Argonne National Laboratory

Argonne National Laboratory integrates WIT2, EMP, MPW, SENTRA, and PatScan to enable comparative genomic analysis, metabolic reconstruction, and microbial signal transduction annotation.

Key Features:

• Integrated tools: Combines WIT2, EMP, MPW, SENTRA, and PatScan for joint comparative genomics, metabolic, and signal transduction analyses.
• Comparative Genomic Analysis: Uses the WIT (What Is There) system with sequence homologies, ORF-clustering algorithms, and relative gene positioning across approximately 40 completed or nearly completed genomes for functional annotation and pathway integration.
• Metabolic Reconstructions: Utilizes the Metabolic Pathway Database (MPW), a derivative of EMP, and over 2800 pathway diagrams covering primary and secondary metabolism, membrane transport, signal transduction, intracellular traffic, translation, and transcription to support metabolic reconstruction and simulation.
• Encoding and Simulation: Employs an encoding system based on electronic circuit design logic that enables automated derivation of stoichiometric matrices, rate laws, and dynamic models.
• Signal Transduction Analysis: Provides SENTRA data including annotations of conserved domains, paralogous and orthologous sequences, and gene clusters from 43 completely sequenced prokaryotic genomes and sequences drawn from SWISS-PROT and TrEMBL.
• Enzyme and Pathway Knowledgebase: Leverages EMP content encoded from over 10,000 publications and a queryable set of over 1800 pictorial pathway representations to interpret enzymology and metabolic information.

Scientific Applications:

• Genomic Function Annotation: Assigns gene functions and integrates genomic context to infer organismal physiology using comparative genomics and homology-based annotation.
• Metabolic Pathway Modeling: Supports construction and automated simulation of metabolic pathway models, including stoichiometric and dynamic representations.
• Signal Transduction Research: Enables analysis of microbial signal transduction proteins, conserved domains, and gene cluster relationships across prokaryotic genomes.

Methodology:

Sequence homology analysis, ORF-clustering algorithms, relative gene positioning, conserved-domain and paralog/ortholog annotation using SWISS-PROT and TrEMBL sequences, pathway diagram encoding based on electronic circuit design logic, and automated derivation of stoichiometric matrices, rate laws, and dynamic models.