Schloss PD, Gevers D, Westcott SL (2011) Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies. PLoS ONE 6(12): e27310. doi:10.1371/journal.pone.0027310 The study addresses the challenges of reducing sequencing and PCR artifacts in 16S rRNA gene-based microbial community studies. Using 2.7 million reads from 90 identical mock communities, the researchers found an average error rate of 0.0060. They improved this to 0.0002 by implementing quality filtering and using the PyroNoise algorithm. PCR chimeras were also identified, with 8% of raw reads being chimeric, which decreased to 1% after quality filtering and chimera detection. Chimeras contributed to spurious operational taxonomic units (OTUs) and phylotypes, emphasizing the need for equal sequencing effort in community comparisons. The study also evaluated the impact of sequencing centers and found biases in data generation and batch effects. They developed a quality-filtering pipeline to reduce errors and chimeras, which improved the accuracy of microbial community data. The study highlights the importance of standardizing sequencing efforts and using internal controls to minimize artifacts. The results show that even with rigorous data curation, biases and chimeras can affect microbial community analysis. The study concludes that improving experimental design and developing correction methods is essential for accurate 16S rRNA-based studies.Schloss PD, Gevers D, Westcott SL (2011) Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies. PLoS ONE 6(12): e27310. doi:10.1371/journal.pone.0027310 The study addresses the challenges of reducing sequencing and PCR artifacts in 16S rRNA gene-based microbial community studies. Using 2.7 million reads from 90 identical mock communities, the researchers found an average error rate of 0.0060. They improved this to 0.0002 by implementing quality filtering and using the PyroNoise algorithm. PCR chimeras were also identified, with 8% of raw reads being chimeric, which decreased to 1% after quality filtering and chimera detection. Chimeras contributed to spurious operational taxonomic units (OTUs) and phylotypes, emphasizing the need for equal sequencing effort in community comparisons. The study also evaluated the impact of sequencing centers and found biases in data generation and batch effects. They developed a quality-filtering pipeline to reduce errors and chimeras, which improved the accuracy of microbial community data. The study highlights the importance of standardizing sequencing efforts and using internal controls to minimize artifacts. The results show that even with rigorous data curation, biases and chimeras can affect microbial community analysis. The study concludes that improving experimental design and developing correction methods is essential for accurate 16S rRNA-based studies.