![]() Furthermore, in-house workflows oftentimes do not contain steps to mitigate biases nor common artifacts including reagent kit contaminants or cross-talk among index sequences. differing 16S rRNA primer sets, polymerases, PCR cycling conditions, and/or implementation of different bioinformatic software ) make it difficult to identify methodological choices that maximize a workflow’s ability to correctly return all distinct sequences (coverage) and maximize the proportion of correct sequences (accuracy). Thus, methodological preferences at each step of a custom workflow (e.g. While techniques to study the microbiome have been adopted across many scientific fields, standardization of workflows to produce microbiome data has lagged, prompting researchers to develop in-house workflows. With paired-end sequencing, two reads (R1 and R2) for each 16S rRNA template are produced by the instrument and are typically denoised by trimming and removing low-quality sequences before or after merging. ![]() Finally, resulting reads are computationally demultiplexed using the sample-specific index sequences. The amplicons are then pooled and sequenced using a single- or paired-end sequencing approach. From the gDNA, typically the 16S rRNA marker gene is amplified using polymerase chain reaction (PCR) and labeled with sample-specific index sequences. First, genomic DNA (gDNA) is extracted from the bacteria present in individual samples through physical or chemical lysing. Workflows to generate compositional microbiome data from a sample requires several laboratory-based sample preparation and computational steps. The use of next-generation sequencing in biological research laboratories has offered important insight into the role of microbial ecology to environmental and human health. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health. Optimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. ![]() Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Of the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. ![]() Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. The development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. ![]()
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