ART

ART simulates next-generation sequencing (NGS) reads using platform-specific empirical error models and quality profiles to generate realistic synthetic datasets for development and benchmarking of NGS analysis methods.

Key Features:

• Technology-Specific Simulation: Emulates sequencing processes using empirical error models and quality profiles specific to Illumina's Solexa, Roche's 454, and Applied Biosystems' SOLiD platforms.
• Customizable Error Models: Accepts user-supplied error model parameters and quality profiles to tailor simulations to specific experimental conditions.
• Read Types Supported: Produces single-end, paired-end, and mate-pair reads to match diverse sequencing library configurations.
• Realistic Dataset Generation: Generates synthetic reads that mimic real-world error characteristics for controlled evaluation of analytical methods.

Scientific Applications:

• Read alignment benchmarking: Provides simulated reads for evaluating alignment algorithms under known error and quality profiles.
• De novo assembly evaluation: Supplies platform-specific synthetic reads to test assembly methods across read types and error models.
• Genetic variation discovery testing: Enables assessment of variant calling techniques using controlled, realistic simulated datasets.

Methodology:

Simulates sequencing reads by replicating platform-specific processes and error characteristics using empirically derived quality profiles and error models parameterized from recalibrated sequencing data, with support for user-defined parameters.