Jeff Grethe: CAMERA

Jeffrey S. Grethe, Ph.D. Center for Research in Biological Systems University 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

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)

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)

Scientific Workflow Systems …
… and a cross-project collaboration
… initiated August 2003
1 st release: May 13 th , 2008
More than 20 thousand downloads!
www.kepler-project.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
Ptolemy II: A laboratory for investigating design KEPLER: A problem-solving environment for Scientific Workflow KEPLER = “Ptolemy II + X” for Scientific Workflows

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 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

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

Jeff Grethe: CAMERA

by Iddo on Jul 23, 2009

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