I want to use Canu to correct my nanopore long read (version: MinION R9.5), but I am not quite sure how to set the correctErrorRate. Should I follow the Canu manual (Nanopore R7 2D and Nanopore R9 1D Increase the maximum allowed difference in overlaps from the default of 14.4% to 22.5% with correctedErrorRate=0.225), or you guys have a better option?
It's generally a good idea to trust the "official" suggestions. You can also adjust the error rate based on coverage according to parameter reference:
For low coverage datasets (less than 30X), we recommend increasing correctedErrorRate slightly, by 1% or so.
For high-coverage datasets (more than 60X), we recommend decreasing correctedErrorRate slighly, by 1% or so.
Raising the correctedErrorRate will increase run time. Likewise, decreasing correctedErrorRate will decrease run time, at the risk of missing overlaps and fracturing the assembly.
That being said, the sequencing quality can vary from between libraries or flow cells. The error rate is not a constant.
According to the developer (full thread):
if you want to assemble 1D data or have a bad run, it is possible you would need to increase the error rate. You could run with a high error rate (0.1) and look at the distribution of overlap error rates in the unitig step to look for a peak in the distribution. If you have a near neighbor you could also map the corrected reads to it and estimate the residual error that way.
There seems to be some confusion here.
For the first stage of the Canu pipeline (read correction), i think the parameter to use is rawErrorRate.
From the docs:
rawErrorRate The allowed difference in an overlap between two uncorrected reads, expressed as fraction error. Sets corOvlErrorRate and corErrorRate. The rawErrorRate typically does not need to be modified. It might need to be increased if very early reads are being assembled. The default is 0.300 For PacBio reads, and 0.500 for Nanopore reads.
I think that correctedErrorRate is what you use at the next stages of the pipeline (trimming and assemble).