SimGBS.jl

SimGBS.jl simulates genotyping-by-sequencing (GBS) data from diploid genomes to enable benchmarking and development of methods that account for genotype uncertainty in GBS datasets.

Key Features:

• Genome Input Flexibility: SimGBS.jl accepts any real reference genome or simulated diploid genome as input for GBS data simulation.
• Population Structure Definition: Users can define populations with distinct demographic histories by modifying population size over specified generations or by providing a pedigree to generate structured populations.
• Customizable GBS Parameters: Most GBS parameters are highly customizable, including the choice of restriction enzyme and sequencing depth.
• Multiple Output Formats: SimGBS.jl outputs both true genotype calls and DNA sequences in FASTQ format.
• Cross-Platform Implementation: The method is implemented in Julia and can be accessed from R through the JuliaCall interface.
• Computational Efficiency: SimGBS.jl is capable of generating datasets for thousands of individuals, with simulations for 172 samples completing in less than two hours.

Scientific Applications:

• GBS Experiment Design: SimGBS.jl assists with designing GBS experiments by allowing researchers to simulate data under various parameter settings.
• Bioinformatic Pipeline Evaluation: SimGBS.jl enables evaluation of bioinformatic pipelines for GBS data analysis.
• Statistical Method Development: SimGBS.jl supports development and benchmarking of statistical methods that account for genotype uncertainty in GBS datasets.
• Population Genetics Studies: SimGBS.jl facilitates large-scale population genetics studies, particularly in non-model species.

Methodology:

SimGBS.jl simulates large-scale GBS data by processing input diploid genomes with customizable restriction enzyme digestion and sequencing depth parameters, generating true genotype calls and FASTQ-format DNA sequences.