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BioPig: Hadoop-based Analytic Toolkit
for Next-Generation Sequence Data
Zhong Wang, Ph.D.
Computational Biology Staff Scie...
Cellulase
The deep metagenome approach to discover
cellulases for biofuel research
Large data, large reward
http://www.cazy.org/
Only 1% shared
(>=95% identity)
50% validated activity
Science. 2011 Jan 28;...
Sequence data
More data would be even better
Rumen(2009) Rumen(2010) Rumen(2012)
17 Gb
250 Gb
1000 Gb
But, can analysis keep up with data growth?
Ideal solutions for the terabase problem
1.Scalable to 1Tb?
2.Performance (within hours)?
High-Mem cluster
Input/Output (IO)Memory
MP/MPI solution: k-mer counting
1
2
3
4
Raw Data Data slices
Each node/core
has data and table slices
Count table
MP/MPI performance
MPI version
412 Gb, 4.5B reads
2.7 hours on 128x24 cores
NESRC HopperII
MP Threaded version
268 Gb, 3B ...
Hadoop/Map Reduce framework
• Google MapReduce
– Data Parallel programming model to process petabyte data
– Generally has ...
Programmability: Hadoop vs Pig
finding out top 5 websites young people visit
BioPig: design goals
• Flexible
– every dataset is unique, data analysts have domain knowledge that is essential
to optimi...
Runs on any hardware supporting Hadoop
• JGI Titanium (commodity hadoop cluster)
– Up to 20 16-cores 32GB RAM 1.799Ghz, 1G...
BioPig Modules
Blast
Input/Output
(Fasta,q)
K-mer
Counter
Assembly
How k-mer count is implemented
Load Mapper
Shuffle
/sort
Reducer Merge
<id1, header, ‘attagc’>
<id2, header, ‘gttagg’>
<id...
A 7-liner BioPig script for k-mer counting
Rumen metagenome gene discovery pipeline
Read
preprocess
(remove artifacts)
pigBlast
(blast reads
against known
cellulases...
Cloud solution to large data
BioPig-
Blaster
BioPig-
Assembler
BioPig-
Extender
BioPIG
BioPig: 61 lines of code
MPI-extend...
Conclusions
Hadoop-based BioPig shows great
potential for scalable analysis on very large
sequence data, it is robust and ...
Challenges in application
• IO optimization, e.g., reduce data copying
• Some problems do not easily fit into
map/reduce f...
Acknowledgement
• Karan Bhatia
• Henrik Nordberg
• Kai Wang
• Rob Egan
• Alex Sczyrba
• Jeremy Brand @JGI/NERSC
• Shane Ca...
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BioPig for scalable analysis of big sequencing data

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This talk was adapted from my presentation at the Finishing in the Future 2011, Santa Fe, NM.

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BioPig for scalable analysis of big sequencing data

  1. 1. BioPig: Hadoop-based Analytic Toolkit for Next-Generation Sequence Data Zhong Wang, Ph.D. Computational Biology Staff Scientist
  2. 2. Cellulase The deep metagenome approach to discover cellulases for biofuel research
  3. 3. Large data, large reward http://www.cazy.org/ Only 1% shared (>=95% identity) 50% validated activity Science. 2011 Jan 28;331(6016):463-7.
  4. 4. Sequence data More data would be even better
  5. 5. Rumen(2009) Rumen(2010) Rumen(2012) 17 Gb 250 Gb 1000 Gb But, can analysis keep up with data growth?
  6. 6. Ideal solutions for the terabase problem 1.Scalable to 1Tb? 2.Performance (within hours)?
  7. 7. High-Mem cluster Input/Output (IO)Memory
  8. 8. MP/MPI solution: k-mer counting 1 2 3 4 Raw Data Data slices Each node/core has data and table slices Count table
  9. 9. MP/MPI performance MPI version 412 Gb, 4.5B reads 2.7 hours on 128x24 cores NESRC HopperII MP Threaded version 268 Gb, 3B reads 5 days on 32 cores High-Mem Cluster • Experienced software engineers • Six months of development time • One nodes fails, all fail Problems: Fast, scalable
  10. 10. Hadoop/Map Reduce framework • Google MapReduce – Data Parallel programming model to process petabyte data – Generally has a map and a reduce step • Apache Hadoop – Distributed file system (HDFS) and job handling for scalability and robustness – Data locality to bring compute to data, avoiding network transfer bottleneck
  11. 11. Programmability: Hadoop vs Pig finding out top 5 websites young people visit
  12. 12. BioPig: design goals • Flexible – every dataset is unique, data analysts have domain knowledge that is essential to optimize the analysis, – pluggable modules that analysts can use to build custom analytic pipelines, • High-Level – domain-specific language enable data analysts to create custom pipelines, – hide details of parallelism (too complex for most people), • Scalability – leverage data parallelism to speed up analytics, – integrate external tools and applications where necessary, – scale from 1 to hundreds of compute nodes with minimal effort and linear scalability. • Robustness – Data and computation are replicated across nodes to combat failures BioPIG
  13. 13. Runs on any hardware supporting Hadoop • JGI Titanium (commodity hadoop cluster) – Up to 20 16-cores 32GB RAM 1.799Ghz, 1G Ethernet • NERSC Magellan Cloud Testbed – Up to 200 8-core 24GB RAM, and 2.67GHz Nehalem processors, 10Gbit InfiniBand, GPFS • Amazon AWS – Elastic MapReduce with cluster compute nodes (23 GB of memory, 2 x Intel quad-core “Nehalem” architecture 1690 GB of instance storage, 10G Ethernet
  14. 14. BioPig Modules Blast Input/Output (Fasta,q) K-mer Counter Assembly
  15. 15. How k-mer count is implemented Load Mapper Shuffle /sort Reducer Merge <id1, header, ‘attagc’> <id2, header, ‘gttagg’> <id1, ‘atta’>, <id1,’ttag’> <id2, ‘gtta’>, <id2, ‘ttag’> <‘atta’, id1>, <‘ttag’, id1, id2> <‘gtta’, id2>, <‘tagg’, id2> <‘atta’, 1>, <‘ttag’, 2> <‘gtta’, 1>, <‘tagg’, 1> <‘atta’, 3>, <‘ttag’, 2> <‘gtta’, 2>, <‘tagg’, 1>
  16. 16. A 7-liner BioPig script for k-mer counting
  17. 17. Rumen metagenome gene discovery pipeline Read preprocess (remove artifacts) pigBlast (blast reads against known cellulases) pigAssembler (Assemble reads into contigs) pigExtender (Extend contigs into full-length enzymes)
  18. 18. Cloud solution to large data BioPig- Blaster BioPig- Assembler BioPig- Extender BioPIG BioPig: 61 lines of code MPI-extender: ~12,000 lines (vs 31 in BioPig) Flexibility Programmability Scalability x x
  19. 19. Conclusions Hadoop-based BioPig shows great potential for scalable analysis on very large sequence data, it is robust and easy to use.
  20. 20. Challenges in application • IO optimization, e.g., reduce data copying • Some problems do not easily fit into map/reduce framework, e.g., graph-based algorithms • Integration into exiting framework, Galaxy
  21. 21. Acknowledgement • Karan Bhatia • Henrik Nordberg • Kai Wang • Rob Egan • Alex Sczyrba • Jeremy Brand @JGI/NERSC • Shane Cannon @NERSC BioPIG

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