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
傾向スコアによる効果検証 / Propensity Score Analysis and Causal Effect Estimation
ikuma_w
0
110
白金鉱業Meetup Vol.16_【初学者向け発表】 数理最適化のはじめの一歩 〜身近な問題で学ぶ最適化の面白さ〜
brainpadpr
11
2.3k
安心・効率的な医療現場の実現へ ~オンプレAI & ノーコードワークフローで進める業務改革~
siyoo
0
310
アナログ計算機『計算尺』を愛でる Midosuji Tech #4/Analog Computing Device Slide Rule now and then
quiver
1
240
Masseyのレーティングを用いたフォーミュラレースドライバーの実績評価手法の開発 / Development of a Performance Evaluation Method for Formula Race Drivers Using Massey Ratings
konakalab
0
180
データベース15: ビッグデータ時代のデータベース
trycycle
PRO
0
320
データマイニング - コミュニティ発見
trycycle
PRO
0
130
05_山中真也_室蘭工業大学大学院工学研究科教授_だてプロの挑戦.pdf
sip3ristex
0
600
SpatialBiologyWestCoastUS2024
lcolladotor
0
170
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1k
生成検索エンジン最適化に関する研究の紹介
ynakano
2
1.3k
データマイニング - ノードの中心性
trycycle
PRO
0
250
Featured
See All Featured
Navigating Team Friction
lara
188
15k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
9
770
Building a Modern Day E-commerce SEO Strategy
aleyda
43
7.4k
[RailsConf 2023] Rails as a piece of cake
palkan
56
5.8k
Testing 201, or: Great Expectations
jmmastey
45
7.6k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
Code Review Best Practice
trishagee
69
19k
Documentation Writing (for coders)
carmenintech
73
5k
Being A Developer After 40
akosma
90
590k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
Building Adaptive Systems
keathley
43
2.7k
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]