Long read quality control standards

[justinjj24]

Quality control metric for long read WGS:

• Oxford Nanopore technologies (ONT) and SMRT sequencing from Pacific Bioscience (PacBio)

• https://pmc.ncbi.nlm.nih.gov/articles/PMC7144081/

• https://www.biorxiv.org/content/10.1101/2024.08.05.606643v1

• Porechop, filtlong, canu, racon, and medaka

N50 of aligned reads

The most common QC approach for long reads is a dotplot or contour plot of average base accuracy (or mapped accuracy, if available) vs read length.

NanoPlot can be used for this:

https://github.com/wdecoster/NanoPlot

PycoQC
NanoGalaxy

• https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html

PacBio use the IsoSeq pipeline

[justinjj24]

Long-read sequencing provides longer sequences but often comes with higher error rates and lower throughput. QC metrics for long-read technologies include:

• Read length distribution: Ensures that the long reads are sufficiently long to span structural variants or complex genomic regions.
• Accuracy metrics: To evaluate base call errors, especially for technologies with higher raw error rates.
• Alignment quality: Mapping long reads against a reference genome, evaluating discrepancies in alignments or errors like base substitutions.
• Coverage uniformity: Ensures even distribution of reads across the genome.
  Tools like NanoPlot and PycoQC are specifically designed for long-read QC .

Both sequencil for various genomic research applications, with QC being crucial to ensure high-quality, reproducible, and comparable data, particularly in large-scale or clinical projects.

Sources
[1] FastQC: A tool for quality control of high throughput sequence data.
[2] Picard: A toolkit for high-throughput sequencing data analysis.
[3] NanoPlot: A tool for visualizing long read data from nanopore sequencing.
[4] PycoQC: A quality control tool for long-read sequencing from PacBio and Oxford Nanopore.