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Reproducible Science: Cancer Researchers Embrace Containers in the Cloud
Tuesday, September 06, 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.
Genomic Data Processing on Google Cloud Platform
Tuesday, April 05, 2016
Posted by Dr. Stacey Gabriel, Director of the Genomics Platform at the Broad Institute of MIT and Harvard
Today we hear from Broad Institute of MIT and Harvard about how their researchers and software engineers are collaborating closely with the Google Genomics team on large-scale genomic data analysis. They’ve already reduced the time and cost for whole genome processing by several fold, helping researchers think even bigger. Broad’s open source tools, developed in close
collaboration with Google Genomics
, will also be made available to the wider research community.
– Jonathan Bingham, Product Manager, Google Genomics
Dr. Stacey Gabriel, Director of the
Genomics Platform at the Broad Institute
As one of the largest genome sequencing centers in the world, the
Broad Institute
of MIT and Harvard generates a lot of data. Our DNA sequencers produce more than 20 Terabytes (TB) of genomic data per day, and they run 365 days a year. Moreover, our rate of data generation is not only growing, but accelerating – our output increased more than two-fold last year, and nearly two-fold the previous year. We are not alone in facing this embarrassment of riches; across the whole genomics community, the rate of data production is doubling about every eight months with no end in sight.
Here at Broad, our team of software engineers and methods developers have spent the last year working to re-architect our production sequencing environment for the cloud. This has been no small feat, especially as we had to build the plane while we flew it! It required an entirely new system for developing and deploying pipelines (which we call
Cromwell
), as well as a new framework for wet lab quality control that uncouples data generation from data processing.
Courtesy: Broad Institute of MIT and Harvard
Last summer Broad and Google
announced a collaboration
to develop a safe, secure and scalable cloud computing infrastructure capable of storing and processing enormous datasets. We also set out to build cloud-supported tools to analyze such data and unravel long-standing mysteries about human health. Our engineers collaborate closely; we teach them about genomic data science and genomic data engineering, and they teach us about cloud computing and distributed systems. To us, this is a wonderful model for how a basic research institute can productively collaborate with industry to advance science and medicine. Both groups move faster and go further by working together.
As of today, the largest and most important of our production pipelines, the
Whole Genome Sequencing Pipeline
, has been completely ported to the
Google Cloud Platform
(GCP). We are now beginning to run production jobs on GCP and will be switching over entirely this month. This switch has proved to be a very cost-effective decision. While the conventional wisdom is that public clouds can be more expensive, our experience is that cloud is dramatically cheaper. Consider the curve below that my colleague Kristian Cibulskis recently showed at
GCP NEXT
:
Out of the box, the cost of running the
Genome Analysis Toolkit
(GATK) best practices pipeline on a 30X-coverage whole genome was roughly the same as the cost of our on-premise infrastructure. Over a period of a few months, however, we developed techniques that allowed us to
really
reduce costs: We learned how to parallelize the computationally intensive steps like aligning DNA sequences against a reference genome. We also optimized for GCP’s infrastructure to lower costs by using features such as
Preemptible VMs
. After doing these optimizations, our production whole genome pipeline was about 20% the cost of where we were when we started, saving our researchers millions of dollars, all while reducing processing turnaround time eight-fold.
There is a similar story to be told on storage of the input and output data.
Google Cloud Storage Nearline
is a medium for storing DNA sequence alignments and raw data. Like most people in genomics, we access genetic variants data every day, but raw DNA sequences only a few times per year, such as when there is a new algorithm that requires raw data or a new assembly of the human genome. Nearline’s price/performance tradeoff is well-suited to data that’s infrequently accessed. By using Nearline, along with some compression tricks, we were able to reduce our storage costs by greater than 50%.
Altogether, we estimate that, by using GCP services for both compute and storage, we will be able to lower the total cost of ownership for storing and processing genomic data significantly relative to our on premise costs. Looking forward, we also see advantages for data sharing, particularly for large multi-group genome projects. An environment where the data can be securely stored and analyzed will solve problems of multiple groups copying and paying for transmission and storage of the same data.
Porting the GATK whole genome pipeline to the cloud is just the starting point. During the coming year, we plan to migrate the bulk of our production pipelines to the cloud, including tools for arrays, exomes, cancer genomes, and RNA-seq. Moreover, our non-exclusive relationship with Google is founded on the principle that our groups can leverage complementary skills to make products that can not only serve the needs of Broad, but also help serve the needs of researchers around the world. Therefore, as we migrate each of our pipelines to the cloud to meet our own needs, we also plan to make them available to the greater genomics community through a Software-as-a-Service model.
