Genome Analysis Pipelines, Big Data Style
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Genome Analysis Pipelines, Big Data Style Allen Day, Chief Scientist, MapR Powerful new tools exist for processing large volumes of data quickly across a cluster of networked computers. Typical bioinformatics workflow requirements are well-matched to these tools' capabilities. However, the tool Spark, for example, is not commonly used because many legacy bioinformatics applications make assumptions about their computing environment. These assumptions present a barrier to integrating the tools into more modern computing environments. Fortunately, these barriers are quickly coming down. In this presentation, we'll examine a few operations common to many bioinformatics pipelines, show how they were usually implemented in the past, and how they're being re-implemented right now to save time, money, and make new types of analysis possible. Some code examples will also be provided. Video Presentation: https://youtu.be/iwgfjHiHr7Q
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Genome Analysis Pipelines, Big Data Style
- 1. ® © 2015 MapR Technologies 1 ® © 2015 MapR Technologies Allen Day, PhD // Chief Scientist @ MapR.com 2016.04.12, Big Data Everywhere
- 2. ® © 2015 MapR Technologies 2 Agenda • Presentation Motivations – Data inertia, data local computing • Highlights of BigData solutions ecosystem – MapR, NoSQL, Spark • Biotech Analytics Use Cases – Transition from sensors to insights - population DBs • NoSQL performance – Cost savings • NoSQL cost structure – Legacy tools – integration • Spark wrappers
- 3. ® © 2015 MapR Technologies 3 Data Inertia • Newton’s 1st Law of Motion (Law of Inertia) • “An object at rest stays at rest … unless acted upon by an unbalanced force” • Force required to transport data increases with data size and device latency – CPU < CPU caches < RAM < Disk/SSD < Network bigger faster
- 4. ® © 2015 MapR Technologies 4 Data Inertia + Exponential Data Growth => Data Local “BigData” Computing • Traditional algorithm design moves data to the executing program – High Perf Cluster + Storage Network (HPC+SAN) • Key insight – program proportionally much smaller than data, thus easier to move. • Modern algorithm design moves executing program to the data
- 5. ® © 2015 MapR Technologies 5 Some BigData Tools What is Spark? • Spark is a parallel computing framework that allows a job to run on 1000s of computers as easily as 1. No code changes required. • Makes good use of RAM and SSD storage What is HBase? • HBase is a non-relational (NoSQL), distributed database modeled on Google’s BigTable. • Provides highly scalable sustained and random access to very large data sets
- 6. ® © 2015 MapR Technologies 6 MapR Converged Platform for BigData
- 7. ® © 2015 MapR Technologies 7© 2015 MapR Technologies ® Cost-Effective ETL (Novartis)
- 8. ® © 2015 MapR Technologies 8 The Problem • Key step in data ingest for R&D handled by enterprise data warehouse (EDW) – Video, Proteomics, NGS, Metagenomics • EDW at maximum capacity – Multiple rounds of software optimization already done – Data still growing • Insight limiting (= career limiting) bottleneck
- 9. ® © 2015 MapR Technologies 9 Three Options 1. No more insights / candidates 2. Increase EDW size – Expensive – Known to not scale well 3. Find a more scalable solution
- 10. ® © 2015 MapR Technologies 10 Extract, Load Raw data: • Public/private • Compounds • Expression data • Genotype data • EHR data • … Transform, Load Downstream Analysis (R&D) Original Flow – ELTL Knowledge graph Data Warehouse
- 11. ® © 2015 MapR Technologies 11 Simplified Analysis – EDW Strategy • Majority of EDW storage consumed by ELTL processing – Caused by minority of code (raw data transformations) • Increasing EDW capacity yields sub-linear performance – poor division of labor
- 12. ® © 2015 MapR Technologies 12 With ETL Offload Raw data: • Public/private • Compounds • Expression data • Genotype data • EHR data • … Extract, Load Transform, Load Knowledge graph Data Warehouse Downstream Analysis (R&D) MapR
- 13. ® © 2015 MapR Technologies 13 Simplified Analysis – MapR Strategy • Lower Cost per TB of increased ETL capacity by replacing EDW with MapR • Scale-out architecture – linear spend gives linear performance increase • Strategic advantage – next-gen architecture for implementing new use cases – Insights/time (and career) acceleration
