MPP

MPP analyzes microarray-derived gene and marker content data to estimate probabilities of presence or absence of specific genes or markers and to infer evolutionary trees or networks.

Key Features:

• Implementation: MPP is implemented in Java.
• Input formats: Processes flat file outputs from high-throughput microarray experiments.
• Presence/absence estimation: Determines probabilities of presence or absence of specific genes or markers in genomes.
• Dataset construction: Constructs gene or marker content datasets for multiple genomes.
• Phylogenetic inference: Estimates evolutionary trees or networks from gene or marker content datasets.
• Validation: Compares inferred results with known gene contents for validation.
• Experimental support: Supports comparative genome hybridization (CGH), retrotransposon-based insertion polymorphism (RBIP), and tagged microarray marker (TAM) experiments.
• Organism examples: Applied to fungi, bacteria, and to RBIP/TAM marker scores in pea.
• Algorithms: Uses both novel and established algorithms for analysis.
• Extensibility: Allows integration of new analytical procedures as plugins.

Scientific Applications:

• Microarray gene/marker analysis: Analysis of gene and marker content datasets derived from high-throughput microarray experiments.
• Comparative genome hybridization (CGH): Comparative analysis of gene content using CGH microarray data in fungi and bacteria.
• RBIP/TAM marker analysis in pea: Scoring and interpretation of retrotransposon-based insertion polymorphism (RBIP) markers from tagged microarray marker (TAM) experiments in pea.
• Phylogenetic reconstruction: Inferring evolutionary relationships and genetic evolution via trees or network estimation.
• Result validation: Validating inferred gene content or phylogenies by comparison to known gene contents.

Methodology:

Processes flat file outputs from microarray experiments, computes probabilities of gene or marker presence or absence, constructs gene/marker content datasets, estimates evolutionary trees or networks, compares results with known gene contents, and supports plugin-based integration using novel and established algorithms.