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