FunPat

FunPat performs integrated analysis of time series RNA sequencing data to identify differentially expressed genes and associate them with functional terms and representative temporal expression profiles.

Key Features:

• Integrated Framework: Integrates gene selection, clustering, and functional annotation into a single analysis framework.
• Functional Annotations Utilization: Performs integrated selection–clustering analyses for each functional term, such as Gene Ontology terms, to identify genes that share annotations and common dynamic expression profiles.
• Performance Assessment: Evaluation on simulated and real datasets indicated improved recall without compromising the false discovery rate, achieving high precision and recall for temporal expression patterns.
• Robustness with Limited Data: Produces reproducible lists of significant genes even in the absence of biological replicates.
• Enhanced Sensitivity and Readability: Combines statistical evidence of differential expression with temporal profiles and functional annotations to increase sensitivity, associate genes with informative terms and representative temporal patterns, and reduce redundancy.
• Real-data Application: Demonstrated selection of differentially expressed genes with high reproducibility from real time series expression data.
• Implementation: Implemented as an R package for analysis of high-throughput RNA sequencing time series data.

Scientific Applications:

• Cellular responses to environmental changes: Enables analysis of temporal gene expression changes in cellular responses to environmental stimuli.
• Developmental biology: Supports study of gene expression dynamics during development.
• Disease progression: Facilitates investigation of temporal transcriptional changes during disease progression.
• Drug response mechanisms: Applied to characterize transcriptional responses and mechanisms following drug treatment.
• Functional pattern discovery: Facilitates identification of key temporal patterns associated with functional groups of genes.

Methodology:

Integrates gene selection, clustering, and functional annotation and performs integrated selection–clustering analyses per functional term (e.g., Gene Ontology), combining statistical evidence of differential expression with temporal expression profiles and functional annotations.