INCLUSive

INCLUSive performs integrated analysis of gene expression data and discovery of cis-regulatory sequence elements to identify regulatory motifs and functionally score gene clusters.

Key Features:

• Data normalization, filtering, and clustering: Normalizes, filters, and clusters microarray gene expression data to prepare high-quality datasets for downstream analysis.
• Functional scoring of gene clusters: Assigns functional scores to gene clusters to assess biological relevance and potential regulatory roles.
• Sequence retrieval: Retrieves genomic sequences associated with expression data for downstream motif discovery and comparative genomics.
• Detection of regulatory elements: Detects known and novel cis-regulatory elements using probabilistic sequence models and Gibbs sampling.
• Scalability to large datasets: Processes large-scale microarray and high-throughput genomic datasets for genome-wide analyses.

Scientific Applications:

• Gene regulation analysis: Identification of cis-regulatory motifs and regulatory modules underlying gene expression patterns.
• Developmental biology studies: Analysis of regulatory elements and gene clusters relevant to developmental processes.
• Disease mechanism and therapeutic research: Investigation of regulatory changes associated with disease progression and therapeutic targets.
• High-throughput genomic studies: Large-scale analysis of microarray and other high-throughput expression datasets for systems-level insights.

Methodology:

Methods explicitly include normalization, filtering, and clustering of microarray data; functional scoring of gene clusters; sequence retrieval; and the use of probabilistic sequence models and Gibbs sampling for regulatory element detection.