NetSeekR

NetSeekR provides comprehensive network analysis of RNA-Seq time series data in R to infer temporal regulatory networks from Next Generation Sequencing (NGS) experiments and integrate differential expression, correlation, and gene ontology analyses.

Key Features:

• Implementation: An R package that integrates multiple network analysis and differential expression methods into a unified framework.
• Time Series Analysis: Tailored support for RNA-Seq time series data to capture dynamic gene expression changes and infer temporal regulatory networks.
• Correlation and Regulatory Network Inference: Performs correlation network analysis and regulatory network inference to identify relationships and potential regulators among genes.
• Differential Gene Expression Analysis: Includes a differential expression module to identify genes with significant expression changes over time or between conditions.
• Gene Ontology Enrichment Analysis: Incorporates gene ontology enrichment analysis to provide functional interpretation of differentially expressed genes.
• Network Visualization: Provides network visualization capabilities to represent differentially expressed genes and their interactions within inferred regulatory networks.
• Support for Multiple RNA-Seq Read Mapping Methods: Compatible with various RNA-Seq read mapping methods to accommodate different preprocessing workflows.
• Comparative Genomics Integration: Facilitates integration of results from different bioinformatics methods to support comparative analyses across datasets.
• Standardization and Integration: Addresses standardization of input/output formats and normalization of results to enable consistent comparative analyses.

Scientific Applications:

• Systems Biology: Integration of large-scale NGS data with network analysis to study system-level gene interactions.
• Dynamic Regulatory Network Studies: Investigation of temporal regulatory networks and dynamic biological processes using time series RNA-Seq.
• Functional Interpretation: Functional analysis and hypothesis generation through differential expression and gene ontology enrichment.
• Comparative Genomics: Comparative analyses across datasets and methods to identify conserved or divergent regulatory patterns.

Methodology:

Alignment of RNA-Seq reads, differential gene expression analysis, correlation network analysis, regulatory network inference, and gene ontology enrichment analysis within an integrated R pipeline.