Adventures in Computational Biology

Adventures in
Computational Biology
Alyssa Frazee
Johns Hopkins biostatistics
using statistics, math, biology, and computer
programming to untangle the mysteries of gene
expression

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gene expression: definition
transcription
DNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT!
RNA AUCAGUCGAUCACCGAU!
translation
protein

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transcription

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transcription
we can measure a gene’s expression level as
the amount of RNA present in the cell that
was transcribed from the gene

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transcription can get complex: splicing
AUCAGUCGAUC
\!
CGAUCACCGAU!

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so what?
differences in gene expression and splicing
have been implicated in:
cell differentiation
(Trapnell et al 2010)

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so what?
organism development
(Graveley 2010)
image: Chris,ne Gerhart, bit.ly/16h6P0Y. license.

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so what?
cancer
(Govindan 2012)
image: Wikimedia Commons,
bit.ly/1cvKEc6

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measuring expression: RNA-seq data
Genome
Transcripts
RNA-seq reads
(50-100 bp long)
great for whole
genomes / populations!

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next gen
sequencing
is awesome
G
C
T
A
A
G
C T
A
F
a Illumina/Solexa — Reversible terminators
Incorporate
all four
nucleotides,
each label
with a
different dye
Repeat cycles
T
G
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T
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G C
A
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G C
A
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F
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F F
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F F F
F F F
F
F F
F
F
F
F
Cleave dye
and terminating
groups, wash
Wash, four-
colour imaging
Metzker 2010

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next gen sequencing is awesome
Metzker 2010
Repeat cycles
wa
C
G
A
T
b
CATCGT
Top:
Bottom: CCCCCC
Figure 2 | Four-colour and one-colour cyclic reve
termination (CRT) method uses Illumina/Solexa’s 3
solid-phase-amplified template clusters (FIG. 1b, sh
imaging, a cleavage step removes the fluorescent
tris(2-carboxyethyl)phosphine (TCEP)23. b | The fou

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@22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2!
CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
+!
GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
@22:16362385-16362561W:ENST00000440999:3:177:-56:294:S/2!
GCGTGAGCCACAGGGCCCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCT
+!
@=ABBBBIIIIIIIIHHGGGGIIDBDIIIIIIGIIIIHIIIIHFDD@BBDBGGFIDEE8DCC/29>BGFCGHHHGF
@22:16362385-16362561W:ENST00000440999:4:177:137:254:S/1!
TCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCAAGGCCTGAACTACCTGCaGTGGGGAGCACCTCAGGGTTT
+!
DDGBBCGGGIGGGBDDDHIIGGDGD77=BDIIIIIIIIFHHHHIIIHEFFHGGDD8A>DEGHHIFDDHH8@BEDDI
@22:16362385-16362561W:ENST00000440999:5:177:68:251:S/2!
AGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCCTGGAGCGAGTTGTGGATGGCaaaaaGaCnCgCC
+!
HIGHIHFHEGE4111:.;8@?@HDIIIIIIIEGGIHHHIIGA?=:FIIIDD8.02506A8=AC#############
@22:16362385-16362561W:ENST00000440999:6:177:348:453:S/1!
AAGGCCTGAACTACCTGCGGTGGGGAGCACCTCAGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCC
+!
B9?@8=42:E@GDEDIIIIIGGHIIIFBEEAGIIDIIDHHGGHIIEGEIIIIIHIHFHFFEEFGGGGGB88>:DGH
@22:51205934-51222090C:ENST00000464740:132:612:223:359:S/2!
GGAAGTATGATGCTGATGACAACGTGAAGATCATCTGCCTGGGAGACAGCGCAGTGGGCAAATCCAAACTCATGGA
+!
IIEHHHHHIIIIIIIHGGDGHHEDDG8=;?==19;<<>D@@GGGIIHIIHGGDDHGBA=ABEG@@DFCCAA<:=>8
@22:51205934-51222090C:ENST00000464740:125:612:-1:185:S/1!
TGGAGTGCGCTGCGGCGCGAGCTGGGCCGGCGGGCGTGGTTCGAGAGCGCGCAGAGTCCAGACTGGCGGCAGGGCC
+!
HHIIIHIDGG@;=@GIIIIIDDGBBBEDB@8>5554,/':9B@@C?==@1:2@?=GG=;

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© Allie Brosh, Hyperbole and a Half.
hLp://bit.ly/1mImIJx
(I try to avoid this)

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analyzing the data, step (1): alignment
match reads back to their original genomic location
genome
lide credit: Jeﬀ

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analyzing the data, step (2): assembly!
reconstruct transcripts from read alignments
Genome
Fragments
Transcripts
lide credit: Jeﬀ

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Genome
equally likely
assemblies
ASSEMBLY IS A REALLY DIFFICULT PROBLEM

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Genome
ASSEMBLY IS A REALLY DIFFICULT PROBLEM
…but researchers are motivated to solve it
because data-driven assembly allows for
discovery

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Genome
Transcripts
lide credit: Jeﬀ
analyzing the data, step (3): estimate expression!
How many reads originated from each transcript?

