The paper "Removing Noise From Pyrosequenced Amplicons" by Christopher Quince, Anders Lanzen, Russell J Davenport, and Peter J Turnbaugh addresses the challenges of distinguishing true sequence diversity from noise in 454 pyrosequencing data, particularly in the context of microbial diversity studies. The authors introduce AmpliconNoise, an algorithm that separately removes sequencing errors and PCR single base substitutions, and Perseus, a chimera removal program that leverages sequence abundances. They demonstrate that AmpliconNoise significantly reduces per-base error rates and accurately estimates the number of Operational Taxonomic Units (OTUs) by accounting for the differential rates of nucleotide errors in PCR. Perseus is shown to have high sensitivity in detecting chimeras, crucial for accurate OTU construction. The study uses a series of test data sets to validate the effectiveness of these algorithms, highlighting the importance of removing noise to improve the reliability of microbial diversity estimates. The results indicate that AmpliconNoise followed by Perseus is an effective pipeline for noise removal, with potential applications in new sequencing technologies.The paper "Removing Noise From Pyrosequenced Amplicons" by Christopher Quince, Anders Lanzen, Russell J Davenport, and Peter J Turnbaugh addresses the challenges of distinguishing true sequence diversity from noise in 454 pyrosequencing data, particularly in the context of microbial diversity studies. The authors introduce AmpliconNoise, an algorithm that separately removes sequencing errors and PCR single base substitutions, and Perseus, a chimera removal program that leverages sequence abundances. They demonstrate that AmpliconNoise significantly reduces per-base error rates and accurately estimates the number of Operational Taxonomic Units (OTUs) by accounting for the differential rates of nucleotide errors in PCR. Perseus is shown to have high sensitivity in detecting chimeras, crucial for accurate OTU construction. The study uses a series of test data sets to validate the effectiveness of these algorithms, highlighting the importance of removing noise to improve the reliability of microbial diversity estimates. The results indicate that AmpliconNoise followed by Perseus is an effective pipeline for noise removal, with potential applications in new sequencing technologies.