Effective double-digest RAD sequencing and genotyping despite large genome size

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• Genomic projects in nonmodel species face challenges, especially with large genomes.
• Double-digest RAD sequencing (ddRADseq) is common but requires rigorous quality control for reliable data.
• Large genome sizes in nonmodel plants complicate data acquisition and analysis.

Purpose of the Study:

• To establish a comprehensive workflow for ddRADseq in large-genome nonmodel plants.
• To address analytical challenges in obtaining reliable population genetic data from such species.
• To adapt bioinformatic filtering strategies for specific dataset features.

Main Methods:

• Analysis of two populations of Cypripedium calceolus (a large-genome nonmodel orchid).
• Workflow covers library preparation, bioinformatic filtering, and genetic diversity/differentiation inference.
• Exploration of filtering strategies to distinguish true from artefactual data.

Main Results:

• Demonstrated the necessity of adapting filtering strategies to dataset-specific characteristics.
• Highlighted the importance of considering organellar sequences in library preparation and analysis.
• Showcased a complete workflow from sample to genetic inference for large-genome nonmodel plants.

Conclusions:

• Rigorous quality control and tailored bioinformatic filtering are crucial for reliable genomic data in large-genome nonmodel species.
• Attention to experimental design, including organellar DNA, is vital for successful population genetic studies.
• High-quality library preparation and sequencing mitigate unforeseen technical and biological constraints.