DeSP

DeSP simulates noise and error propagation across DNA data storage workflows to quantify sequence loss and within-sequence errors and to inform encoding redundancy and error-correction optimization.

Key Features:

• Systematic Error Simulation: Simulates errors across all stages of DNA data storage, including sequence loss and within-sequence errors, to model how errors propagate to sequencing outcomes.
• Optimization of Encoding Redundancy: Uses simulated noise structures specific to DNA storage channels to evaluate and optimize encoding redundancy for balancing information density and error-correction performance.
• Influence Analysis: Analyzes the effects of varying error rates and sampling depths on final error outcomes.
• Consistency with Experimental Results: Validates simulation outputs against in vitro experiments to ensure correspondence with empirical observations.
• Flexibility and Adaptability: Implemented in Python as a flexible framework intended for integration into experimental pipelines.

Scientific Applications:

• Encoding strategy optimization: Inform selection of redundancy levels and encoding strategies that balance redundancy with information density for DNA-based data storage.
• Error-correction design and benchmarking: Support optimization and evaluation of error-correction codes tailored to observed noise structures.
• Experimental planning and cost reduction: Guide experimental design by predicting how error rates and sampling depth affect outcomes, reducing reliance on extensive in vitro testing.
• Validation against empirical data: Compare simulation predictions with in vitro sequencing experiments to validate storage system designs.

Methodology:

Simulates the DNA storage process from encoding through sequencing, modeling sequence loss and within-sequence errors and performing systematic simulations across varying error rates and sampling depths to evaluate noise structures and optimize encoding redundancy.