EXPANDER

EXPANDER: Integrated Gene Expression and Regulatory Network Analysis Platform

EXPANDER performs genome-wide gene expression analysis across 18 organisms, integrating preprocessing, normalization, differential expression, clustering, functional enrichment, transcriptional network inference, regulatory motif analysis, and ChIP-seq data integration.

Key Features:

• Broad Organism Support: Enables genome-wide analyses for 18 organisms.
• Preprocessing and Normalization: Processes gene expression data matrices with low-level preprocessing and normalization to ensure data quality and comparability.
• Differential Expression Analysis: Identifies genes with significant expression changes between experimental conditions.
• Clustering and Biclustering: Groups genes by similar or condition-specific expression patterns.
• Supervised Grouping: Applies supervised classification methods to predefined gene sets or conditions.
• Functional and Pathway Enrichment: Performs high-level functional annotation and pathway enrichment analysis.
• Transcriptional Network Inference: Constructs transcriptional regulatory networks.
• Regulatory Motif Analysis: Detects enriched regulatory motifs associated with gene sets.
• ChIP-seq Integration: Integrates gene expression profiles with ChIP-seq data to refine transcriptional network inference and functional interpretation.

Scientific Applications:

• Gene Expression Profiling: Characterizes condition-specific gene expression patterns and regulatory programs.
• Transcriptional Network Analysis: Elucidates gene regulatory interactions and pathway-level mechanisms.
• Multi-Omics Integration: Combines gene expression and ChIP-seq data to study transcription factor binding and downstream effects.

Methodology:

Implements published computational algorithms for gene expression preprocessing, normalization, differential expression testing, clustering, supervised grouping, enrichment analysis, transcriptional network reconstruction, regulatory motif discovery, and integration of ChIP-seq datasets.