Short overview talk on exome and genome sequencing and DNAse-seq.

1. High-­‐Resolu,on
Views
of
Cancer
Genomes

3. The
Central
Dogma

6. +

8. Your
Nature
Paper

9. Our
First
Experiment

10. Overview
of
BAC
in
the
Genome

11. Sequencing
a
BAC

12. Sequence
Coverage

13. Repeats

14. Repeats

15. Repeats
are
not
created
equal

16. Genomic
Sequencing
TargeFng
the
Exome

17.  Long
oligos
synthesized
on
arrays
(DNA)
 RNA
baits
synthesized
from
DNA
oligo
template
 RNA
baits
hybridized
to
DNA
sequencing
library
 Targets
captured
using
beads
and
bioFn-­‐labeled
baits
 RNA
bait
degraded,
leaving
sequencing
library
enriched
for
target
regions

18. Data
Flow
 FASTQ
files
generated
by
Illumina
pipeline
 Aligned
to
reference
genome
(hg18,
excluding
_random,
unmapped,
and
hap)
using
Novoalign
 SAM/BAM
used
extensively
 Follow
Broad
InsFtute
GATK
pipeline
for
exome
capture
 Use
picard
java
library
for
quality
assessment
 Processed
BAM
files
available
via
local
hZp
for
browsing

19. Data
Pipeline....
 Samtools
import
 Samtools
sort
 Picard
MarkDuplicates
 GATK
Indel
Realignment
 GATK
Quality
RecalibraFon
 Picard
QC
metrics

20. Realignment
around
Indels
 The
problem
Aligners
align
each
read
independently
PotenFally
leads
to
increased
error
rates
around
indels
 A
potenFal
soluFon
Locally
realign
reads
in
regions
that
might
harbor
an
indel
Goal
is
to
align
reads
overlying
indels
more
accurately,
reducing
errors
in
each
read
and,
in
turn,
reducing
SNV
call
error
rates

21. Quality Recalibration
 Since most SNV callers will rely on quality scores to
estimate error probabilities, having the best possible
estimates for error rates is important
 Reported error rates from the Illumina sequencer
generally reflect technical parameters of the base call
process, but not other systematic biases
 Quality recalibration can include covariates to
account for systematic biases
Cycle count, dinucleotide context, original quality,
and sample/library variables

22. Variant
Calling
and
EvaluaFon
A
developing
art

24. Sequencing
Tumor/Normal
Pairs

25. Good
SNP

26. Suspect
Variant

27. SomaFc
(tumor
only)
Variant

28. Likely
False
PosiFve
(normal
only)

29. LOH

30. NCI60
Exome
Sequencing
No
Normals
Available!

33. Variants
by
Genomic
LocaFon

34. All
Coding
Variants

35. Type
1:
in
dbSNP,
Type
2:
not
in
dbSNP

36. Coding,
novel
(no
dbSNP)

38. Copy
Number
from
Exomes

42. Complete
Genome
Sequencing
Complete
Genomics
Data

43. Data
 Delivery
 Via
USB
results
 Storage
 Sizes
are
LARGE
400GB
per
sample
as
delivered
with
raw
reads
included
 Should
use
2-­‐locaFon
backed-­‐up
storage
Not
trivial
to
find
such
storage,
so
might
resort
to
mulFple
USB
drives
 Minimize:
Data
movement
Keeping
mulFple
copies
indefinitely

44. Breakdown
of
Data
Sizes

46. Data
 Delivery
 Storage
 Processing
 Data
are
typically
tab-­‐delimited
text
files,
so
Excel
can
be
useful
for
examining
individual
small
files
 Generally,
command-­‐line
tools
needed
 MacOS
and
linux
only
supported
operaFng
systems,
but
Windows
might
work....
 Some
analyses
(snpdiff)
require
large
memory

47. Directory
Structure

48. Workflows
 Tumor/Normal
 Copy
Number
 Structural
Varia,on
 Annotated
SomaFc
Variants
 Germline
 List
of
annotated
genotypes
per
individual,
summarized
into
a
single
file
that
can
be
used
for
filtering

49. Germline
Workflow

50. Germline
Workflow
 Output
 Future
direcFons
 Be
“smarter”
about
inheritance
framework
 Further
refinements
of
comparison
to
other
data
types
(exomes,
snp
arrays,
RNA-­‐seq)

51. Tumor/Normal
Workflow

52. Medvedev
et
al.,
Nature
2009

58. Frequent
geneFc
alteraFons
in
three
criFcal
signalling
pathways.
The
Cancer
Genome
Atlas
Research
Network
Nature
000,
1-­‐8
(2008)
doi:10.1038/nature07385

61. ChromaFn
 ChromaFn
is
the
complex
of
protein
and
DNA
that
make
up
the
chromosomes.
It
is
not
a
staFc
structure.

62.  DNAse
is
an
enzyme
that
cuts
DNA
at
locaFons
where
DNA
is
accessible
 These
“accessible”
regions
have
been
associated
with
open
chromaFn
 Regions
of
open
chromaFn
are
necessary
for
transcripFonal
and
regulatory
machinery
to
have
access
to
gene
neighborhoods
and
facilitate
transcripFon

63. DNAse
HypersensiFvity
 Method
for
finding
regions
of
“open”
chromaFn
 In
data
published
with
the
ENCODE
consorFum,
DNAse
hypersensiFve
(HS)
were
shown
to
be
correlated
with:
 Histone
modificaFon
 TranscripFon
start
sites
 Early
replicaFng
regions
 TranscripFon
factor
binding
sites
(experimentally
determined
by
ChIP/chip,
etc.)
IdenFficaFon
and
analysis
of
funcFonal
elements
in
1%
of
the
human
genome
by
the
ENCODE
pilot
project.
The
ENCODE
ConsorFum.
Nature,
2007.

64. DNAse-­‐chip
Method
Crawford,
G.E.,
Davis,
S.,
Scacheri,
P.C.,
Renaud,
G.,
Halawi,
M.J.,
Erdos,
M.R.,
Green,
R.,
Meltzer,
P.S.,
Wolfsberg,
T.G.,
and
Collins,
F.S.
Nat
Methods,
2006

65. DNAse-­‐Seq
Method
Crawford,
G.E.,
Davis,
S.,
Scacheri,
P.C.,
Renaud,
G.,
Halawi,
M.J.,
Erdos,
M.R.,
Green,
R.,
Meltzer,
P.S.,
Wolfsberg,
T.G.,
and
Collins,
F.S.
Nat
Methods,
2006

67. DNAse
Sites
RelaFve
to
Genes

68. DNAse
HS
Sites
and
Gene
Expression
 DNAse
HS
sites
near
transcripFon
start
sites
are
associated
with
acFvely
transcribed
genes.

70. Nucleosome
PosiFoning
 Distances
between
sequences
in
non-­‐DNAse
HS
regions
have
an
oscillaFng
paZern
with
frequency
that
corresponds
to
a
single
turn
of
the
double-­‐
helix
 DNAse
is
known
to
cut
preferenFally
in
the
minor
groove,
which
is
exposed
every
10.4
bases
when
wrapped
around
a
nucleosome
 A
nucleosome
is
wrapped
by
147
base
pairs
when
complexed
with
DNA
 ImplicaFon:
Nucleosomes
are
posiFoned
in
a
highly
organized,
precise
manner

73. The
Last
Mile