Thank you for clerification.
based on my Raw data i get error rates around 2–2.2%.
Shold i use --nano-hq or --nano-raw.
what do you suggest based on your experience ?
Hi reader,
I have some nanopore sequenced fungal sample. Sequenced with R10.4 cell and LSK114 kit.
Some initial Information
Question 1: I am not sure about the term Error-Rate in sequencing. Can you guide or point me to direction where I can read about it and calculate it in my data sets ?
The Sequencing company already basecalled the data with Dorado and provided fastQ files as pass/fail. I want to generate assemblies so using fastQ-Pass files for downstream analysis.
I have used Porechop to remove adapters and Chopper to remove reads with Q<10 and Length<2000.
I am using Flye v-2.9.5-b1801 for genome assemblies. I have question regarding selecting the read-type option.
Question 2: out of these two which one is more suitable for my data .
--nano-raw path [path ...]
ONT regular reads, pre-Guppy5 (<20% error)
--nano-hq path [path ...]
ONT high-quality reads: Guppy5+ SUP or Q20 (<5% error)
Also, Is the --scaffold option in Flye assembler recomended to use ?
Thank you.
Error rate typically means a Phred like score
https://en.wikipedia.org/wiki/Phred_quality_score
where the error rate E is plugged into the formula
Error probability = 10^(-E/10)
so for example E=20 would be 10^-2 --> P = 1/100 = 0.01 that is 1% error, one error every hundred basecalls.
Sometimes people call it E or Q, sometimes it is shown as P (probability) as a fraction, and sometimes it is expressed as a percent, so it can be a bit confusing.
Thank you for clerification.
based on my Raw data i get error rates around 2–2.2%.
Shold i use --nano-hq or --nano-raw.
what do you suggest based on your experience ?
If data has been basecalled with dorado using super accuracy (SUP) or high accuracy (HAC) models then use the --nano-hq.
Hi. What if i don't know which dorado model has been used ? Is there any way to find that out from fast5 fastQ or the summary file created by of each sample.
Fastq file header should contain the model used for basecalling. I see that in files I work with that are rebasecalled. Q score distribution should be better than Q20 (on avg), if the data is high or super accuracy.
Thank you. I found it to be basecall_model_version_id=dna_r10.4.1_e8.2_400bps_hac@v4.2.0 but my Q-score is Q16 on average.
I have already generated assemblies using --nano-raw and polished them but now i am confused if i had to use --nano-hq
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