Perspectives and Benefits of High-Throughput Long-Read Sequencing in Microbial Ecology

Abstract

Short-read, high-throughput sequencing (HTS) methods have yielded numerous important insights into microbial ecology and function. Yet, in many instances short-read HTS techniques are suboptimal, for example, by providing insufficient phylogenetic resolution or low integrity of assembled genomes. Single-molecule and synthetic long-read (SLR) HTS methods have successfully ameliorated these limitations. In addition, nanopore sequencing has generated a number of unique analysis opportunities, such as rapid molecular diagnostics and direct RNA sequencing, and both Pacific Biosciences (PacBio) and nanopore sequencing support detection of epigenetic modifications. Although initially suffering from relatively low sequence quality, recent advances have greatly improved the accuracy of long-read sequencing technologies. In spite of great technological progress in recent years, the long-read HTS methods (PacBio and nanopore sequencing) are still relatively costly, require large amounts of high-quality starting material, and commonly need specific solutions in various analysis steps. Despite these challenges, long-read sequencing technologies offer high-quality, cutting-edge alternatives for testing hypotheses about microbiome structure and functioning as well as assembly of eukaryote genomes from complex environmental DNA samples.

Keywords: Oxford Nanopore sequencing; PacBio sequencing; bioinformatics; direct RNA sequencing; epigenomics; nanopore; synthetic long reads; unique molecular identifiers (UMI).

FIG 1

Comparison of workflows of the third-generation sequencing platforms for DNA and RNA sequencing. (Left) Pacific BioSciences PacBio SMRT sequencing; (right) Oxford Nanopore Technologies nanopore sequencing.

FIG 2

Workflow of producing synthetic long reads (SLRs) using unique molecular identifiers (UMIs) (A) and the following three alternative short-read sequencing platforms: MGI Tech (B), Illumina (C), and Ion Torrent (D). The SLR preparation follows the LOOPseq protocol (74).

FIG 3

Taxonomic resolution of rRNA marker gene fragments in short-read and long-read analyses. Distribution of taxonomic information in the rRNA operon of bacteria (A) and fungi (B). Relative sequence similarity (C, D) and number of mismatching nucleotides (E, F) in long-read and short-read rRNA gene sequence data of bacteria (left) and fungi (right) at the level of kingdom (open columns) and genus (shaded columns) based on pairwise alignments of fragments. The values are given as mean (box) ± standard deviation (whiskers) and median (asterisk). Different letters above bars depict statistically significant differences among groups (P_adj < 0.001). Mean fragment length ± standard deviation is indicated below the fragments. The methods of calculating distances are outlined in Item S1 in the supplemental material.