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Jeffrey S. Grethe, Ph.D.
Center for Research in Biological SystemsUniversity of California, San Diego
Standards in the Context of a Large-Scale Microbial Ecology Cyberinfrastructure
(CAMERA)
Global Scientific Research Cyber-Community
CAMERA 2.0
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)
Creating CAMERA 2.0 -Advanced Cyberinfrastructure Service Oriented
Architecture
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)
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.
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
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
Integration of Enhanced Metadata
Integrate and browse additional sources of microbial data
Community Data Requirements
• A simple submission process (web based entry or template upload)
• Support from CAMERA staff during process (collaborative environment)
• Large variety (metadata) and quantity of data should not mean a long submission (choice of interfaces)
• Compliance with community stadards
• Support pre-registration of samples for sequencing
CAMERA 2.0 (Data Submission)
Growing the CAMERA Community and Resource…
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)
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)
User Friendly Compute Environment
CAMERA 2.0 (Computation)
From simple job submission to community developed and published workflows…
The Big Picture: Supporting the Scientist
Conceptual Workflow
Executable Workflow
From “Napkin Drawings” …
… to Executable Workflows
Source: Mladen Vouk (NCSU)
Ptolemy II: A laboratory for investigating designKEPLER: A problem-solving environment for Scientific Workflow
KEPLER = “Ptolemy II + X” for Scientific Workflows
Scientific Workflow Systems …
• … and a cross-project collaboration
… initiated August 2003• 1st release: May 13th, 2008
• More than 20 thousand downloads!
www.kepler-www.kepler-project.orgproject.org
• Builds upon the open-source Ptolemy II framework
• Different Scientific Workflows• Visual component integration
• Taverna, Triana• Grid-base distributed execution
• Pegasus, Askalon• Visualization
• Vistrails, SciRUN• Transaction-oriented
• BPEL, mostly industrial
• Execution Platforms• Portals, e.g., GEON, CAMERA• Web 2.0, e.g., myExperiment
Personalized (Collaborative) Workflow and Data Spaces
Default and Advanced UI
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
MetagenomicRaw reads
CD-HIT-EST, 95%
DNAclusters
Proteinclusters
Representativesequences
Unique DNAsequences
ORF Annotation
1. ORF_finder2. Metagene
CD-HIT, 60 or 30%
COG
Pfam
Tigrfam
HMMER HMMERHEADRPS-BLAST
ClusterAnnotation
1. tRNA scan2. rRNA scan3. meta_RNA
ORFs
Non-redundantORFs
tRNAs
rRNAs
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.
Run branches within workflow
A ORF
clustering branch
A functional annotation
branch
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
Provenance Schema and Viewer in CAMERA 2.0
http://camera.calit2.net