The advent of next-generation sequencing has revolutionized microbial ecology research, but it has also introduced challenges due to sequencing errors and PCR artifacts. The study by Schloss, Gevers, and Westcott aimed to reduce these artifacts in 16S rRNA gene sequencing data. They analyzed mock community samples to evaluate the impact of sequencing errors and chimeras on downstream analyses. The average error rate was 0.0060, which was reduced to 0.0002 through various methods, including quality filtering and the PyroNoise algorithm. Chimeras, which are formed during PCR, were identified in 8% of raw reads, but this rate decreased to 1% after quality filtering and using the Uchime chimera detection program. The presence of chimeras led to the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes, which increased with sequencing effort. The study also highlighted the importance of standardizing sample sizes to account for biases related to sequencing effort. Finally, the authors applied their improved quality-filtering pipeline to benchmarking studies, demonstrating that even with stringent data curation, biases in the data generation pipeline and batch effects can still confound the interpretation of microbial community data.The advent of next-generation sequencing has revolutionized microbial ecology research, but it has also introduced challenges due to sequencing errors and PCR artifacts. The study by Schloss, Gevers, and Westcott aimed to reduce these artifacts in 16S rRNA gene sequencing data. They analyzed mock community samples to evaluate the impact of sequencing errors and chimeras on downstream analyses. The average error rate was 0.0060, which was reduced to 0.0002 through various methods, including quality filtering and the PyroNoise algorithm. Chimeras, which are formed during PCR, were identified in 8% of raw reads, but this rate decreased to 1% after quality filtering and using the Uchime chimera detection program. The presence of chimeras led to the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes, which increased with sequencing effort. The study also highlighted the importance of standardizing sample sizes to account for biases related to sequencing effort. Finally, the authors applied their improved quality-filtering pipeline to benchmarking studies, demonstrating that even with stringent data curation, biases in the data generation pipeline and batch effects can still confound the interpretation of microbial community data.