The accuracy of absolute differential abundance analysis from relative count data

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. In the differential abundance setting compositional effects are hypothesized to contribute to increased rates of spurious differences (false positives). However in practice, partial reconstruction of total abundance can be imputed through renormalization of observed per-sample abundance. Given the renormalized data differential abundance need not be called on relative counts themselves but on estimates of absolute counts. We use simulated data to explore the consistency of differential abundance calls made on these adjusted relative abundances and find that while overall rates of false positive calls are low substantial error is possible. Conditions consistent with microbial community profiling are the most at risk of error induced by compositional effects. Increasing complexity of composition (i.e. increasing feature number) is generally protective against this effect. In real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, results are similar: though median accuracy is high, microbial community profiling and single-cell transcriptomic data sets can have poor outcomes. However, we show that problematic data sets can often be identified by summary characteristics of their relative abundances alone, giving researchers a means of anticipating problems and adjusting analysis strategies where appropriate.

Emu: Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads

16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate of long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from one simulated data set and two mock communities prove Emu capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.

Simple amplicon sequencing library preparation for plant root microbial community profiling

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have developed a novel high-throughput method based on a two-step PCR amplification protocol, involving DNA extraction using magnetic beads and PCR purification using exonuclease, for 16S rRNA gene amplicon sequencing of plant root microbiota. This method reduces sample handling and captures microbial diversity comparable to that obtained by the standard method. We found that using a buffer with magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. In addition, we demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. Our method offers a simple and high-throughput solution for maintaining the quality of plant root microbial community profiling.

Nanoliter-scale next-generation sequencing library-mediated high-throughput 16S rRNA microbial community profiling

An ultra-high-throughput workflow for next-generation sequencing library construction at nanoliter scale for amplicon sequencing, termed Smartchip Nanowell Platform for Target Enrichment, was established using a nanodispenser system and a nanoliter-scale PCR chip. To demonstrate its cost and time advantages over conventional methods for library construction, quality control and pooling for large-scale samples, target amplicon sequencing of the 16S ribosomal RNA gene V3-V4 region widely used for microbial community profiling was chosen for comparison. The finding of no significant difference in microbial community profiling between the two methods strongly supports the conclusion that Smartchip Nanowell Platform for Target Enrichment is a cost-effective method for next-generation sequencing library construction for large-scale samples to conduct amplicon sequencing-based applications.