Answers.Body: <p>How are you evaluating sequencing error rate? My most recent re-calls of 2017 sequences are demonstrating median single-read accuracy over 96%.</p>
  <p>Before considering Illumina, it'd be worth it to do an initial correction using nanopore-only reads. This will make sure that the best results are obtained from the nanopore signal. First, make sure that the reads have been recalled using the latest highest-accuracy basecaller (currently guppy v5.0.11):</p>
  <pre><code>guppy_basecaller -c dna_r9.4.1_450bps_sup.cfg ... # for 9.4.1 reads
  guppy_basecaller -c dna_r10.3_450bps_sup.cfg ... # for 10.3 reads
  </code></pre>
  <p>If you don't have any reference sequences available, reads can be corrected using the other reads by using <a href="https://canu.readthedocs.io/en/latest/quick-start.html" rel="nofollow noreferrer">canu</a>:</p>
  <pre><code>canu -correct -p nonModel -d nonModel genomeSize=10m -nanopore reads.fastq
  </code></pre>
  <p>If you <em>do</em> have reference sequences available, then reads can be corrected using <a href="https://github.com/nanoporetech/megalodon#getting-started" rel="nofollow noreferrer">megalodon</a>. I don't have any experience with megalodon, but have seen a couple of papers demonstrating that it has good accuracy, especially for methylation calling.</p>
  <p>After all that, if you're still not happy with the accuracy, you can use <a href="https://github.com/broadinstitute/pilon" rel="nofollow noreferrer">Pilon</a> for read correction using the Illumina reads. Pilon is used for correcting a <em>reference</em> sequence, so you'll need to create your haplotype assembly first before using Pilon. Even when using Pilon, I'd still recommend only using the Illumina reads for <a href="https://github.com/broadinstitute/pilon/wiki/Methods-of-Operation#finding-and-fixing-small-indels" rel="nofollow noreferrer">local INDEL correction</a>. Pileup nanopore reads are very accurate for substitutions, especially when considering [lack of] strand bias when evaluating variants.</p>
  <p>Update:</p>
  <p>Given that you're interested in determining haplotypes across the MHC, which is something that performs poorly for short reads even for well-annotated genomes, I think you're better of sticking with reference-free correction, and categorising haplotypes while taking taking this into account. If you just need to be able to distinguish haplotypes, I recommend doing homopolymer compression on the resultant sequences, as hompolymer inaccuracies are the most common error mode for nanopore reads. Even if that doesn't get you all the way to perfect haplotypes, it might get close enough that something like <a href="http://weizhong-lab.ucsd.edu/cdhit_suite/cgi-bin/index.cgi" rel="nofollow noreferrer">CDHIT</a> can be used for binning similar sequences.</p>
  <p>HLA <em>typing</em> in humans can be done from nanopore reads (e.g. see <a href="https://doi.org/10.3389/fgene.2018.00152" rel="nofollow noreferrer">here</a> and <a href="https://doi.org/10.1016/j.humimm.2021.02.005" rel="nofollow noreferrer">here</a>), but don't expect perfect base-level accuracy. What I'm suggesting is something similar to a typing process, but without an established reference, i.e. binning reads based on their similarity to each other.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-26T08:24:41.390Z
Answers.Text: Thank you very much for this amazing response it is very comprehensive. What do you think are the major issues with haplotype assembly when sequencing the MHC as we are interested in this region? While there is a reference genome for this non model organism it is poorly annotated (has not been updated for 10 years) and the fact that we are interested in the MHC region in particular makes things even more complex. Would you have any additional recommendations about error correction in the MHC region due to the high polymorphism?
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Answers.Text: @mk894 I haven't seen papers or preprints that can get good haplotype assembly in HLA only using nanopore reads from one sample. A major problem is that nanopore error correctors can't reliably distinguish sequencing errors from real polymorphisms. This may lead to false correction and mess up downstream assembly.
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Body: <p>I am sequencing long read genomic sequences to assemble MHC region haplotypes in a non model organism using Cas9 Sequencing Kit (SQK-CS9109) using flongle adaptors in the minION from Oxford Nanopore Technologies (ONT). However the error rate in genomic sequences results from ONT has been over 14%.</p>
  <p>I know that there is error correction using Illumina sequencing and I was hoping anyone knows more about how this is accomplished?</p>
  <p>Additionally, there are bioinformatic tools to error correct these errors? Has anyone accomplished diminishing this big error rate using bioinformatic tools?</p>
  
Tags: <nanopore><illumina><errors>
Title: What are some ways to error correct Oxford Nanopore long read sequencing?
ThreadText: # What are some ways to error correct Oxford Nanopore long read sequencing?
  
