Stanford University HIV Drug Resistance Database - A curated public database designed to represent, store, and analyze the divergent forms of data underlying HIV drug resistance.

Syn-SCAN

Synonymous Nonsynonymous Mutation Rates between Sequences Containing Ambiguous Nucleotides (Syn-SCAN)

Nucleotide substitutions that result in an amino acid change are nonsynonymous; those that do not result in an amino acid change are synonymous. The ratio of nonsynonymous to synonymous substitutions in a protein-coding gene reflects the relative influence of positive selection and neutral evolution. Several methods have been developed to estimate the numbers of synonymous and nonsynonymous substitutions between two sequences and programs based on the method of Nei and Gojobori have been used frequently (e.g. MEGA , SNAP). These programs, however, ignore codons with allelic mixtures.

Direct PCR sequencing on genetic material containing allelic mixtures often results in sequences containing ambiguous nucleotides. Codons exhibiting allelic mixtures often present evidence of stronger evolutionary pressure than codons without mixtures. It is important to include this ambiguous nucleotide information in the assessment of codon synonymy because evolutionary pressure relates directly to drug resistance in several RNA virus quasispecies. "Synonymous Nonsynonymous Mutation Rates between Sequences Containing Ambiguous Nucleotides" (Syn-SCAN) calculates synonymous and nonsynonymous substitution rates using a model that includes allelic mixtures.


Syn-SCAN was written in Perl by Matthew Gonzales, Jonathan Dugan, and Robert Shafer. It is available for download for Windows and UNIX environments (version 0.9). Syn-SCAN is also available in two web-based implementations:
  • Paired HIV-1 RT and protease sequences
    This program compares sets of paired HIV-1 RT or protease sequences.
  • Multiple sequence alignment of any proteing coding gene
    Syn-SCAN calculates synonymous and nonsynonymous nucleotide substitution rates between aligned sequences according to the method of Nei and Gojobori using a model that includes genetic mixtures.