Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
The New Genomics
Search
Matt Wood
October 02, 2012
Science
3
14k
The New Genomics
The value of reproducing, reusing and remixing scientific research. Slides from Strata London.
Matt Wood
October 02, 2012
Tweet
Share
More Decks by Matt Wood
See All by Matt Wood
Field Notes from Expeditions in the Cloud
mza
2
410
A Platform for Big Data
mza
6
760
The Data Lifecycle
mza
5
520
Provision Throughput Like a Boss
mza
0
450
Impact of Cloud Computing: Life Sciences
mza
2
870
Latency's Worst Nightmare: Performance Tuning Tips and Tricks
mza
4
1.1k
Under the Covers of DynamoDB
mza
4
1.1k
From Analytics to Intelligence: Amazon Redshift
mza
9
1k
Scaling Science
mza
3
520
Other Decks in Science
See All in Science
A Guide to Academic Writing Using Generative AI - A Workshop
ks91
PRO
0
130
ド文系だった私が、 KaggleのNCAAコンペでソロ金取れるまで
wakamatsu_takumu
2
1.2k
データベース11: 正規化(1/2) - 望ましくない関係スキーマ
trycycle
PRO
0
940
データマイニング - ノードの中心性
trycycle
PRO
0
270
Symfony Console Facelift
chalasr
2
460
02_西村訓弘_プログラムディレクター_人口減少を機にひらく未来社会.pdf
sip3ristex
0
600
データベース04: SQL (1/3) 単純質問 & 集約演算
trycycle
PRO
0
980
AI(人工知能)の過去・現在・未来 —AIは人間を超えるのか—
tagtag
1
120
CV_5_3dVision
hachama
0
150
論文紹介 音源分離:SCNET SPARSE COMPRESSION NETWORK FOR MUSIC SOURCE SEPARATION
kenmatsu4
0
310
白金鉱業Meetup Vol.16_数理最適化案件のはじめかた・すすめかた
brainpadpr
4
2k
Agent開発フレームワークのOverviewとW&B Weaveとのインテグレーション
siyoo
0
330
Featured
See All Featured
GraphQLとの向き合い方2022年版
quramy
49
14k
Imperfection Machines: The Place of Print at Facebook
scottboms
268
13k
The Illustrated Children's Guide to Kubernetes
chrisshort
48
50k
Docker and Python
trallard
45
3.6k
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
18
1.1k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
32
1.5k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
51
5.6k
It's Worth the Effort
3n
187
28k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.5k
Large-scale JavaScript Application Architecture
addyosmani
512
110k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
285
13k
Rebuilding a faster, lazier Slack
samanthasiow
83
9.2k
Transcript
The New Genomics
[email protected]
Dr. Matt Wood
Hello
Hello
Data
DNA
Chromosome 11 : ACTN3 : rs1815739
Chromosome X : rs6625163
Chromosome 19 : FUT2 : rs601338
+0.25 Chromosome 15 : rs2472297
Chromosome 2 : rs10427255
TYPE II Chromosome 10 : rs7903146
Chromosome 1 : rs4481887
I know this, because...
None
A T C G G T C C A G
G
A T C G G T C C A G
G A G C C A G G U C C Transcription
A T C G G T C C A G
G A G C C A G G U C C Translation Ser Glu Val Transcription
None
None
Chromosome 11 : ACTN3 : rs1815739
Chromosome X : rs6625163
Chromosome 19 : FUT2 : rs601338
+0.25 Chromosome 15 : rs2472297
Chromosome 2 : rs10427255
TYPE II Chromosome 10 : rs7903146
Chromosome 1 : rs4481887
I know all that, because...
Human Genome Project
40 species ensembl.org
Compare species
Biological importance
Step change
Less time. Lower cost.
None
None
Compare individuals
None
Data generation costs are falling (pretty much everywhere)
Sequencing challenge X
Amazona vittata
Analytics challenge
Lots of data, Lots of uses, Lots of users, Lots
of locations
Cost
Analytics challenge X
Accessibility challenge
The New Genomics
Graceful. Beautiful.
Impossible to re-create
Snowflake Science
Reproducibility
Reproducibility scales science
Reproduce. Reuse. Remix.
Value++
None
How do we get from here to there? 5PRINCIPLES REPRODUCIBILITY
OF
1. Use the gravity of data 5 PRINCIPLES REPRODUCIBILITY OF
Increasingly large data collections
1000 Genomes Project: 200Tb
Challenging to obtain and manage
Expensive to experiment
Large barrier to reproducibility
Data size will increase
Data integration will increase
Move data to the users
Move data to the users X
Move tools to the data
Place data where it can consumed by tools
Place tools where they can access data
None
None
None
Canonical source
None
More data, more users, more uses, more locations
Cost and complexity
Cost and complexity kill reproducibility
Utility computing
Availability
Intel Xeon E5 NVIDIA Tesla GPUs
90 - 120k IOPS on SSDs
Pay-as-you-go
100% Reserved capacity
100% Reserved capacity On-demand
100% Reserved capacity On-demand
Spot instances
Name-your-price
None
2. Ease of use is a pre-requisite 5 PRINCIPLES REPRODUCIBILITY
OF
http://headrush.typepad.com/creating_passionate_users/2005/10/getting_users_p.html
Help overcome the suck threshold
Easy to embrace and extend
Choose the right abstraction for the user
$ ec2-run-instances
$ starcluster start
None
None
Package and automate
Package and automate Amazon machine images, VM import
Package and automate Amazon machine images, VM import Deployment scripts,
CloudFormation, Chef, Puppet
Expert-as-a-service
None
None
1000 Genomes Cloud BioLinux
None
Your HiSeq data Illumina BaseSpace
DNA and RNA sequences Genomespace, Broad Institute at MIT
Data as a programmable resource
3. Reuse is as important as reproduction 5 PRINCIPLES REPRODUCIBILITY
OF
Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
Infonauts are hackers
They have their own way of working
The ‘Big Red Button’
Fire and forget reproduction is a good first step, but
limits longer term value.
Monolithic, one-stop-shop
Work well for intended purpose
Challenging to install, dependency heavy
Inflexible
Embrace infonauts as hackers
Small things. Loosely coupled.
Easier to reuse
Easier to integrate
Scale out
Cancer drug discovery: 50,000 cores < $1000 an hour Schrödinger
and CycleServer
4. Build for collaboration 5 PRINCIPLES REPRODUCIBILITY OF
Workflows are memes
Reproduction is just the first step
Bill of materials: code, data, configuration, infrastructure
Full definition for reproduction
Utility computing provides a playground for data science
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Package, automate, contribute.
Utility platform provides scale for production runs
5. Provenance is a first class object 5 PRINCIPLES REPRODUCIBILITY
OF
Versioning becomes really important
Especially in an active community
Doubly so with loosely coupled tools
Provenance metadata is a first class entity
Distributed provenance
5PRINCIPLES REPRODUCIBILITY OF
Remove constraints 5PRINCIPLES REPRODUCIBILITY OF
Accelerate science 5PRINCIPLES REPRODUCIBILITY OF
Chromosome 11 : ACTN3 : rs1815739
Chromosome X : rs6625163
Chromosome 19 : FUT2 : rs601338
+0.25 Chromosome 15 : rs2472297
Chromosome 2 : rs10427255
TYPE II Chromosome 10 : rs7903146
Chromosome 1 : rs4481887
Thank you aws.amazon.com @mza
[email protected]