Share and analyse genomic data at scale with Spark, Adam, Tachyon & the Spark Notebook Sharp intro to Genomics data What are the Challenges Distributed Machine Learning to the rescue Projects: Distributed teams Research: Long process Towards Maximum Share for efficiency

1. Share and analyse genomic data
at scale
with Spark, Adam, Tachyon & the Spark Notebook
by @DataFellas, Oct • 29th • 2015

2. Outline
● Sharp intro to Genomics data
● What are the Challenges
● Distributed Machine Learning to the rescue
● Projects: Distributed teams
● Research: Long process
● Towards Maximum Share for efficiency

3. Andy Petrella
Maths
Geospatial
Distributed Computing
Spark Notebook
Trainer Spark/Scala
Machine Learning
Xavier Tordoir
Physics
Bioinformatics
Distributed Computing
Scala (& Perl)
trainer Spark
Machine Learning
“There must be another way of doing the credits” -- Robin Hood: Men in Tights (1993, Mel Brooks)

4. Analyse Genomic At Scale
Spark, Adam, Spark Notebook
➔ Sharp intro to Genomics data
➔ What are the Challenges
➔ Distributed Machine Learning to the rescue

5. What is genomics data?
DNA?
What makes us what we are…
… a complex biochemical soup.
With applications to medical diagnostics, drug response,
disease mechanisms

6. What is genomics data?
DNA?
What makes us what we are…
… a complex biochemical soup.
With applications to medical diagnostics, drug response,
disease mechanisms

7. On the production side
Fast biotech progress…
… can IT keep up?

8. On the production side
Sequence {A, T, G, C}
3 billion characters (bases)

9. On the production side
Sequence {A, T, G, C}
3 billion characters (bases)
… x 30 (x 60)
Massively parallel

10. Lots of data?

11. Lots of data?
10’s millions

12. Lots of data!
10’s millions
1,000s
1,000,000s
...

13. ADAM: Spark genomics library
http://www.bdgenomics.org
Matt Massie
Frank Nothaft

14. ADAM: Spark genomics library

15. ADAM: Spark genomics library

16. ADAM: Spark genomics library

17. ADAM: Spark genomics library
Avro schema
Parquet storage
Genomics API

18. So what do we do with this?
Study variations between populations
Descriptive statistics
Machine Learning (Population stratification or Supervised
learning)
… and share and replay!

19. The Spark Notebook
… comes to the rescue.
+ Self described and consistent
+ Easily shared (code)
+ Scala (types, production quality)
+ Reactive&pluggage charts API (scala = no.js)
+ easy install, no deps.
+ multiple sparkContext
http://www.spark-notebook.io

20. The Spark Notebook

21. The Spark Notebook

22. The Spark Notebook

23. So what do we do with this?
… and share and replay!
Code can be shared easily but we want more...
How do we share data produced by the notebook?
How do we publish the notebook as a service?

24. Share Genomic At Scale
Spark, Tachyon, Mesos, Shar3
➔ Projects: Distributed teams
➔ Research: Long process
➔ Towards Maximum Share for efficiency

25. Projects
Intrinsically involving many teams
geolocally distributed in different
countries or laboratories
with different skills in
Biology, Genetics, I.T., Medicine (, legal...)

26. Projects
Require many types of data ranging from
bio samples
imagery
textual
archives/historical

27. Projects
Of course
Generally gather many people from several populations
Note: This is very expensive and burns $time as hell!

28. Projects
1.000 genomes (2008-2012): 200To
100.000 genomes (2013-2017): 20Po (probably more)
1.000.000 genomes (2016-2020): 0.2Eo (probably more)
eQTL: mixing many sources

29. Projects
Need proper data management between entities, yet
coping with:
amount of data
heterogeneity of people
distance between actors
constraints related to data location

30. Projects
Distributed friendly
SCHEMAS + BINARY
f.i. Avro

31. Research
Research in medicine or health in general is
LOOOOOOO…OOOOONG

32. Research
Most reasons are quite obvious and must not be overlooked
Lots of measures and validation
Lots of control (including by Gov.)
Lots of actors

33. Research
As a matter of fact, research needs
to be conducted on data and
to produce results
And both are extremely exposed to reuse
So what if we lose either of them?

34. Research
However, we can get into troubles instantly
without even losing them!
What if we don’t track the processes?
In any scientific process: confrontation, replay and
enhancement are keys to move forward

35. This is misleading to think that sharing the code is enough.
Remind: we look for data and results, not for code.
The process includes the code, the context, the sources
and so on, and all should be part of the data
discovery/validation task
Research

36. Assess the risk factor associated with a disease given
mutations of a certain gene.
More than 50 years of data collecting and modelling.
Hundreds of researchers, each generation has new ideas.
Replaying old processes on new data,
new processes on old data
Research

37. Share share
share
All these facts relate to our capacity to share our work and
to collaborate.
We need to share efficiently and accurately the
★ data
★ processes
★ results

38. Share share
share
The challenge resides in the workflow

39. Share share
share
“Create” Cluster
Find sources (context, quality, semantic, …)
Connect to sources (structure, schema/types, …)
Create distributed data pipeline/Model
Tune accuracy
Tune performances
Write results to Sinks
Access Layer
User Access
ops
data
ops data
sci
sci ops
sci
ops data
web ops data
web ops data sci

40. Share share
share
“Create” Cluster
Find sources (context, quality, semantic, …)
Connect to sources (structure, schema/types, …)
Create distributed data pipeline/Model
Tune accuracy
Tune performances
Write results to Sinks
Access Layer
User Access
ops
data
ops data
sci
sci ops
sci
ops data
web ops data
web ops data sci

41. Share share
share
Streamlining development lifecycle
for better Productivity
with Shar3

42. Share share
share
Analysis
Production
DistributionRendering
Discovery
Catalog
Project
Generator
Micro Service /
Binary format
Schema for output
Metadata

43. That’s all folks
Thanks for listening/staying
Poke us on Twitter or via http://data-fellas.guru
@DataFellas @Shar3_Fellas @SparkNotebook
@Xtordoir & @Noootsab
Building Distributed Pipelines for Data Science using
Kafka, Spark, and Cassandra (form → @DataFellas)
Check also @TypeSafe: http://t.co/o1Bt6dQtgH