Table 7. Computing Roadmap: Facility for Whole Proteome Analysis

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Table 7. Computing Roadmap: Facility for Whole Proteome Analysis (acronyms)

Topic	Research, Design, and Development	Demonstration: Pilots and Modular Deployment	Integration and Production Deployment
LIMS and Workflow Management Participate in GTL cross-facility LIMS working group Develop technologies and methods to: Manage massive dataflow Process and integrate data Manage workflow Conduct QA/QC Deploy collaborative tools for shared access to data and processes	Archival storage systems Prototype bulk data capture and retrieval systems Prototype inter- and intralab limited LIMS Shared LIMS and workflow technology for each analytical capability	LIMS for each analytical pipeline Data archives for each analytical pipeline Inter- and intralab LIMS	Establish LIMS for each analytical pipeline workflow Products: Output data products Cross-facility access and tracking Information management systems and automation Efficient, analytically rigorous pipelines Components and integration to GTL process
Bioinformatics Participate in GTL cross-facility working group for data representation and standards Provide user environments, community access, database development Integrate data-analysis methods Develop large-scale integrated experiment designs, analysis pipelines	Workflow processes and database needs Evaluation of technical solutions Large-scale storage and retrieval solutions Entire workflow processes and methods for experimentation and analysis Algorithms Quality control and assessment measures	Statistically designed experiments Multidimensional data-analysis and integration tools for large-scale experimentation Multilevel databases for bulk and derived data for each profiling method Analysis pipeline for derived data Community-access systems Cross-facility data-sharing processes and analysis methods Archival, computing, and network capacity to match demand	Bulk data archives for key data sets Process to link archives to production activities Local facility data archive Cross-facility data-sharing processes and analysis methods Mature bulk data archives, analysis piles Scaleup of archival activities, computing, and network capacity to match demand Products: Whole proteome analysis for each GTL organism Experiment templates and data sets for modeling and simulation Defined experiment archive integrated with data and analysis from each analytical pipeline Molecular profiling context-dependent database
Computing Infrastructure Participate in GTL cross-cutting working group for computing infrastructure Establish scientific computing with massive data reduction, archival storage application development Develop infrastructure: hardware, software, code control, libraries, environments Use ultrahigh-speed internet connection to GTL facilities	Operations process Computational architecture Large-scale data mining Access and security plans and processes Performance and quality metrics of service Capacity planning Backup and recovery strategy Testing plans Workflow Dev-Test-Pro strategy for implementations	Test network Development environment Validation methods Data archive Access methods Storage and retrieval methods Application integration and implementation Production infrastructure Cross-facility data sharing Infrastructure: hardware, software, and network	Production environment and data archive Bulk data archives for key data sets Process to link production activities to local facility data archive Cross-facility data-sharing processes and analysis methods Mature bulk data archives and analysis pipelines

Topic

Research, Design,
and Development

Demonstration: Pilots and Modular Deployment

Integration and Production Deployment

LIMS and Workflow Management

Participate in GTL cross-facility LIMS working group

Develop technologies and methods to:

Manage massive dataflow
Process and integrate data
Manage workflow
Conduct QA/QC
Deploy collaborative tools for shared access to data and processes

Archival storage systems

Prototype bulk data capture and retrieval systems

Prototype inter- and intralab limited LIMS

Shared LIMS and workflow technology for each analytical capability

LIMS for each analytical pipeline

Data archives for each analytical pipeline

Inter- and intralab LIMS

Establish LIMS for each analytical pipeline workflow

Products:

Output data products

Cross-facility access and tracking

Information management systems and automation

Efficient, analytically rigorous pipelines

Components and integration to GTL process

Bioinformatics

Participate in GTL cross-facility working group for data representation and standards

Provide user environments, community access, database development

Integrate data-analysis methods

Develop large-scale integrated experiment designs, analysis pipelines

Workflow processes and database needs

Evaluation of technical solutions

Large-scale storage and retrieval solutions

Entire workflow processes and methods for experimentation and analysis

Algorithms

Quality control and assessment measures

Statistically designed experiments

Multidimensional data-analysis and integration tools for large-scale experimentation

Multilevel databases for bulk and derived data for each profiling method

Analysis pipeline for derived data

Community-access systems

Cross-facility data-sharing processes and analysis methods

Archival, computing, and network capacity to match demand

Bulk data archives for key data sets

Process to link archives to production activities

Local facility data archive

Cross-facility data-sharing processes and analysis methods

Mature bulk data archives, analysis piles

Scaleup of archival activities, computing, and network capacity to match demand

Products:

Whole proteome analysis for each GTL organism
Experiment templates and data sets for modeling and simulation
Defined experiment archive integrated with data and analysis from each analytical pipeline
Molecular profiling context-dependent database

Computing Infrastructure

Participate in GTL cross-cutting working group for computing infrastructure

Establish scientific computing with massive data reduction, archival storage application development

Develop infrastructure: hardware, software, code control, libraries, environments

Use ultrahigh-speed internet connection to GTL facilities

Operations process

Computational architecture

Large-scale data mining

Access and security plans and processes

Performance and quality metrics of service

Capacity planning

Backup and recovery strategy

Testing plans

Workflow

Dev-Test-Pro strategy for implementations

Test network

Development environment

Validation methods

Data archive

Access methods

Storage and retrieval methods

Application integration and implementation

Production infrastructure

Cross-facility data sharing

Infrastructure: hardware, software, and network

Production environment and data archive

Bulk data archives for key data sets

Process to link production activities to local facility data archive

Cross-facility data-sharing processes and analysis methods

Mature bulk data archives and analysis pipelines

U.S. Department of Energy Office of Science

Genomics:GTL

Table 7. Computing Roadmap: Facility for Whole Proteome Analysis (acronyms)

	Human Genome Project Information Genomic Science Program DOE Microbial Genomics home