TimesVector-web

TimesVector-web analyzes time-course gene expression across multiple experimental conditions to identify genes with temporal modulation and enable downstream functional interpretation.

Key Features:

• Multi-Condition Time-Course Analysis: Tailored for multi-class time course data to compare multiple conditions simultaneously and detect differences in gene expression patterns over time.
• Temporal Pattern Identification: Identifies genes that exhibit specific modulation across time under various experimental conditions.
• Time-Series-Aware Differential Analysis: Addresses limitations of pairwise DEG methods by analyzing series of time points rather than individual pairwise comparisons.
• Cross-Platform Integration: Integrates and analyzes datasets from microarray and RNA-seq platforms.
• Downstream Functional Analyses: Provides analyses for transcription factors (TF), microRNA (miRNA) targets, gene ontology, and pathway enrichment.

Scientific Applications:

• Dynamic Transcriptome Studies: Analysis of temporal gene expression changes in contexts such as developmental biology.
• Disease Progression Studies: Characterization of transcriptomic dynamics during disease onset and progression.
• Treatment Response Analysis: Identification of time-dependent transcriptional responses to experimental treatments.
• Method Validation: Demonstrated utility through three case studies using both microarray and RNA-seq data that captured biologically relevant insights.

Methodology:

Identification of temporal gene expression patterns across multiple conditions, integration and analysis of microarray and RNA-seq datasets, and application of advanced computational techniques to account for time-series data rather than pairwise DEG comparisons.