SeqNet

SeqNet generates gene-gene networks and simulates RNA-seq data to create realistic in silico datasets for benchmarking and evaluating gene regulatory network inference methods.

Key Features:

• R package implementation: Implemented as an R package for integration into R-based analysis workflows.
• Gene Network Generation: Generates diverse gene-gene network structures that can be tailored to specific research needs.
• RNA-Seq Data Simulation: Simulates RNA-sequencing (RNA-seq) data that resemble experimental outputs, producing synthetic gene expression/count data.
• Benchmarking dataset generation: Produces in silico RNA-seq datasets intended for evaluating and comparing gene network inference methodologies.
• Realistic variability and structure: Produces synthetic expression data with realistic variability and network-structured dependencies.

Scientific Applications:

• Gene regulatory network inference: Provides realistic simulated RNA-seq datasets for developing and testing algorithms for inferring gene regulatory networks.
• Methodological comparisons: Enables comparative evaluation of different network inference methods using consistent benchmarking datasets.
• Educational use: Supplies simulated RNA-seq and network examples for teaching principles of RNA-seq analysis and network modeling.

Methodology:

Implemented as an R package that generates gene-gene networks and simulates RNA-sequencing (RNA-seq) data to produce in silico RNA-seq datasets with realistic variability and network structure.