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analyzing the data, step (4): differential
expression testing
Is the mean abundance for transcript X the same in population
A and population B?
transcripts
flag as differentially expressed
population A population B

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our proposed pipeline: Ballgown
image: bit.ly/HBbLaO

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align with TopHat

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align with TopHat
(which depends on Bowtie

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align with TopHat
assemble with Cufflink

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align with TopHat
assemble with Cufflink
analyze with
Ballgown

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align assemble
estimate
abundances
organize output
•  visualize assembly structure
•  postprocess assembly if necessary
•  test for differential expression
[use tool of your choice]
Ballgown

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visualize
51210000 51215000 51220000
genomic position
0 78.92 157.84 236.76 315.68 394.61 473.53 552.45 631.37 713.88
expression, by transcript
gene XLOC_000454, sample 1

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check
51210000 51215000 51220000
genomic position
Assembled and Annotated Transcripts
annotated assembled

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fix?
51210000 51215000 51220000
genomic position
Assembled and Annotated Transcripts
annotated assembled

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test for differential expression

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recall! we want to know if the mean abundance for transcript X
is the same in population A and population B.
transcripts
flag as differentially expressed
population A population B

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LINEAR REGRESSION
IN FIFTEEN MINUTES!

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55 60 65 70 75 80
100 120 140 160
Heights and Weights of 500 Adults
Height (inches)
Weight (lbs)

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55 60 65 70 75 80
100 120 140 160
Height (inches)
Weight (lbs)
this seems like a good line!
y=mx + b

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55 60 65 70 75 80
100 120 140 160
Height (inches)
Weight (lbs)
but wait! is this line better?

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Height (inches)
Weight (lbs)
or perhaps this one?

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55 60 65 70 75 80
100 120 140 160
Height (inches)
Weight (lbs)
what does “better” even mean?

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Height (inches)
Weight (lbs)

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55 60 65 70 75 80
100 120 140 160
Height (inches)
Weight (lbs)
the y-axis distance from a fitted
line to a data point is called a
“residual”

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55 60 65 70 75 80
100 120 140 160
Height (inches)
Weight (lbs)
“best” line has minimum
“sum of squared residuals”

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100 120 140 160
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Weight (lbs)
The Best Line for this data

View Slide

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Height (inches)
Weight (lbs)
male
female

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55 60 65 70 75 80
100 120 140 160
Height (inches)
Weight (lbs)
male
female
regression can handle it!

View Slide

what do we learn from regression?
y = a + b1
x
weight = 20.9 + 1.53*height
slope?
intercept?

View Slide

what do we learn from regression?
y = a + b1
x1
+ b2
x2
weight = 55.2 + 0.935*height + 9.76(if male)
slope(s)?
intercept?

View Slide

nice statistical tests exist to help us
decide which model is better
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55 60 65 70 75 80
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Weight (lbs)
male
female
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55 60 65 70 75 80
100 120 140 160
Height (inches)

View Slide

back to molecular biology!
for each transcript, fit 2 linear regression models:
model A: includes “disease” as covariate
model B: does not include disease aaa
Y is expression (# of RNA-seq reads)
X is “disease status”
if model A fits better than model B, then disease status
has something to do with expression of that transcript

View Slide

a real-world example
6 7 8 9 10
0 1 2 3 4
RIN
log2(transcript expression + 1)
transcript 2208, chr1: 17732159−17739760
YRI
CEU
FIN
GBR
TSI
6 7 8 9 10
0.0 0.5 1.0 1.5 2.0
RIN
log2(transcript expression + 1)
transcript 295456, chr8: 52729948−52814118
YRI
CEU
FIN
GBR
TSI

View Slide

a real-world example
Applicable to other data!
I’ve done projects on:
•  cancer
•  psychiatric disease
•  stem cells

View Slide

View Slide

other “flavors” of linear models for
gene expression:
•  EdgeR and DESeq (“generalized” linear models)
•  limma (empirical Bayes approach)

View Slide

on our way

View Slide

thank you!
contact (please feel free):
email: [email protected]
twitter: @acfrazee
website: alyssafrazee.com
My collaborators: Jeff Leek (advisor), Geo Pertea, Steven
Salzberg, Ben Langmead, Andrew Jaffe, and several others in
the Center for Computational Biology and biostatistics
department

View Slide

references (by PubMed ID)
•  Ballgown paper: http://biorxiv.org/content/early/
2014/03/30/003665
•  Cufflinks: 20436464 (Trapnell et al 2010)
•  EdgeR: 19910308 (Robinson et al 2010)
•  DESeq: 20979621 (Anders and Huber 2010)
•  Limma: “Linear Models for Microarray Data” by Gordon K Smyth, in
Bioinformatics and Computational Biology Solutions using R and
Bioconductor, Springer 2005; 24485249 (Law et al 2014)
•  Drosophila life cycle: 21179090 (Graveley et al 2011)
•  Isoforms & cancer: 22980976 (Govindan et al 2012)
•  cell differentiation: 20436464 (Cufflinks; Trapnell et al 2010)
•  Next generation sequencing paper/figures: 19997069 (Metzker 2010)
•  image sources: http://bit.ly/16h6P0Y, http://bit.ly/1cvKEc6,
http://bit.ly/19TaSH9, http://bit.ly/12pNREw, http://bit.ly/HBbLaO

View Slide

Adventures in Computational Biology

Adventures in Computational Biology

Alyssa Frazee

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