This is an exciting time for us at Broad. For more than a decade we have served the genomics community by acting as a hub for data generation; now, we are extending this mission to encompass not only sequencing services, but also data services. We believe that by expanding access to our tools and optimizing our pipelines for the cloud, will enable the community to benefit from the enormous effort we have invested. We look forward to expanding the scope of this mission in the years to come.
Facilitating Genomics Research with Google Cloud Platform
Wednesday, July 30, 2014
Posted by Paul C. Boutros, Ontario Institute for Cancer Research, Josh Stuart, UC Santa Cruz, Adam Margolin, Oregon Health & Science University; Nicole Deflaux and Jonathan Bingham, Google Cloud Platform and Google Genomics
The understanding of the origin and progression of cancer remains in its infancy. However, due to rapid advances in the ability to accurately read and identify (i.e. sequence) the DNA of cancerous cells, the knowledge in this field is growing rapidly. Several
comprehensive
sequencing
studies
have shown that alterations of single base pairs within the DNA, known as
Single Nucleotide Variants
(SNVs), or duplications, deletions and rearrangements of larger segments of the genome, known as
Structural Variations
(SVs), are the
primary causes of cancer
and can influence what drugs will be effective against an individual tumor.
However, one of the major roadblocks hampering progress is the availability of accurate methods for interpreting genome sequence data. Due to the sheer volume of genomics data (the entire genome of just one person produces more than 100 gigabytes of raw data!), the ability to precisely localize a genomic alteration (SNV or SV) and resolve its association with cancer remains a considerable research challenge. Furthermore, preliminary benchmark studies conducted by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) have discovered that different mutation calling software run on the same data can result in detection of different sets of mutations. Clearly, optimization and standardization of mutation detection methods is a prerequisite for realizing personalized medicine applications based on a patient’s own genome.
The ICGC and TCGA are working to address this issue through an open community-based collaborative competition, run in conjunction with leading research institutions: the
Ontario Institute for Cancer Research
,
University of California Santa Cruz
,
Sage Bionetworks
,
IBM-DREAM
, and
Oregon Health and Sciences University
. Together, they are running the
DREAM Somatic Mutation Calling Challenge
, in which researchers from across the world “compete” to find the most accurate SNV and SV detection algorithms. By creating a living benchmark for mutation detection, the DREAM Challenge aims to improve standard methods for identifying cancer-associated mutations and rearrangements in tumor and normal samples from
whole-genome sequencing
data.
Given Google’s recent partnership with the
Global Alliance for Genomics and Health
, we are excited to provide cloud computing resources on Google Cloud Platform for competitors in the DREAM Challenge, enabling scientists who do not have ready access to large local computer clusters to participate with open access to contest data as well as credits that can be used for Google Compute Engine virtual machines. By leveraging the power of cloud technologies for genomics computing, contestants have access to powerful computational resources and a platform that allows the sharing of data. We hope to democratize research, foster the open access of data, and spur collaboration.
In addition to the core Google Cloud Platform infrastructure, the Google Genomics team has implemented a
simple web-based API
to store, process, explore, and share genomic data at scale. We have made the Challenge datasets available through the Google Genomics API. The challenge includes both simulated tumor data for which the correct answers are known and real tumor data for which the correct answers are not known.
Genomics API Browser
showing a particular cancer variant position (highlighted) in dataset
in silico #1
that was missed by many challenge participants.
Although submissions for the simulated data can be scored immediately, the winners on the real tumor data will not immediately be known when the challenge closes. This is a consequence of the fact that current DNA sequencing technology does not provide 100% accurate data, which adds to the complexity of the problem these algorithms are attempting to tackle. Therefore, to identify the winners, researchers must turn to alternative laboratory technologies to verify if a particular mutation that was found in sequencing data is actually (or likely) to be true. As such, additional data will be collected after the Challenge is complete in order to determine the winner. The organizers will re-sequence DNA from the cells of the real tumor using an independent sequencing technology (Ion Torrent), specifically examining regions overlapping the positions of the cancer mutations submitted by the contest participants.
As an analogy, a "scratched magnifying glass" is used to examine the genome the first time around. The second time around, a "stronger magnifying glass with scratches in different places" is used to look at the specific locations in the genome reported by the challenge participants. By combining the data collected by those two different "magnifying glasses", and then comparing that against the cancer mutations submitted by the contest participants, the winner will then be determined.
We believe we are at the beginning of a transformation in medicine and basic research, driven by advances in genome sequencing and computing at scale. With the DREAM Challenge, we are all excited to be part of bringing researchers around the world to focus on this particular cancer research problem. To learn more about how to participate in the challenge
register here
.
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