Simulation with RADinitio improves RADseq experimental design and sheds light on sources of missing data

Area of Science:

• Population genomics
• Molecular evolution
• Bioinformatics

Background:

• Restriction-site associated DNA sequencing (RADseq) is a key tool for large-scale evolutionary and genomic studies.
• Diverse RADseq protocol variants exist, posing challenges for researchers in selecting optimal methods.
• Suboptimal protocol choices can lead to biased data and reduced analytical power.

Purpose of the Study:

• To introduce RADinitio, a novel simulation software for optimizing RADseq experimental design.
• To enable researchers to select and refine RADseq protocols by generating realistic sequencing data.
• To assess the impact of different RADseq protocols and library preparation steps on data quality and missing data.

Main Methods:

• RADinitio simulates population evolution and incorporates various RADseq protocols with user-defined parameters.
• The software generates simulated sequencing data comparable to empirical RADseq datasets.
• The study evaluates RADinitio's efficacy across different organisms and RADseq protocols, analyzing missing data and allelic dropout.

Main Results:

• Protocol selection significantly impacts the magnitude and quality of acquired RADseq data.
• Library preparation and sequencing steps are major contributors to missing alleles, often exceeding the impact of population-level variation.
• RADinitio effectively simulates RADseq data, aiding in protocol selection and experimental design.

Conclusions:

• RADinitio is a valuable tool for optimizing RADseq experiments in population genomics.
• Careful consideration of library preparation and sequencing protocols is essential to minimize missing data in RADseq studies.
• The software enhances the power of evolutionary and genomic analyses by improving data quality and reducing bias.