ASTRO

ASTRO analyzes short time-series gene expression data to identify temporal expression patterns and infer dynamics of cellular processes from datasets with limited temporal points.

Key Features:

• Algorithmic Innovation: Implements two algorithms, ASTRO and MiMeSR, inspired by rank order preserving frameworks and minimum mean squared residue approaches to exploit limited numbers of time points.
• NP-hard to Linear Transformation: Reformulates an NP-hard pattern-detection problem into a linear formulation appropriate for short time-series datasets.
• Robustness to Noise: Specifically engineered to be robust against noise and random patterns to reliably detect temporal expression profiles in functional categories.
• Performance Superiority: Demonstrates superior performance compared to traditional clustering algorithms and algorithms developed for short time-series gene expression analysis.

Scientific Applications:

• Integration with GO and ChIP-chip data: Uses Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data to provide insights into the dynamics of cellular processes through pattern recognition in temporal gene expression.

Methodology:

Employs the ASTRO and MiMeSR algorithms, based on rank order preserving frameworks and minimum mean squared residue approaches respectively, reformulating an NP-hard problem into a linear problem and incorporating Gene Ontology (GO) annotations and ChIP-chip data for pattern analysis.