Blog
The latest news from Google AI
Reproducible Science: Cancer Researchers Embrace Containers in the Cloud
Tuesday, September 6, 2016
Posted by Dr. Kyle Ellrott, Oregon Health and Sciences University, Dr. Josh Stuart, University of California Santa Cruz, and Dr. Paul Boutros, Ontario Institute for Cancer Research
Today we hear from the principal investigators of the ICGC-TCGA DREAM Somatic Mutation Calling Challenges about how they are encouraging cancer researchers to make use of Docker and Google Cloud Platform to gain a deeper understanding of the complex genetic mutations that occur in cancer, while doing so in a reproducible way.
– Nicole Deflaux and Jonathan Bingham, Google Genomics
Today’s genomic analysis software tools often give different answers when run in different computing environments - that’s like getting a different diagnosis from your doctor depending on which examination room you’re sitting in.
Reproducible
science matters, especially in cancer research where so many lives are at stake. The
Cancer Moonshot
has called for the research world to '
Break down silos and bring all the cancer fighters together
'. Portable software “
containers
” and cloud computing hold the potential to help achieve these goals by making scientific data analysis more reproducible, reusable and scalable.
Our team of researchers from the
Ontario Institute for Cancer Research
,
University of California Santa Cruz
,
Sage Bionetworks
and
Oregon Health and Sciences University
is pushing the frontiers by encouraging scientists to package up their software in reusable
Docker
containers and make use of cloud-resident data from the
Cancer Cloud Pilots funded by the National Cancer Institute
.
In 2014 we initiated the
ICGC-TCGA DREAM Somatic Mutation Calling (SMC) Challenges
where Google provided credits on
Google Cloud Platform
. The first result of this collaboration was the DREAM-SMC DNA challenge, a public challenge that engaged cancer researchers from around the world to find the best methods for discovering
DNA somatic mutations
. By the end of the challenge, over 400 registered participants competed by submitting 3,500 open-source entries for 14 test genomes,
providing key insights
on the strengths and limitations of the current mutation detection methods.
The SMC-DNA challenge enabled comparison of results, but it did little to facilitate the exchange of cross-platform software tools. Accessing extremely large genome sequence input files and shepherding complex software pipelines created a “double whammy” to discourage data sharing and software reuse.
How can we overcome these barriers?
Exciting developments have taken place in the past couple of years that may annihilate these last barriers. The availability of cloud technologies and
containerization
can serve as the vanguards of reproducibility and interoperability.
Thus, a new way of creating open DREAM challenges has emerged: rather than encouraging the status quo where participants run their own methods themselves on their own systems, and the results cannot be verified, the new challenge design requires participants to submit open-source code packaged in Docker containers so that anyone can run their methods and verify the results. Real-time leaderboards show which entries are winning and top performers have a chance to claim a prize.
Working with Google Genomics and Google Cloud Platform, the DREAM-SMC organizers are now using cloud and containerization technologies to enable portability and reproducibility as a core part of the DREAM challenges. The latest SMC installments, the
SMC-Het Challenge
and the
SMC-RNA Challenge
have implemented this new plan:
SMC-Het Challenge
: Tumour biopsies are composed of many different cell types in addition to tumour cells, including normal tissue and infiltrating immune cells. Furthermore, the tumours themselves are made of a mixture of different subpopulations, all related to one another through cell division and mutation. Critically, each sub-population can have distinct clinical outcomes, with some more resistant to treatment or more likely to metastasize than others. The goal of the SMC-Het Challenge is to identify the best methods for predicting
tumor subpopulations
and their “family tree” of relatedness from genome sequencing data.
SMC-RNA Challenge
: The alteration of RNA production is a fundamental mechanism by which cancer cells rewire cellular circuitry. Genomic rearrangements in cancer cells can produce fused protein products that can bestow Frankenstein-like properties. Both RNA abundances and novel fusions can serve as the basis for clinically-important prognostic biomarkers. The SMC-RNA Challenge will identify the best methods to detect such rogue expressed RNAs in cancer cells.
Ultimately, the success will be gauged by the amount of serious participation in these latest competitions. So far, the signs are encouraging. SMC-Het, which focuses on a very new research area, launched in November 2015 and has already enlisted 18 teams contributing over 70 submissions. SMC-RNA just recently launched and will run until early 2017, with several of the world leaders in the field starting to prepare entries. What’s great about the submissions being packaged in containers is that even after the challenges end, the tested methods can be applied and further adapted by anyone around the world.
Thus, the moon shot need not be a lucky solo attempt made by one hero in one moment of inspiration. Instead, the new informatics of clouds and containers will enable us to combine intelligence so we can build a series of bridges from here to there.
To participate in the DREAM challenges, visit the
SMC-Het
and
SMC-RNA
Challenge sites.
Labels
2018
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2019
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
