1. DESIGNING THE MICROBIAL RESEARCH COMMONS: AN INTERNATIONAL SYMPOSIUM NATIONAL ACADEMY OF SCIENCES, WASHINGTON, DC, 8-9 OCTOBER 2009 Paul Gilna, B.Sc., Ph.D. California Institute for Telecommunications & Information Technology (Calit2) University of California, San Diego Large-Scale Microbial Ecology Cyberinfrastructure (CAMERA)

2. Global Scientific Research Cyber-Community

3. 3100 users 70 countries

4. CAMERA 2.0 Objectives CAMERA serves as one representation of a specific research community’s need for a system to - Provide a metadata rich family of scalable databases and make them available to the community - Collect and reference increasing metadata relevant to environmental metagenome datasets - Exploit the power of querying on metadata across multiple geospatial locations - Provide a facility that allows for a diversity of software tools to be easily integrated into the system (and sufficient compute resources to support these analyses)

5. The Semantically Aware DB Schema Some key features of the semantically aware DB schema - Environmental parameters: Modeled more generally, to accommodate any environment and any parameter within an environment - Sequence: Separate “registries” for DNA, rRNA, mRNA, viral segments, reference genomes etc. Sequence annotations are independently searchable. - Workflow Connection: Every computed property is associated with the workflow instance that created it. - Associated Data : Data not produced in CAMERA but often used for analysis and comparison - Ontologies: All metadata, measured and observed parameters are connected to ontologies, whenever possible.

6. Integration of External Data Warehousing - Reference genomes - Homologs, CoG clusters - Raster data from slow/complex servers Remote Data - KEGG pathways - NASA MODIS data - World Ocean Atlas - Other data that come as “data sets” that do not conform to the schema

7. NASA Aqua-MODIS satellite data Metadata: beyond data collected at sampling site Sea Surface Temp Chlorophyll MODIS Images covering GOS sites #8 – 12, mid November, 2003

8. Integration of Enhanced Metadata

9. Integrate and browse additional sources of microbial data

10. CAMERA 2.0 (Data Submission) Growing the CAMERA Community and Resource…

11. Investigator submits proposal to GBMF Investigator submits metadata to CAMERA CAMERA sends acknowledgement to Investigator, Seq. Group, GBMF Seq. Group send barcoded sample “kit” to investigators Seq. Group Upload data to CAMERA (& Investigator) Data & Metadata Released in six months Metadata now collected before sequence data: GSC-compliant Project-ID serves as acceptance-proof Sample is Received and Sequenced Webb Miller and Stephan C. Schuster, and Roche / 454 Genome Sequencer GBMF Data Acquisition Pipeline: A New Data Submission Paradigm-Metadata First!

12. Data Standards Minimal Information for (Meta)Genomic Sequences: MIGS/MIMS A Metadata standard, developed by the Genomics Standards Consortium - Controlled vocabularies e.g. EnvO, PATO - Common language: GCDML Submissions shall comply with a MIMS/MIGS core, but any metadata can be entered via keywords and free text Different metadata submission forms for different habitats: (water, soil, air, hosts)

13. User Friendly Compute Environment

14. CAMERA 2.0 (Computation) From simple job submission to community developed and published workflows…

15. RAMMCAP – Rapid clustering and functional annotation for metagenomic sequences RNA finding/filtering DNA Clustering Unique sequence Taxonomy / population analysis ORF clustering ORF calling Unique sequences Protein families ORF and cluster annotation Pfam, Tigrfam, COG, etc. Features Very fast (10-100x) as compared to BLAST-based methods Effective tools: CD-HIT, HMMERHEAD, meta_RNA, and RPS-BLAST Focused functional annotation via curated protein families CD-HIT, 90-95% More in-depth analysis and further annotation Metagenomic Raw reads CD-HIT-EST, 95% DNA clusters Protein clusters Representative sequences Unique DNA sequences ORF Annotation 1. ORF_finder 2. Metagene CD-HIT, 60 or 30% COG Pfam Tigrfam HMMER HMMERHEAD RPS-BLAST Cluster Annotation 1. tRNA scan 2. rRNA scan 3. meta_RNA ORFs Non-redundant ORFs tRNAs rRNAs

16. Annotation workflow A green box is called an ‘actor’, which performs a task. This special actor represents an annotation component, such as BLAST search. Workflow parameters, which can be specified by users in the portal, are passed to workflow components. Data flow is divided.

17. Provenance of Workflow Related Data Provenance: A concept from art history and library - Inputs, outputs, intermediate results, workflow design, workflow run Collected information - Can be used in a number of ways - Validation, reproducibility, fault tolerance, etc… - Linked to the semantic database - Viewable and searchable from CAMERA 2.0

18. http://camera.calit2.net