(and
advanced)
by
the
limits
(and
advancements)
of
measurement
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77
• Ilya
Shmulevich
Genomic
Signal
Processing
“Validity
of
the
model
involves
observa;on
and
measurement,
scien;fic
knowledge
is
limited
by
the
limits
of
measurement”
• Erwin
Shrödinger
Science
Theory
and
Man:
“It
really
is
the
ul;mate
purpose
of
all
schemes
and
models
to
serve
as
scaffolding
for
any
observa;ons
that
are
at
all
means
observable”