NanoPlot is a program designed to produce graphs and summaries of sequencing experiment results produced by ONT. The program uses as input a series of fastq files (using the --fastq option), or a text file produced by ONT (by default called "sequencing_summary.txt"). By parsing fastq files directly, you can produce graphs with more detailed information.
Important
In this tutorial, we will work with the text file sequencing_summary_FAT98192_deec7cb2_ec30cd82.txt.gz. This file needs to be downloaded from a google drive link and needs to be stored in the NanoPlot folder in your local directory.
NanoPlot --summary sequencing_summary.txt --loglength --o summary-plots-log-transformedAs a result, the program will create a large number of plots (.png files) plus a compilation in html format. These files will be placed in a folder called "summary-plots-log-transformed" (available here, zipped folder).
An example of the output is the following plot, obtained from a previous sequencing experiment, using an R9 flowcell+chemistry (legacy):
This graph indicates how the DNA sequence length produced by ONT relates to their quality (expressed in values similar to Phred). Interestingly, many sequences have values below Phred 10 (i.e., probability of 1 in 10 nucleotides being called incorrectly). But how does this compare with the newest v14 kits and R10 flowcells (read more about ONT accurary here)? Recent studies suggest that R10 flowcells in combination with v14 sequencing kits do outperform older versions of flowcells and kits.
(taken from Ni et al 2023, Comp. Str. Biotech. J.)
Fig. 1. The quality of nanopore read from whole-genome shotgun (WGS) and single-cell whole-genome amplification (WGA) sequencing using R9.4.1 and R10.4 flow cells. A Both R10.4 reads from WGS and scWGA sequencing libraries outperformed the R9.4.1 reads in terms of original Guppy basecaller estimated (grey) and read mapping observed (white) accuracy with medium and quartiles. The dispersion of the boxplot also shows that observed read accuracy is higher than estimated ones accordingly. B The density distribution plot indicates the R10.4 reads had higher modal read accuracy than R9.4.1 both in estimated (top) and observed (bottom) ones. C R10.4 had a higher average accuracy detection rate on homopolymers ranging from 4 to 9 bp than R9.4.1 both for WGS and scWGA libraries. It can also identify the preference for adenine (A) and thymine (T) over cytosine (C) and guanine (G) in homopolymer detection of nanopore reads.
Here is a lenght vs sequence quality plot derived from the the sequencing experiment "Art_altilis_DRY_WGS17".
Low quaility sequences will always happen, regardless of the sequencing technogloy. Incidentally, there are certain regions of the genome that are more difficult to sequence (low complexity regions), and ONT will tend to produce low sequence quality data from these regions (i.e., higher probability of miscalled bases, see recommended literature section) if such regions are associated with high GC content:
(taken from Delahaye & Nicolas 2021, PLoSONE)
Figure 4. Global error rates of reads according to their GC content. GC contents were grouped by integer value. Only values supported by at least n reads were plotted. For each species the mean error rate is computed, and the median on all these values is displayed. A: Results for bacterial data, n = 100. Low-GC species have lower global error rates than high-GC ones.
Apart from the graphical output, NanoPlot generates a succinct report on the overall sequence, where values such as average and median sequence lengths, quality, number of reads etc. are provided. This file is usually called NanoStats.txt and looks like this:
General summary:
Active channels: 488.0
Mean read length: 1,363.1
Mean read quality: 13.0
Median read length: 783.0
Median read quality: 13.8
Number of reads: 4,488,678.0
Read length N50: 2,330.0
STDEV read length: 1,727.3
Total bases: 6,118,383,425.0
Number, percentage and megabases of reads above quality cutoffs
>Q5: 4196018 (93.5%) 5827.1Mb
>Q7: 3944100 (87.9%) 5526.7Mb
>Q10: 3515763 (78.3%) 5019.4Mb
>Q12: 2986773 (66.5%) 4403.6Mb
>Q15: 1635361 (36.4%) 2634.3Mb
Top 5 highest mean basecall quality scores and their read lengths
1: 29.7 (2194)
2: 29.7 (2100)
3: 28.6 (279)
4: 28.1 (178)
5: 28.0 (464)
Top 5 longest reads and their mean basecall quality score
1: 317219 (3.7)
2: 262893 (3.6)
3: 234317 (3.8)
4: 213357 (3.8)
5: 194829 (4.1)
- Produce a quality report from the file
sequencing_summary_FAT98192_deec7cb2_ec30cd82.txt.gzavailable in your local directory (NanoPlot folder). Make sure that the output folder is calledsaya_suka_sekali_genomics:P
- Delahaye C, Nicolas J. 2021. Sequencing DNA with nanopores: troubles and biases. PLoS ONE. https://doi.org/10.1371/journal.pone.0257521
- Orlov YL, Potapov VN. 2004. Complexity: an internet resource for analysis of DNA sequence complexity. Nuc. Ac. Res. 32. https://doi.org/10.1093%2Fnar%2Fgkh466
- Ni Y, Liu X, Simeneh Z, Yang M, Li R. 2023. Benchmarking of Nanopore R10.4 and R9.4.1 flow cells in single-cell whole-genome amplification and whole-genome shotgun sequencing. Comp. Str. Biotech. J. 21: 2353-2364. https://doi.org/10.1016/j.csbj.2023.03.038