In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.

1. Cloud Accelerated Genomics
Allen Day, PhD // Science Advocate
@allenday // #genomics #ml #datascience

2. Table of Contents
Section 1
Section 2
Section 3
Throughout
Getting from Research to Application… Faster
What are the bottlenecks for translating research into products?
Emphasis on information processing.
From CompBio Research to CompBio Engineering
Getting results, more of them, and predictably improving
Data Integration - Cutting Edge Use Cases
What’s happening right now in industry and academia?
How to use Google Cloud?
I’ll introduce specific cloud services, along with examples of
how they’ve been used successfully. Compute Engine,
Kubernetes, Dataflow, Cloud ML, Genomics API

3. How to Understand?
Linear B is a syllabic script
that was used for writing
Mycenaean Greek, the
earliest attested form of
Greek. The script predates
the Greek alphabet by
several centuries. The oldest
Mycenaean writing dates to
about 1450 BC.

4. Hypothetico-Deductive
Method (Iterative)
Organize
Analyze,
Interpret, and
Plan
Choose Data
Acquire

5. Hypothetico-Deductive
Method (Iterative)
Organize
Analyze,
Interpret, and
Plan
Choose Data
Acquire
Situation:
Not enough data.
No means to get more.
Dead Language.
Outcome:
Cannot understand.
Also:
Passive learning.
No feedback.

6. DNA Sequencing Value Chain
%Effort
0
100
Pre-NGS
~2000
Future
~2020
Now
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
Secondary
Analytics
Analytics,
Intepretation,
Planning
Experiment
Design
DNA
Sequencing

7. Human Genetics Scenario
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
Secondary
Analytics
Analytics,
Intepretation,
Planning
Experiment
Design
%Effort
0
100
DNA
Sequencing
Situation:
Unlimited Free DNA
Result:
Slow to understand.
Pre-NGS
~2000
Future
~2020
Now

8. Q: Why Slow to Understand? A1: Data Processing
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
Secondary
Analytics
Analytics,
Intepretation,
Planning
Experiment
Design
%Effort
0
100
DNA
Sequencing
Situation:
We still have an
analysis bottleneck
Result:
Slow to understand.
Pre-NGS
~2000
Future
~2020
Now

9. 00:20 - Connecting…
01:22 - Link Established

11. GOOGLE CONFIDENTIAL
Google Cloud Platform lets you run your apps on the
same system as Google

12. GOOGLE CONFIDENTIAL
So you can focus on what matters
to your science

13. Google confidential │ Do not distribute
Google is good at handling massive volumes of data
uploads per minute
users
search index
query response time
300hrs
500M+
100PB+
0.25s

14. Google confidential │ Do not distribute
Google can is good at handleing massive volumes of genomic data
uploads per minute
users
search index
query response time
300hrs
500M+
100PB+
0.25s
~6WGS
>100x US PhDs
~1M WGS
0.25s

15. Google confidential │ Do not distributeGoogle confidential │ Do not distribute
Google Genomics
August 2015

16. Google confidential │ Do not distribute
Google Genomics is more than infrastructure
General-purpose
cloud infrastructure
Genomics-specific
featuresGenomics API
Virtual Machines & Storage
Data Services & Tools

17. Google confidential │ Do not distribute
BioQuery Analysis Engine
Medical Records Genomics Devices Imaging Patient Reports
Baseline Study Data Private Data
Pharma Health Providers …
Google’s vision to tackle complex health data
Public Data

18. Google confidential │ Do not distribute
BioQuery Analysis Engine
Medical Records Genomics Devices Imaging Patient Reports
Baseline Study Data Private Data
Pharma Health Providers …
Google’s vision to tackle complex health data
Public Data

19. CONFIDENTIAL & PROPRIETARY
3.75 TERABYTES PER HUMAN
1.00 TB GENOME
2.00 TB EPIGENOME
0.70 TB TRANSCRIPTOME
0.06 TB METABOLOME
0.04 TB PROTEOME
~1 MB STANDARD LAB TESTS
5-YR LONGITUDINAL STUDY
BASELINE STUDY: BIG DATA ANALYSIS
Validate a pipeline to process complex phenotypic, biochemical,
and genomic data
● Pilot Study (N=200)
○ Determine optimal biospecimen collection strategy for stable sampling
and reproducible assays
○ Determine optimal assay methodology
○ Validate quality control methods
○ Validate device data against surrogate and primary endpoints
● Baseline Study (N=10,000+)
○ 6 cohorts from low to high risk for cardiovascular and cancer
○ Characterize human systems biology
○ Define normal values for a given parameter in heterogeneous states
○ Predict meaningful events
○ Validate wearable devices for human monitoring
○ Characterize transitions in disease state

