Deep sequencing artificially inflates estimates of microbial diversity
Sequencing artifacts challenge accuracy and reproducibility when quantifying microbial diversity. To track error propagation in microbiome analyses, we analyze no-diversity amplicons, which are amplified from host genes with limited genetic diversity or from synthetic spike-ins. We find that sequencing at greater than 104 reads exponentially increased no-diversity amplicon sequence variant (ASV)…
A study highlights how sequencing artifacts can lead to inaccurate and inconsistent estimates of microbial diversity. By analyzing "no-diversity amplicons," which are derived from host genes with limited genetic diversity or synthetic spike-ins, researchers discovered that sequencing greater than 100,000 reads exponentially increases no-diversity amplicon sequence variant (ASV) richness. This means that hundreds of ASVs are observed per sample, which significantly inflates the diversity estimates.
This phenomenon was not exclusive to microbial amplicons like 16S rRNA, ITS, gyrB, and rpoB. Inflated Shannon diversity was also found with increasing read counts for both the overall community and within individual taxa. By comparing sequencing error profiles between no-diversity and microbial amplicons, researchers found that truncating reads to shorter lengths and using the AVITI Element platform can help reduce, but not completely eliminate, the effects of artificial inflation.
The study emphasizes the importance of exercising caution when analyzing data where read depths vary significantly between samples. It concludes that using no-diversity amplicons can be beneficial in optimizing parameters to improve the accuracy of microbial diversity estimates.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
