computing in the age of the $1000 genome

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Computing in the age of the $1000 Genome William Spooner [email protected]

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A look at trends

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Page 1: Computing in the Age of the $1000 Genome

Computing in the age of the

$1000 Genome

William [email protected]

Page 2: Computing in the Age of the $1000 Genome

Biomarkers for Pharmacogenomics

Right doseRight drugRight patientRight time

PERSONALIS

ED MEDIC

INE

Page 3: Computing in the Age of the $1000 Genome

Biomarkers for Plant Breeding

Page 4: Computing in the Age of the $1000 Genome

Discovering Biomarkers in Sequence

Genotype;

Genome/exome resequencing,

Genome Wide Association Studies (GWAS).

Gene expression;

RNA-Seq,

Expression Quantative Trait Loci (eQTL).

Epigenetic;

ChIP-Seq, Bisulfide-Seq, sequence capture,

Epigenetic GWAS (eGWAS).

Metagenomic.MISSIN

G HERITA

BILITY

Page 5: Computing in the Age of the $1000 Genome

Bioinformatics Areas

%

Gene

expr

ession

Genom

ic v

aria

tion

Compa

rativ

e ge

nom

ics

Path

way

ana

lysis

Syst

ems bi

olog

y

Epig

enet

ics

Gene

pred

ictio

n

Biom

arke

r disco

very

Prot

eom

ics

Met

agen

omics

Met

abol

omics

0

20

40

60

80

100

120

Not anticipated

In the future

Currently

The big twoBlockbuster

>70%Mainstream

50-70%

Specialty<50%

Specialty

Page 6: Computing in the Age of the $1000 Genome

Areas, Public V. Private

Gene

expr

ession

Genom

ic v

aria

tion

Compa

rativ

e ge

nom

ics

Path

way

s

Syst

ems bi

olog

y

Epig

enet

ics

Gene

pred

ictio

n

Biom

arke

r disco

very

Prot

eom

ics

Met

agen

omics

Met

abol

omics

0

10

20

30

40

50

60

70

80

90

100

PUBLICPRIVATE

%

MatureEmerging Commercialised

Page 7: Computing in the Age of the $1000 Genome

Sequencing Technology

Capillary Sequencing

Array sequencing

1998

20052006

2010

2010201?

Sequence Cost

Read Length

Quality

$1000

OPT-ICAL

AMPL-IFIED

ELECT-RICAL

AMPLI-FIED

OPT-ICAL

SINGLE MOL

ELECT-RICAL

SINGLE MOL

Page 8: Computing in the Age of the $1000 Genome

Turner – the Deluge

“Data tsunami”

“Tidal wave of data”

Page 9: Computing in the Age of the $1000 Genome

The Sequencing Cliff

$1000 genome~$0.01 Mbase (30x coverage)

Page 10: Computing in the Age of the $1000 Genome

Bioinformatics Crash Landing?

What needs to change?

The following must increase:

1. Hardware scalability,

2. In-house bioinformaticians, and/or bioinformatics outsourcing

3. Software quality.

Page 11: Computing in the Age of the $1000 Genome

1. Hardware scalability

On desktop PCsOn serversOn clustersOn the cloud

Yes

Yes

No

No

Page 12: Computing in the Age of the $1000 Genome

2. In-house Vs. Outsourcing

Respo

nse

to cha

nge

Data

secu

rity

Supp

ort c

ost

Acce

ss to

exp

ertis

e

Devel

opm

ent c

ost

Prod

uct q

ualit

y

Spee

d of

del

iver

y

Wor

kloa

d sc

alab

ility

Overa

ll pr

ojec

t cos

t

Overa

ll pr

ojec

t risk

0

10

20

30

40

50

60

70

80

90

100

Outsourced betterNeitherIn-house better

%

In-house wins overall

Outsource wins in scalability

Page 13: Computing in the Age of the $1000 Genome

3. Software Quality

Stab

ility

/relia

bilit

y

Scie

ntifi

c va

lidat

ion

Compu

tatio

nal e

fficien

cy

Easy

to in

stal

l/mai

ntai

n

Visu

al re

pres

enta

tion

Secu

rity

Inte

grat

ion

Ease

of u

se

Avai

labi

lity

of tr

aini

ng

Comm

ercial

sup

port

0

20

40

60

80

100

120

IrreleventUsefulImportant

%

Technical attributes win

Technical attributes win

Technical attributes win

Usability attributes lose

Usability attributes lose

Usability attributes lose

Page 14: Computing in the Age of the $1000 Genome

BUT…CAN THIS APPROACH SCALE?

Bioinformaticians like to;

• Develop their own solutions,

• Using open-source software,

• That’s stable, reliable, and published

Bioinformaticians don’t like to;

•Outsource, or use commercial software,

•Develop user-friendly, supported software.

Page 15: Computing in the Age of the $1000 Genome

Is this the Answer?

“Genome Content Management is the set of processes and technologies that support the creating, managing, and reporting of genomic data.”

Create

Man

ag

e

Report

Create

Report

Ext

end

Manage

Share

Reuse

TIMELINE: 100% Bespoke…..…Common Schemas/APIs…..…Content Management Systems

Page 16: Computing in the Age of the $1000 Genome

An Open Source Service Company

Consultancy/advice Training Support Installation/Integration Customization Out sourced management

BusinessOpen Community(e.g. Academia)

Service Company

ServiceCollaboration

Page 17: Computing in the Age of the $1000 Genome

And Finally

• The survey closes at the end of march,

• Those that participated, thanks!

• Those that did not, please participate!

• Tell your friends! Tell your enemies!

• We will run it again next year; suggestions?

http://eaglegenomics.com/survey

Page 18: Computing in the Age of the $1000 Genome

Genome Content Management Systems (G-CMS)

Wo

rkfl

ow

Ori

ente

d

Datab

ase Orien

ted

Open Source Proprietary