Multiplexed long-read plasmid validation and analysis using OnRamp

Abstract

Recombinant plasmid vectors are versatile tools that have facilitated discoveries in molecular biology, genetics, proteomics, and many other fields. As the enzymatic and bacterial processes used to create recombinant DNA can introduce errors, sequence validation is an essential step in plasmid assembly. Sanger sequencing is the current standard for plasmid validation; however, this method is limited by an inability to sequence through complex secondary structure and lacks scalability when applied to full-plasmid sequencing of multiple plasmids owing to read-length limits. Although high-throughput sequencing does provide full-plasmid sequencing at scale, it is impractical and costly when used outside of library-scale validation. Here, we present Oxford nanopore-based rapid analysis of multiplexed plasmids (OnRamp), an alternative method for routine plasmid validation that combines the advantages of high-throughput sequencing's full-plasmid coverage and scalability with Sanger's affordability and accessibility by leveraging nanopore's long-read sequencing technology. We include customized wet-laboratory protocols for plasmid preparation along with a pipeline designed for analysis of read data obtained using these protocols. This analysis pipeline is deployed on the OnRamp web app, which generates alignments between actual and predicted plasmid sequences, quality scores, and read-level views. OnRamp is designed to be broadly accessible regardless of programming experience to facilitate more widespread adoption of long-read sequencing for routine plasmid validation. Here we describe the OnRamp protocols and pipeline and show our ability to obtain full sequences from pooled plasmids while detecting sequence variation even in regions of high secondary structure at less than half the cost of equivalent Sanger sequencing.

Figure 1.

OnRamp protocol and pipeline. Pooled plasmids have nanopore adapters added by transposase or by digestion and ligation (A) and then are sequenced (B). (C) Base-called reads are provided to the OnRamp web app, which generates consensus sequences. (D) Consensus sequences are then aligned to user-provided references to identify variation.

Figure 2.

OnRamp web app. (A) Image of OnRamp submission page, where users submit read data and plasmid reference files and choose analysis settings. (B–D) Output generated from example data, including sequence alignments (B), alignment quality metrics (C), and IGV viewer panel showing individual reads (D).

Figure 3.

Real plasmid sequencing experiment characteristics and variant detection. (A) Per-plasmid read depth across pooled sequencing runs. (B) Per-plasmid count of bases in consensus sequence that differ from reference (gaps). (C) Table describing variation contributing to outliers (labeled points) in A and B. (D) Table summarizing read and gap data for experiments shown in A and B (gap counts do not include variants listed in C). (E) OnRamp alignment results showing a 22-bp deletion. (F) Sanger validation of deletion in E. (G) IGV browser view of reads mapping to deletion (red outline) from E in an SV40 promoter (green box). (Left inset) Zoomed view. (H) IGV view of reads mapping to a clone without (top) or a clone with (bottom) a subclonal repetitive element (orange boxes) deletion (red outline). IGV: Black lines are deletions; dark purple marks are insertions.

Figure 4.

Restriction-digest barcoding for highly similar or clonal plasmids. (A) Diagram of restriction cut-site method for unique read mapping of clonal plasmids using “rotated” references. (B) Number of reads mapping uniquely to each plasmid in a 15-plasmid clonal test pool. (C) Number of reads mapping uniquely to each reference in a nine-plasmid mixed clonal run.