20. Public Datasets Project
https://cloud.google.com/bigquery/public-data/
A public dataset is any dataset that is stored in BigQuery and made available to the general public. This URL lists a
special group of public datasets that Google BigQuery hosts for you to access and integrate into your applications.
Google pays for the storage of these data sets and provides public access to the data via BigQuery. You pay only for the
queries that you perform on the data (the first 1TB per month is free)

21. Confidential & ProprietaryGoogle Cloud Platform 21
Platinum Genomes
1000 Genomes
Medical (Human)
Population-scale Genome Projects
1000 Bulls
10K Dog Genomes
Veterinary / Agriculture
Open Cannabis Project
Genome To Fields
Panzea (1000 Maize)
AgriculturePersonal Genome Project
Human Microbiome Project
NCBI GEO Human 100K
Cancer Genome Atlas
Many Other
Interesting
Datasets...

22. Google confidential │ Do not distribute
PI / Biologist : variant calls for the 1,000 genomes

23. Google confidential │ Do not distribute
Information: principal coordinates analysis (1000 genomes)

24. Google confidential │ Do not distribute
Knowledge: populations cluster together

25. Bioinformatics scientist: BigQuery enables fast tertiary analysis

26. Google Cloud Platform
Dataflow + BigQuery
Used for Extract, Transform,
Load (ETL), analytics,
real-time computation and
process orchestration.
cloud.google.com/dataflow
Dataflow
Run SQL queries against
multi-terabyte datasets in
seconds.
cloud.google.com/bigquery
BigQuery

27. Google Cloud Platform
Dataflow + BigQuery
Used for Extract, Transform,
Load (ETL), analytics,
real-time computation and
process orchestration.
cloud.google.com/dataflow
Dataflow
Run SQL queries against
multi-terabyte datasets in
seconds.
cloud.google.com/bigquery
BigQuery

28. Google Cloud Platform
Dataflow + BigQuery

29. Google confidential │ Do not distribute
Example: GATK
Analysis Pipeline
Old way: install
applications on host
kernel
libs
app
app app
app
Makefiles,
CWL, WDL
(on a virtual machine)

32. Google confidential │ Do not distribute
Example: GATK
Analysis Pipeline
Old way: install
applications on host
kernel
libs
app
app app
app
Makefiles,
CWL, WDL
(on a virtual machine)

33. Google confidential │ Do not distribute
Example: GATK
Analysis Pipeline
● Decouple process
management from
host configuration
● Portable across OS
distros and clouds
● Consistent
environment from
development to
production
● Immutable images
New way: deploy
containers
Old way: install
applications on host
kernel
libs
app
app app
app
libs
app
kernel
libs
app
libs
app
libs
app
Makefiles,
CWL, WDL
(on a virtual machine)
Dockerflow:
Dataflow + Docker
Benefits

34. Google confidential │ Do not distribute
Use Case:
Reproducible Science with Docker
● Objective: Build a mutation-detection pipeline
● Provided to competitors
○ Training data set
○ Evalutation data set
● Competitors submit pipelines as Docker images to DREAM Challenge host, Sage Bionetworks
● Submitted pipelines were used to process unseen data set
● Post-competition, Docker images made public
● Incidentally, Google won this competition with a deep-learning based variant caller called
DeepVariant cloud.google.com/genomics/v1alpha2/deepvariant

35. Confidential & ProprietaryGoogle Cloud Platform 35
An idealized version of the
hypothetico-deductive
model of the scientific
method is shown. Various
potential threats to this
model exist (indicated in
red), including
hypothesizing after the
results are known
(HARKing) and lack of
data sharing. Together
these undermine the
robustness of results, and
may impact on the ability
of science to self-correct.
Threats to
reproducible
science.
http://www.nature.com/articles/s41562-016-0021

36. > java -jar target/dockerflow*dependencies.jar
--project=YOUR_PROJECT
--workflow-file=hello.yaml
--workspace=gs://YOUR_BUCKET/YOUR_FOLDER
--runner=DataflowPipelineRunner
To run it:
Variant Calls
Your Variant Caller
36PubSub
Queue
Sequencer
DNA Reads
Genomics
API
Genomics
API
BigQuery
Your Other Tool

