Improved metagenomic analysis using low error amplicon sequencing or how human intelligence defeats nature’s errors
Humans and polymerases have something in common: They make errors. Empathy ends when polymerase errors during amplification of community samples generate sequence artifacts that impede the accurate metagenomic analysis of the actual community. Read on and find out how human intelligence and IMGM beat nature’s errors and generate more precise metagenomic analyses than ever.
Metagenomic analyses of 16S rRNA or other amplicons by next generation sequencing (NGS) are widely used to describe the phylogenetic composition of microbial communities. However, a considerable discrepancy between our deduced conception and the actual composition of the sequenced community is generated by erroneous polymerase activity during amplification and library preparation or by inaccuracies during sequencing. Unfortunately, this error rate is due to currently unchangeable natural and technological constraints. Unlike resequencing approaches, metagenomic analyses lack a correcting reference genome, making it impossible to detect or correct such errors.
We are happy to announce that we have now lowered significantly this unsatisfying error rate and nagging discrepancy by optimizing the usability of a low error amplification method for sequencing amplicon libraries in metagenomic analyses.
Using a pool of unique random barcodes to label the DNA template pool of a microbial community will result in unique barcode-template combinations (see figure, STEP 1). Subsequently, the whole template pool is amplified and sequenced (see figure, STEP 2), but due to the unique barcode, all sequences with the same barcode can be traced back to the unique original DNA template from STEP 1. Therefore, clustering those template copies labeled with the same unique barcode allows building a consensus sequence of the initial DNA template. The beauty of this method lays exactly in this clustering step: Every template copy serves as correcting sequence for the others and thus enables the elimination of amplification and sequencing errors using bioinformatics tools (see figure, consensus sequence generation). Duplicate sequences within clusters are also eliminated in this step, thus diminishing PCR amplification bias.
The correction of such amplification and sequencing errors directly leads to better phylogenetic assignment and improved sequencing precision in general. Compared to the discrepancy introduced by classic metagenomic sequencing approaches, the presented low error amplification method enables a more precise picture of the actual composition of the analyzed community and thus opens otherwise non-thinkable perspectives for all metagenomic applications.
Analyzing reality instead of artifacts and being more precise than outdated methods touches a nerve in you when it comes to your metagenomic research projects? Get in touch with our team via our contact sheet or call +49.89.4524667.0. For more information on IMGM’s service portfolio check out our homepage www.IMGM.com