  (Asked by: username_0 on 2021-06-25)
  
  I am sequencing long read genomic sequences to assemble MHC region haplotypes in a non model organism using Cas9 Sequencing Kit (SQK-CS9109) using flongle adaptors in the minION from Oxford Nanopore Technologies (ONT). However the error rate in genomic sequences results from ONT has been over 14%.
  
  I know that there is error correction using Illumina sequencing and I was hoping anyone knows more about how this is accomplished?
  
  Additionally, there are bioinformatic tools to error correct these errors? Has anyone accomplished diminishing this big error rate using bioinformatic tools?
  
  **Tags:** nanopore, illumina, errors
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-06-25. Score: 4 | Accepted answer
  
  How are you evaluating sequencing error rate? My most recent re-calls of 2017 sequences are demonstrating median single-read accuracy over 96%.
  
  Before considering Illumina, it'd be worth it to do an initial correction using nanopore-only reads. This will make sure that the best results are obtained from the nanopore signal. First, make sure that the reads have been recalled using the latest highest-accuracy basecaller (currently guppy v5.0.11):
  
  ```
  guppy_basecaller -c dna_r9.4.1_450bps_sup.cfg ... # for 9.4.1 reads
  guppy_basecaller -c dna_r10.3_450bps_sup.cfg ... # for 10.3 reads
  
  ```
  
  If you don't have any reference sequences available, reads can be corrected using the other reads by using [canu](https://canu.readthedocs.io/en/latest/quick-start.html):
  
  ```
  canu -correct -p nonModel -d nonModel genomeSize=10m -nanopore reads.fastq
  
  ```
  
  If you *do* have reference sequences available, then reads can be corrected using [megalodon](https://github.com/nanoporetech/megalodon#getting-started). I don't have any experience with megalodon, but have seen a couple of papers demonstrating that it has good accuracy, especially for methylation calling.
  
  After all that, if you're still not happy with the accuracy, you can use [Pilon](https://github.com/broadinstitute/pilon) for read correction using the Illumina reads. Pilon is used for correcting a *reference* sequence, so you'll need to create your haplotype assembly first before using Pilon. Even when using Pilon, I'd still recommend only using the Illumina reads for [local INDEL correction](https://github.com/broadinstitute/pilon/wiki/Methods-of-Operation#finding-and-fixing-small-indels). Pileup nanopore reads are very accurate for substitutions, especially when considering [lack of] strand bias when evaluating variants.
  
  Update:
  
  Given that you're interested in determining haplotypes across the MHC, which is something that performs poorly for short reads even for well-annotated genomes, I think you're better of sticking with reference-free correction, and categorising haplotypes while taking taking this into account. If you just need to be able to distinguish haplotypes, I recommend doing homopolymer compression on the resultant sequences, as hompolymer inaccuracies are the most common error mode for nanopore reads. Even if that doesn't get you all the way to perfect haplotypes, it might get close enough that something like [CDHIT](http://weizhong-lab.ucsd.edu/cdhit_suite/cgi-bin/index.cgi) can be used for binning similar sequences.
  
  HLA *typing* in humans can be done from nanopore reads (e.g. see [here](https://doi.org/10.3389/fgene.2018.00152) and [here](https://doi.org/10.1016/j.humimm.2021.02.005)), but don't expect perfect base-level accuracy. What I'm suggesting is something similar to a typing process, but without an established reference, i.e. binning reads based on their similarity to each other.
  
  > **Comments:**
  > **username_0**: Thank you very much for this amazing response it is very comprehensive. What do you think are the major issues with haplotype assembly when sequencing the MHC as we are interested in this region? While there is a reference genome for this non model organism it is poorly annotated (has not been updated for 10 years) and the fact that we are interested in the MHC region in particular makes things even more complex. Would you have any additional recommendations about error correction in the MHC region due to the high polymorphism?  
  > **username_2**: @username_0 I haven't seen papers or preprints that can get good haplotype assembly in HLA only using nanopore reads from one sample. A major problem is that nanopore error correctors can't reliably distinguish sequencing errors from real polymorphisms. This may lead to false correction and mess up downstream assembly.  
  
  ---
  
  