3. Clinical
Sequencing?
• first,
you’ve
got
to
have
reliable
measurements
• next,
you
need
to
know
how
reliable
they
are
• then,
you
need
clinical
interpreta2on
• in
prac2ce,
you’ll
need
infrastructure
to
enable
distributed
clinical
implementa2on
4. Clinical
Sequencing?
• first,
you’ve
got
to
have
reliable
measurements
• next,
you
need
to
know
how
reliable
they
are
• then,
you
need
clinical
interpreta2on
Genome-‐in-‐a-‐Bo*le
Consor0um
• in
prac2ce,
you’ll
need
infrastructure
to
to
work
on
these…
enable
distributed
clinical
implementa2on
7. Who
does
what?
• what
NIST
can/will
do
• what
members
can/will
do
– host
and
convene
– at
this
point,
it's
easiest
for
• NIST
is
non-‐regulatory,
neutral
membership
to
be
voluntary
broker
• without
requirements
for
• NIST
can
and
will
commit
to
CRADAs,
formal
declara2ons
development
and
of
membership
dissemina2on
of
products
– members
may
find
it
useful
to
– NIST
Reference
Materials
par2cipate
in
the
standard
• access
interna2onal
seRng
work
of
the
community
with
recognized
consor2um
reference
material
program
• collabora2on
on
development
• wide
regulatory
acceptance
of
RMs,
data,
tools,
and
documentary
standards
8. Genome-‐in-‐a-‐Bo.le
Consor2um
• NIST
hosted
three
small
• August
2012
workshops
to
address
– get
feedback
on
draX
Reference
Material
work
plan,
refine
needs
in
this
space
– get
working
groups
– Stanford,
June
2011
underway
– ASHG,
Montreal,
– iden2fy
resources
October
2011
• materials
• sequencing
– NIST,
Gaithersburg,
MD,
• analysis
April
2012
9. Agenda
Thursday
16
August
2012
Friday
17
August
2012
• Clinical
Applica2ons
of
WGS
• Report-‐out
from
Breakout
• WGS
Technology
Outlook
Groups
• 2
approaches
to
Genome
• Workplan
Refinement
Characteriza2on
• Resources
&
Next
Steps
• 5
minute
Topic
Talks
• NIST
work
to
develop
Whole
Genome
RMs
– in
partnership
with
FDA
• Breakout
Groups
10. Genome
in
a
Bo.le
Working
Groups
Reference
Material
Meaurements
for
Bioninforma2cs,
Performance
Metrics
Selec2on
Reference
Material
Data
Integra2on,
and
&
Figures
of
Merit
&
Design
Characteriza2on
Data
Representa2on
Andrew
Grupe,
Ellio.
Margulies,
Steve
Sherry,
NCBI
Jus2n
Johnson,
Celera
Illumina
EdgeBio
• Develop
priori2zed
list
• Develop
consensus
• Develop
plan
for
• User
interface
to
the
of
whole
human
plan
for
experimental
integra2ng
Genome-‐in-‐a-‐Bo.le
genomes
for
Reference
characteriza2on
of
experimental
data
and
Reference
Material
Materials
Reference
Materials
forming
consensus
• “Dashboard”
• Iden2fy
candidate
variant
calls
and
• what
an
end
user
will
approaches
and
confidence
es2mates
see
and
report
to
materials
for
ar2ficial
• Develop
consensus
understand
and
RMs
plan
for
data
describe
the
• Develop
priori2zed
list
representa2on
performance
of
their
experiment
• variant
call
accuracy
• process
performance
measures
to
enable
op2miza2on
12. Logis2cs
• Food
• This
is
an
open,
public
– NIST
Cafeteria
adjacent
to
mee2ng
this
auditorium
• Press
may
be
in
• Internet
Access
a.endance
– passwords
for
• There
will
be
an
open
NIST-Visitor
webinar
broadcast
of
this
are
in
your
folder
mee2ng
• Transporta2on
Ma.ers
– please
see
Angela
Ellis
or
• Speakers,
please
ask
Tia
Crawford
@
audience
to
refrain
from
registra2on
desk
twee2ng/blogging
if
you
so
desire
– otherwise,
it’s
fair
game
15. Genome
in
a
Bo.le
Working
Groups
Reference
Material
Meaurements
for
Bioninforma2cs,
Performance
Metrics
Selec2on
Reference
Material
Data
Integra2on,
and
&
Figures
of
Merit
&
Design
Characteriza2on
Data
Representa2on
Andrew
Grupe,
Ellio.
Margulies,
Steve
Sherry,
NCBI
Jus2n
Johnson,
Celera
Illumina
EdgeBio
• Develop
priori2zed
list
• Develop
consensus
• Develop
plan
for
• User
interface
to
the
of
whole
human
plan
for
experimental
integra2ng
Genome-‐in-‐a-‐Bo.le
genomes
for
Reference
characteriza2on
of
experimental
data
and
Reference
Material
Materials
Reference
Materials
forming
consensus
• “Dashboard”
• Iden2fy
candidate
variant
calls
and
• what
an
end
user
will
approaches
and
confidence
es2mates
see
and
report
to
materials
for
ar2ficial
• Develop
consensus
understand
and
RMs
plan
for
data
describe
the
• Develop
priori2zed
list
representa2on
performance
of
their
experiment
• variant
call
accuracy
• process
performance
measures
to
enable
op2miza2on
17. Some
use
scenarios…
• Obtain
metrics
for
valida2on,
QC,
QA,
PT
• Determine
sources
and
types
of
bias/error
• Learn
to
resolve
difficult
structural
variants
• Improve
reference
genome
assembly
• Op2miza2on
– integra2on
of
data
from
mul2ple
plagorms
– sequencing
and
analysis
• Enable
regulated
applica2ons