pirs

pirs simulates Illumina paired-end reads from a reference genome to produce datasets with realistic error and quality distributions, GC%-depth coverage biases, and heterozygous diploid variation for sequencing analysis.

Key Features:

• Paired-end read simulation: pirs generates paired-end reads from a reference genome modeled on Illumina sequencing output.
• Empirical profile-based simulation: pirs leverages empirical Base-Calling and GC%-depth profiles derived from real re-sequencing data to reproduce read characteristics.
• Error and quality distribution replication: pirs reproduces observed error rates and quality score distributions from real Illumina data.
• Coverage bias modeling: pirs incorporates coverage bias patterns to simulate realistic variation in sequencing depth across genomic regions.
• Heterozygous diploid genome simulation: pirs can simulate heterozygous diploid genomes to represent allelic variation.

Scientific Applications:

• Re-sequencing and de novo assembly benchmarking: pirs provides realistic simulated datasets for benchmarking re-sequencing and de novo assembly methods.
• Development and testing of bioinformatics tools: pirs supplies realistic Illumina-like data to evaluate the accuracy and performance of algorithms and pipelines.
• Sequencing experiment design: pirs enables simulation of scenarios to anticipate analysis challenges and optimize experimental parameters.
• Training and education: pirs provides simulated datasets for teaching sequencing data analysis workflows.

Methodology:

Implemented in C++ and Perl and trained on empirical Illumina re-sequencing data to model Base-Calling and GC%-depth profiles, error profiles, quality score distributions, and coverage biases.