NaviSE

NaviSE integrates and algebraically combines epigenomic sequencing data to predict and annotate super-enhancers and associated regulatory features.

Key Features:

• Automated Processing: Integrates and algebraically combines raw sequencing data into annotated reports on predicted super-enhancers, with parallel processing reducing computational time by up to 30% on a 15 CPU machine.
• Epigenomics Signal Algebra: Combines multiple activation and repression epigenomic signals through an algebraic system to enhance super-enhancer prediction accuracy.
• Chromosomal Landscaping Annotation: Generates chromosomal landscape annotations including signal profiles, associated genes, gene ontology enrichment, and transcription factor binding site motifs.
• Comprehensive Analysis Features: Produces graphs evaluating super-enhancer quality, protein–protein interaction networks, and enriched metabolic pathways.
• Multi-Level Data Processing: Supports data formats from SRA-FastQ to BED and unifies processing of multiple replicates.
• Biological Insights: Predicts cell type-specific markers such as SOX2 and ZIC3 in embryonic stem cells, CDK5R1 and REST in neurons, and CD86 and TLR2 in monocytes.
• Optimized Performance: Default parameters provide high performance and outperform traditional non-integrated pipelines.

Scientific Applications:

• Cell fate determination: Facilitates analysis of super-enhancer-associated regulatory programs involved in cell fate determination and differentiation.
• Disease mechanism research: Enables identification of disease-associated regulatory elements and markers in cancer and neurodegeneration.
• Epigenomic regulatory analysis: Supports extraction of regulatory insights from next-generation sequencing epigenomic datasets for annotation and interpretation of regulatory elements.

Methodology:

Integrates raw sequencing data (SRA-FastQ to BED) across replicates, applies an algebraic integration of activation and repression epigenomic signals to predict super-enhancers, annotates predicted regions with signal profiles, associated genes, gene ontology enrichment, transcription factor binding site motifs, protein–protein interaction networks and pathway enrichment, and employs parallel processing (up to 30% time reduction on a 15 CPU machine).