37. GraphConnect SF 2015 / Graphs Are Feeding The World, Tim Williamson, Data Scientist, Monsanto
https://www.youtube.com/watch?v=6KEvLURBenM

38. GraphConnect SF 2015 / Graphs Are Feeding The World, Tim Williamson, Data Scientist, Monsanto
https://www.youtube.com/watch?v=6KEvLURBenM

39. Marker-assisted selection for quantitative traits

40. Marker-assisted selection for quantitative traits
https://www.sec.gov/Archives/edgar/data/1110783/0000950134
02011773/c71992exv99w2.htm

41. Google Cloud Platform
Marker-Assisted Breeding Rapidly Increases Frequency of
Favorable Genes
https://www.slideshare.net/finance28/monsanto-082305a

42. Q: Why Slow to Understand? A1: Data Processing
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
Secondary
Analytics
Analytics,
Intepretation,
Planning
Experiment
Design
%Effort
0
100
DNA
Sequencing
Situation:
We still have an
analysis bottleneck
Result:
Slow to understand.
Pre-NGS
~2000
Future
~2020
Now

43. Q: Why Slow to Understand? A2: Limited Feedback
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
Secondary
Analytics
Analytics,
Intepretation,
Planning
Experiment
Design
DNA
Sequencing
Situation:
Data acquisition cost approaches zero
However, still slow to understand, because:
1. Restricted choice of what can be observed, i.e. controlled
modifications and artificial selection
2. Passive Learning. Limited feedback => Low rate of learning
Contrast with active learning...

44. Act
Observe
Observe
Act
Orient Decide
Decide Act
Biological System
Scientist
Molecular Sensors:
DNA sequencer,
Mass spectrometer,
Etc
However...
(Technology)-Limited
Experimental Capability

45. Google Cloud Platform
Even Moore’s Law / Carlson Curve

46. Google Cloud Platform
Even Moore’s Law / Carlson Curve - also applies to writing DNA

47. Act
Observe
Observe
Act
Orient Decide
Decide Act
Biological System
Scientist
Molecular Sensors:
DNA sequencer,
Mass spectrometer,
Etc
Bioengineering Tech:
DNA synthesizers,
CRISPR/Cas9,
Etc

48. Act
Observe
Observe
Act
Orient Decide
Decide Act
Biological System
Scientist
Molecular Sensors:
DNA sequencer,
Mass spectrometer,
Etc
Environmental Sensors:
Laser scanners,
Hyperspectral scanners,
UAVs
Etc
Bioengineering Tech:
DNA synthesizers,
CRISPR/Cas9,
Etc
Regulate/Measure
System I/O

49. Google Cloud Platform
Integration with Geospatial, Management, and Terrestrial Sensor Data
anezconsulting.com/precision-agronomy/

50. Google Cloud Platform
Descartes Labs - Google Cloud Customer
medium.com/@stevenpbrumby/corn-in-the-usa-d487dce84ee1
Cloud ML
Engine
TensorFlow

51. Google Cloud Platform
Phenomobile, http://www.mdpi.com/2073-4395/4/3/349/htm
See also: http://www.genomes2fields.org/

52. Google Cloud Platform
Temporo-Spatial Imaging of Growing Plants

53. Google Cloud Platform
Verily: Assisting Pathologists in Detecting Cancer with Deep Learning
research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
Prediction heatmaps produced by the algorithm had
improved so much that the localization score (FROC)
for the algorithm reached 89%, which significantly
exceeded the score of 73% for a pathologist with no
time constraint2
. We were not the only ones to see
promising results, as other groups were getting scores
as high as 81% with the same dataset.
Model generalized very well, even to images that were
acquired from a different hospital using different
scanners. For full details, see our paper “Detecting
Cancer Metastases on Gigapixel Pathology Images”.

54. 00:20 - Connecting…
01:22 - Link Established

55. Google Cloud Platform
~~)( ,
Cloud VisionTensorFlowGoogle Genomics Dataflow Cloud ML Engine Docker
Baseline Study Data Private DataPublic Data

56. Build What’s Next
Thank You!
Allen Day, PhD // Science Advocate // @allenday // #genomics #ml #datascience