- 14. ® © 2015 MapR Technologies 14 Additionally… Raw data: • Public/private • Compounds • Expression data • Genotype data • EHR data • … Extract, Load Knowledge graph Data Warehouse Downstream Analysis (R&D) MapRTransform, Load
- 15. ® © 2015 MapR Technologies 15 New Use Cases are Enabled Raw data: • Public and private • Compounds • Expression data • Genotype data • EHR data • … Extract, Load Knowledge graph Data Warehouse Downstream Analysis (R&D) New Use Cases MapR Transform, Load
- 16. ® © 2015 MapR Technologies 16© 2015 MapR Technologies ® NoSQL: Scalable Population DBs
- 17. ® © 2015 MapR Technologies 17 Catalog genetic variants => find QTLs • Current public human cohort proposals 100K-1M individuals, >400% CAGR • Seed and livestock companies, same trend • Px/Dx biomarkers for PGx, reproductive medicine, biometrics, etc. • Idea is to catalog genetic variants, find QTLs • Well studied problem, let’s take a look
- 18. ® © 2015 MapR Technologies 18 Genome × Phenome Analysis 𝛿5 ϕ5 ϕ3 ϕ1 𝛿3 𝛿1 SPARSE Billion + Phenotypes SPARSEBillion+Genotypes For given population, given SNP 𝛿, and given phenotype ϕ: Count the number of occurrences as the value of the matrix
- 19. ® © 2015 MapR Technologies 19 Associate QTLs to variants via Genome × Phenome Matrix Factorization 𝛿5 ϕ5 ϕ3 ϕ1 𝛿3 𝛿1 Archetypal Genotypes (column Eigenvector) Archetypal Phenotypes (row Eigenvector) Factorize w/ Spark & MapR • Row Eigenvectors of X represent – Sets of related phenotypes (by SNP) • Column Eigenvectors of Y represent – Sets of related SNPS (by phenotype)
- 20. ® © 2015 MapR Technologies 20 𝛿5 ϕ5 ϕ3 ϕ1 𝛿3 𝛿1 Archetypal Genotypes (column Eigenvector) Archetypal Phenotypes (row Eigenvector) Moreover… This is a generalized GWAS
- 21. ® © 2015 MapR Technologies 21 𝛿5 ϕ5 ϕ3 ϕ1 𝛿3 𝛿1 Archetypal Genotypes (column Eigenvector) Archetypal Phenotypes (row Eigenvector) Moreover… This is a generalized GWAS it’s PheWAS
- 22. ® © 2015 MapR Technologies 22 𝛿5 ϕ5 ϕ3 ϕ1 𝛿3 𝛿1 Archetypal Genotypes (column Eigenvector) Archetypal Phenotypes (row Eigenvector) Moreover… This is a generalized GWAS it’s PheWAS NB: These calculations are mixed I/O workload – require high-throughput sustained read and low-latency random- access Proven MapR-DB use case: Aadhar Biometric system, 1B humans biometrics
- 23. ® © 2015 MapR Technologies 23 𝛿5 ϕ5 ϕ3 ϕ1 𝛿3 𝛿1 Furthermore…
- 24. ® © 2015 MapR Technologies 24 doc5 user5 user3 user1 doc3 doc1 If we change the labels…
- 25. ® © 2015 MapR Technologies 25 doc5 user5 user3 user1 doc3 doc1 INTERESTS BEHAVIORS We have the core of Google / Facebook / Twitter Ad Revenue Engine
- 26. ® © 2015 MapR Technologies 26 doc5 user5 user3 user1 doc3 doc1 INTERESTS BEHAVIORS We have the core of Google / Facebook / Twitter Ad Revenue Engine
- 27. ® © 2015 MapR Technologies 27© 2015 MapR Technologies ® Spark: Porting Legacy Pipelines
- 28. ® © 2015 MapR Technologies 28 Alignment Reference Sequences Aligned Reads Downstream Applications… DNA Reads
- 29. ® © 2015 MapR Technologies 29 Alignment Reference Sequences DNA Reads Aligned Reads Downstream Applications… Align()
- 30. ® © 2015 MapR Technologies 30 Possible Align() Outcomes Unaligned DNA Reads Reference Sequences Single Location Reads Multiple Location Reads Unlocatable Reads Align()
- 31. ® © 2015 MapR Technologies 31 Many-to-Many Relationship Between Reads and Locations • Read1 • Read2 • Read3 • Read4 • NULL • LocationA • LocationB • LocationC • LocationD • LocationA • NULL • LocationE
- 32. ® © 2015 MapR Technologies 32 Parallelizing Alignment Unaligne d DNA Reads Locations Locations Locations Part1Part2Part3 Aligned DNA Reads Align() Concat() Sort() Etc…Split()
- 33. ® © 2015 MapR Technologies 33 Using HPC+SAN has Bottlenecks (GridEngine, Etc) Part1Part2Part3 Volume Read Bottleneck Volume Write Bottleneck Read & Write Bottleneck
- 34. ® © 2015 MapR Technologies 34 Using Spark Eliminates Bottlenecks Align() Concat() Sort()Split()
- 35. ® © 2015 MapR Technologies 35 Bottom Level: Integration with Legacy Tools Local I/O Container Legacy Sub-process
- 36. ® © 2015 MapR Technologies 36 Bottom Level: Integration with Legacy Tools
- 37. ® © 2015 MapR Technologies 37 Bottom Level: Integration with Legacy Tools • No time today to look at code, but a deeper slideshow of doing this with Bowtie aligner: • http://www.slideshare.net/allenday • https://github.com/allenday/spark-genome- alignment-demo Local I/O Container Legacy Sub-process
- 38. ® © 2015 MapR Technologies 38 Thanks! Questions? @allenday, @mapr aday@mapr.com linkedin.com/in/allenday slideshare.net/allenday