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Enhancing Clinical Genome Analysis with PostgreSQL: A Case Study from The Children's Hospital**

This presentation by Jeff Pennington, Director of the Translational Informatics Center at The Children's Hospital of Philadelphia, explores the integration of PostgreSQL in genome analysis within clinical settings. It covers the database architecture of VARIFY, a three-tier web application that utilizes Django and JavaScript for enhanced data handling. The talk highlights genome sequencing, the significance of mutations in clinical contexts, and specific tuning strategies for PostgreSQL 9.2 databases, focusing on memory allocation, max connections, and performance optimization for handling vast genomic datasets.

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Enhancing Clinical Genome Analysis with PostgreSQL: A Case Study from The Children's Hospital**

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Presentation Transcript

  1. Postgres and the Genome Jeff Pennington Director, Translational Informatics Center for Biomedical Informatics And Department of Pathology The Children’s Hospital Of Philadelphia

  2. Outline • Background • Genome analysis in the clinic • Application • Database • DB Tuning

  3. DNA as Data • 4 letter ‘alphabet’ of bases – A T C G • 3,000,000,000 base pairs • Sequence codes for biological function

  4. Mutations

  5. Clinical Mutation = ‘Variant’

  6. Sequencing = 100K – 4M Variants

  7. VARIFY

  8. VARIFY Architecture • Varify Architecture • Three-tier web application • Harvest (http://harvest.research.chop.edu) • Javascript client • Python server using Django ORM • Postgres 9.2

  9. Database • Physical – 9.2, RHEL VM, VMWarew/ storage on host • Round 1 – 4G RAM, 80G disk • Round 2 – 32 G RAM, 250G disk

  10. Tuning • max_connections – too big, • shared_buffers – amount of memory allocated to PG • work_mem – amount of memory available to sort • default_statistics_target – gives the query planner something to work with

  11. Resources • Book: PostgreSQL9.0 High Performance • Ch 5 and 6 • Page 145 • Tools: pg_buffercache • Benchmarking: • \timing • EXPLAIN • log_min_duration_statement = 5000

  12. Tuning Round 1 (4G RAM) • max_connections = 100 • shared_buffers = 1024MB (default 32MB) • work_mem = 200MB (default 1M) • Tried 1G, bad trade-off on count (slow) vs. list (not much faster)

  13. Tuning Round 2 (32G RAM) • max_connections = 100 • shared_buffers = 24576MB (Increased from 1024MB) • work_mem = 150MB (Decreased from 200MB)

  14. Tuning Round 3 • Everything in Round 2 • default_statistics_target = 1000 (default 100)

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