SACNAS 2016

Using Data Science to Study
Human Brain Genomic Measurements
Leonardo Collado-Torres
@fellgernon

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Mo#va#ng problem: iden#fy and validate
regions of the genome that change expression
during brain development

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Research theme
- annota#on-agnos#c
- reproducible analyses
- easily accessible data
- sta#s#cal tools for the genomics community

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RNA-seq
reads
Genome
(DNA)
RNA transcripts
(many possible
variants)
Measuring gene expression: RNA-seq
Adapted from @jtleek

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Genome
(DNA)
Mapped reads
Common analysis pipelines:
• Feature coun#ng (gene or exon level)
• Transcript assembly

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Challenges in counting
hBp://www-huber.embl.de/users/anders/HTSeq/doc/count.html

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Annotation variation
Frazee et al, Biostatistics, 2014

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Project size is increasing
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2009
(11)
2010
(46)
2011
(121)
2012
(235)
2013
(408)
2014
(625)
2015
(548)
2016
(18)
7
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9
10
11
12
Project size in base−pairs over the years
log10 base−pairs per project
hBps://jhubiosta#s#cs.shinyapps.io/recount/

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Genome
(DNA)
Mapped reads

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DER finder approach
•  Find con#guous base pairs with
Diﬀeren#al Expression signal à DE
Regions or DERs
•  Find nearest annotated feature

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coverage
vector
2 6 0 11 6
Genome
(DNA)
Read coverage

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Identifying brain development DERs
Fetal Infant
Child Teen
Adult 50+
6 / group, N = 36
Discovery data
Null:
Alt:
Models
Initial results
Jaffe et al, Nat. Neuroscience, 2015
50,650 DERs replicated
63,135 DERs
Final results

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Widespread differential expression of novel
transcriptional activity

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Age-associated DERs lack regional specificity
in the human brain
BrainSpan data

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Propor#on of Cells
Expression changes across development may
represent a changing neuronal phenotype
Estimation method: Houseman et al, BMC Bioinformatics, 2012

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Chromatin immunoprecipitation seq.
http://assets.illumina.com/content/dam/illumina-marketing/images/techniques/large/web-graphic-chipseq-workflow-large.jpg

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Common ChIP-seq analysis pipeline
Sample 1
Sample 2
...
Sample N
1
call peaks
call peaks
call peaks
U
11
U
12
U
1N
Number of
unique peaks
Merge unique
peaks
Total peaks:
U
merged
≤ U
1
+ U
2
Determine differentially
bound peaks
between groups 1 & 2
Total tests: U
merged
1
Total: U
1
Sample 1
Sample 2
...
Sample N
2
call peaks
call peaks
call peaks
U
21
U
22
U
2N
Number of
unique peaks
2
Total: U
2
Group 1
Group 2
(A)
(A)
(B)
(C)

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Brain ChIP-seq data
EpiMap project

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Variation mostly explained
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0 20 40 60 80 100
Percentage variance explained
Brain
region
C
ell type
Age
at death
H
em
isphere
PM
I
pH
Sex
H
eight
BM
I
C
hrom
atin
am
ount
M
apped
reads
Individual
Flow
cell batch
Library
batch
R
esidual variation
All Regions
H3K4me3 data: all regions with binding signal

View Slide

Differentially bound regions
0 16 64 128 256 512
A:N−:− A:N+:− A:N−:+ A:N+:+ D:N−:− D:N+:− D:N−:+ D:N+:+
Coverage
PRB2, 0 bp from tss: overlaps 5'
−
+
Genes
11652500 11653000 11653500 11654000 11654500
−
Tx
chr12

View Slide

Collado-Torres et al, F1000Research, 2015
regionReport
chr start end strand p-value
chr1 1000 2000 + 0.9
chr2 5000 8000 - 0.001
chr3 2468 2668 + 0.051
. . . . .
. . . . .
. . . . .
chrX 6000 6300 + 0.009
chrX 6500 6800 - 0.5
Genomic workflow:
identify regions
renderReport
(A) default (B) custom
(C) derfinderReport
(D) DESeq2Report (E) edgeReport
Create HTML/PDF
report

View Slide

regionReport

View Slide

Interactive HTML reports
(A)
(B) Clickable buttons:
show/hide code

View Slide

Interactive HTML reports
(A)
(B)

View Slide

•  Resource with data from 2,040 projects
•  Aligned with
•  Total RNA-seq samples:
49,657 + 9,662 = 59,319

View Slide

recount: via the web

View Slide

View Slide

Mo#va#ng problem: iden#fy and validate
regions of the genome that change expression
during brain development
1. derfinder permits discovery of novel expressed
& differen#ally bound regions
2.  we iden#fied & validated gene expression
changes in the developing brain
3.  we have developed tools for reproducible
shareable repor#ng
4.  these tools can be easily be used to process
2,040 projects via recount

View Slide

Acknowledgements
Advisors
Jeffrey Leek
Andrew Jaffe
Collaborators
Alyssa Frazee
Abhinav Nellore
Michael Love
Ben Langmead
Rafael Irizarry
EpiMap project
Funding
NIH
LIBD
JHU-Biostats
CONACyT México
Conference Support
Harvard Health Policy Data Science Lab, SACNAS 2016

View Slide

References + software + code: @fellgernon
•  Collado-Torres, et al. Nucleic Acids Research (2016) doi: 10.1093/nar/gkw852
–  http://bioconductor.org/packages/derfinder
–  http://leekgroup.github.io/derSupplement/
•  Collado-Torres, et al. F1000Research (2015) doi:10.12688/f1000research.6379.1
-  http://www.bioconductor.org/packages/regionReport
-  http://leekgroup.github.io/regionReportSupp/
•  Collado-Torres and Nellore, et al. bioRxiv (2016) doi: 10.1101/068478
–  http://bioconductor.org/packages/recount
–  https://jhubiostatistics.shinyapps.io/recount
•  Nellore, Collado-Torres, et al. Bioinformatics (2016) doi:10.1093/bioinformatics/btw575
-  rail.bio
• Nellore, …, Collado-Torres, et al. bioRxiv (2016) doi:10.1101/038224
- intropolis.rail.bio
•  Jaffe, Shin, Collado-Torres, et al. Nat. Neurosci. (2015) doi:10.1038/nn.3898
–  https://github.com/lcolladotor/libd_n36
–  https://github.com/leekgroup/enrichedRanges

View Slide

SACNAS 2016

SACNAS 2016

Leonardo Collado-Torres

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