Developing an open source community for cloud bioinformatics
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Developing an open source community for cloud bioinformatics



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Developing an open source community for cloud bioinformatics Presentation Transcript

  • 1. Developing an open source community for cloud bioinformatics Brad Chapman 8 June 2010
  • 2. Overview 1 Building open source bioinformatics communities is hard. 2 Developer resources are a productive target. 3 Framework: collaborative software images and data snapshots.
  • 3. Motivation Open source OpenBio, Biopython Graduate school – developed distributed algorithm. Never reused. Work Startup: Automated biological pipelines. Research hospital: Democratization of analysis.
  • 4. Filters in biological computing Working in same biological area Interest in developing open source code Technical abilities Your software is good enough
  • 5. Successful bioinformatics Sean Eddy, HMMER ...the best software in the field is often an unplanned labor of love from a single investigator.
  • 6. Recognizing contributions
  • 7. Successful community projects OpenBio: BioPerl, Biopython, BioJava Bioconductor Common theme Aimed at developers. Biologists benefit indirectly.
  • 8. Lowering activation energy
  • 9. Establishing common platform The solution = to all our problems Remove install and distribution barriers Building block for scaling
  • 10. Existing cloud bioinformatics work JCVI Cloud BioLinux bioperl-max MachetEC2 Debian Med Overlapping set of useful functionality.
  • 11. Integrated community solution Inclusive but configurable Easy to contribute Automated Bootstrap bare machine to fully ready distributed AMI. biolinux/
  • 12. Inclusive but configurable # Top level YAML configuration file specifying # groups of programs to be installed. packages: - python - r - erlang - databases - viz - bio_search - bio_alignment - bio_nextgen - bio_sequencing - bio_visualization - phylogeny libraries: - r-libs - python-libs
  • 13. Easy to contribute # Configuration file defining R specific libraries that # are installed via CRAN and Bioconductor. cranrepo: cran: - ggplot2 - rjson - sqldf - NMF - ape biocrepo: bioc: - ShortRead - BSgenome - edgeR - GOstats - biomaRt - Rsamtools
  • 14. Automated def install_biolinux(): ec2_ubuntu_environment() pkg_install, lib_install = _read_main_config() _apt_packages(pkg_install) _do_library_installs(lib_install) def _ruby_library_installer(config): for gem in config[’gems’]: sudo("gem install %s" % gem) Fabric:
  • 15. Ready to use biological data % ls /referenceGenomes/ % ls Hsapiens/hg18 Athaliana arachne Celegans bowtie Dmelanogaster bwa Ecoli eland Hsapiens maq Mmusculus seq Msmegmatis snps Mtuberculosis_H37Rv ucsc Paeruginosa_UCBPP-PA14 phiX174 Rnorvegicus Scerevisiae Xtropicalis
  • 16. Organization: Codefest 2010