Pyrosequencing of 16S rDNA is widely used to study microbial communities, and a rich set of software tools support this analysis. Pyrosequencing of protein-coding genes, which can help elucidate functional differences among microbial communities, significantly lags behind 16S rDNA in availability of sequence analysis software. In both settings, frequent homopolymer read errors inflate the estimation of microbial diversity, and de-noising is required to reduce that bias. Here we describe FunFrame, an R-based data-analysis pipeline that uses recently described algorithms to de-noise functional gene pyrosequences and performs ecological analysis on de-noised sequence data. The novelty of this pipeline is that it provides users a unified set of tools, adapted from disparate sources and designed for different applications, that can be used to examine a particular protein coding gene of interest. We evaluated FunFrame on functional genes from four PCR-amplified clones with sequence depths ranging from 9084 to 14 494 sequences. FunFrame produced from one to nine Operational Taxanomic Units for each clone, resulting in an error rate ranging from 0 to 0.18%. Importantly, FunFrame reduced spurious diversity while retaining more sequences than a commonly used de-noising method that discards sequences with frameshift errors.