PyroMap

PyroMap maps pyrosequencing reads to reference sequences using a selectively weighted Smith-Waterman (SW^2) algorithm that integrates base quality scores to improve alignment accuracy and enable detection of minor variants.

Key Features:

• Selectively weighted Smith-Waterman (SW^2): Integrates base quality scores into Smith-Waterman alignments to weight mismatches and gaps during mapping of pyrosequencing reads.
• Empirical error-rate estimation: Estimates empirical error rates from known sequences such as HIV-1 plasmid clones to inform statistical analyses.
• Statistical error management: Applies statistical methods to distinguish authentic minor variants from pyrosequencing sequencing errors.
• Detection of minor variants: Detects low-frequency sequence variants within ultra-deep pyrosequencing data for characterization of genetic diversity and resistance-associated mutations.

Scientific Applications:

• Characterizing genetic diversity: Accurate mapping and minor-variant detection in microbial or viral populations to quantify intra-sample sequence diversity.
• Managing drug-resistant infections: Identification of low-frequency mutations, including in HIV protease and reverse transcriptase genes, that may confer antiretroviral drug resistance.

Methodology:

Uses a selectively weighted Smith-Waterman (SW^2) alignment integrating base quality scores, empirical error-rate estimation from known sequences (e.g., HIV-1 plasmid clones), and statistical error-management methods applied to ultra-deep pyrosequencing data.