An R package for quality control of Oxford Nanopore sequencing data
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.
Full documentation: https://LRB-IIMCB.github.io/NanoQuRe/
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.
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.
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.
pore_activity_heatmap()(andgenerate_report(), which calls it) takes aplatformargument —"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.
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).
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.