SERGIO

SERGIO simulates single-cell gene expression by modeling transcription factor–gene regulatory interactions and transcriptional stochasticity to produce realistic datasets for benchmarking and gene regulatory network analysis.

Key Features:

• Gene Regulatory Network Integration: Utilizes user-provided gene regulatory networks to model transcription factor–gene regulatory interactions with multiple transcription factors regulating target genes.
• Stochastic Modeling: Models the inherent stochasticity of transcriptional processes to capture single-cell expression variability.
• Versatile Cellular State Simulation: Simulates steady-state cell populations and differentiation into multiple fates.
• In Silico Knockout Experiments: Performs virtual knockout experiments to assess the effects of transcription factor perturbations.
• Synthetic Data Matching Sequencing Platforms: Generates synthetic datasets that statistically resemble data from Illumina HiSeq2000, Drop-seq, Illumina 10X Chromium, and Smart-seq for benchmarking.

Scientific Applications:

• Benchmarking Single-Cell Tools: Produces synthetic datasets resembling specific sequencing platforms to benchmark single-cell analysis methods.
• Gene Regulatory Network Inference: Provides realistic simulated expression data for evaluation and validation of GRN inference methods.
• In Silico Knockout Experiments: Enables virtual knockout studies to identify key transcription factors such as Tcf7, Gata3, and Bcl11b involved in T cell differentiation.

Methodology:

Models transcription factor–gene regulatory interactions using user-provided gene regulatory networks, implements stochastic modeling of transcription, simulates steady-state and multi-fate differentiation, generates synthetic datasets resembling specified sequencing platforms, and performs virtual gene knockouts.