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NanoQuRe

An R package for quality control of Oxford Nanopore sequencing data

Introduction

NanoQuRe reads sequencing summary files produced by ONT basecallers (Guppy or Dorado) and generates interactive plots, summary tables, and a self-contained HTML QC report — all from a single function call.

What you get Function
Full HTML QC report (auto single/multi-sample layout) generate_report()
Run-level summary table sequencing_stats()
Quality metrics table (N50, mean Q, longest read) quality_stats()
Cumulative yield / throughput / speed plots plot_cumulative_yield(), plot_seq_throughput(), plot_average_speed()
Read length & Q-score distribution plots plot_read_lengths(), plot_quality_distribution()
Channel activity & pore heatmap plots plot_active_channels(), pore_activity_heatmap(), plot_double()

Unlike packages built around external basecallers/ML toolchains, NanoQuRe is a pure R package with no compiled code and no external system dependencies — install and go.

Documentation

Full documentation: https://LRB-IIMCB.github.io/NanoQuRe/

Installation

NanoQuRe is not currently available on CRAN. Install it using devtools:

install.packages("devtools")
devtools::install_github("LRB-IIMCB/NanoQuRe")
library(NanoQuRe)

No compilation step and no external tools required — installation should take just a few seconds on any platform.

Usage

The quickest path is a single call on the bundled example dataset:

library(NanoQuRe)
data(sample_data)

generate_report(sample_data, output_file = "QC_report.html")

This renders a self-contained HTML report — covering read length and quality distributions, cumulative yield, throughput, translocation speed, channel activity, and pore occupancy — to your current working directory (or wherever you point output_dir).

Every plot and table behind the report is also available standalone, if you want to embed one in your own analysis or R Markdown document:

plot_read_lengths(sample_data)
quality_stats(sample_data)

See the Getting Started and Using Individual Functions vignettes for the full walkthrough.

Input data

NanoQuRe expects a data frame with the columns produced by ONT basecallers: sample_id, run_id, channel, start_time, duration, sequence_length_template, mean_qscore_template, and passes_filtering. generate_report() accepts this either as an in-memory data frame or as a path to a tab-separated summary file.

Important notes

  • pore_activity_heatmap() (and generate_report(), which calls it) takes a platform argument — "minion" (512 channels, default) or "promethion" (3000 channels). Get this wrong for your actual flow cell and channel activity will be silently misrepresented.
  • Multi-sample reports render each sample’s pore heatmap independently; a single problematic sample (e.g. spanning more than one run_id) is reported inline rather than failing the whole report.

Troubleshooting

If you encounter a bug, please open an issue on GitHub, ideally with a minimal reproducible example (a handful of rows of sequencing summary data is usually enough).

Maintainer

Any issues regarding NanoQuRe should be addressed to Natalia Gumińska (nguminska (at) iimcb.gov.pl).

NanoQuRe was developed in the Laboratory of RNA Biology at the International Institute of Molecular and Cell Biology in Warsaw.