<p>I assume you don't want to spend 1-2 years developing your own method based on nanopore data, so the path forward is to convert the output into a format used by standard DMR calling programs. There's no standard format for that, annoyingly, but the general things you'll need are:</p>

<ol>
<li>Coordinates of a C/CpG with coverage</li>
<li>Number of reads supporting methylation at that site.</li>
<li>Number of reads not supporting methylation at that site.</li>
</ol>

<p>Typically you would do some filtering before/after making such a table so you only have decent quality calls (that would translate into some reasonable log likelihood for your data) and possibly only sites with decent (5x, 10x, 30x, etc.) coverage. You'd then feed that for all of the replicates in all of your groups into programs like <a href="https://bioconductor.org/packages/release/bioc/html/bsseq.html" rel="nofollow noreferrer">bsseq</a>, <a href="http://bioconductor.org/packages/release/bioc/html/DSS.html" rel="nofollow noreferrer">DSS</a> or <a href="https://www.bioinf.uni-leipzig.de/Software/metilene/" rel="nofollow noreferrer">Metilene</a> (to not so randomly select 3 examples).</p>

<p>If there aren't DMR finding programs already developed specifically for nanopore data I expect someone is developing it. The technology has a higher error rate, but given that bisulfite conversion as required in Illumina data also leads to a higher error rate that's not necessarily a huge issue if the methylation calling itself is reasonably accurate and the depth is high enough. Note that you probably need 10X coverage as a bare minimum unless your effect size is very very large.</p>

CC BY-SA 4.0
2018-11-12T15:03:15.330Z
2018-11-12T15:03:15.330Z
Devon Ryan
<p>I'm trying to get some pointers on where to start on the analysis of my dataset. First, some basic info:
Background:
We performed Nanopore sequencing of two poolseq samples called UU and DD referring to damage vs undamaged. We were able to obtain approximately 3x coverage (this is currently more of a proof of concept than a hardcore association job, so the coverage is unfortunate but worth pursuing). Between these samples, we know that one is going to have different methylation patterns in response to a phenotypic trait response, but we don't know what regions along the genome are going to be differently methylated. The goal is to compare what's methylated (as in higher or lower levels of methylation) between the two samples, knowing there ought to be a lot that cancels out and come up with ideally a short list of significantly different regions we can associate to the plastic response.</p>

<p>Output of initial methylation calling data:
Using the raw signal level data against the reference genome and sorted, mapped BAM files the Nanopolish software was used to call methylation. This produces a dataset with the following headers:
1. Chromosome, 2. +/- Strand 3. Start vs. 4. End of the CG dinucleotide (the difference between these is 1 or two bases) 5. The read ID the dinucleotide came from 6. the log likelihood ratio calculated from the model embedded in the methylation calling process using 7. log likelihood that it's methylated and 8. log likelihood that it's unmethylated. 8. number of calling strands (always appears to be 1) 9. number of motifs (don't know about this) and finally 10. the sequence surrounding the dinucleotide, which is always short but variable in length.</p>

<p>I can follow the basic interpretation of the log-likelihood... if it's a positive number, there is statistical support that it's methylated. This would probably be about as much as we need, short of a p-value, if we were looking for a specific region, but we are trying to analyze the differences in de/methylation across the entire methylome. How would you suggest using the statistic values provided in our dataset to compare and visualize the significant different modifications of the dinucleotides between strands/samples?</p>

<r><methylation><genetics>
Continuing analysis based on a log-likelihood ratio? (Comparing methylation between two PoolSeq phenotypes)
# Continuing analysis based on a log-likelihood ratio? (Comparing methylation between two PoolSeq phenotypes)

(Asked by: username_0 on 2018-11-11)

I'm trying to get some pointers on where to start on the analysis of my dataset. First, some basic info:
Background:
We performed Nanopore sequencing of two poolseq samples called UU and DD referring to damage vs undamaged. We were able to obtain approximately 3x coverage (this is currently more of a proof of concept than a hardcore association job, so the coverage is unfortunate but worth pursuing). Between these samples, we know that one is going to have different methylation patterns in response to a phenotypic trait response, but we don't know what regions along the genome are going to be differently methylated. The goal is to compare what's methylated (as in higher or lower levels of methylation) between the two samples, knowing there ought to be a lot that cancels out and come up with ideally a short list of significantly different regions we can associate to the plastic response.

Output of initial methylation calling data:
Using the raw signal level data against the reference genome and sorted, mapped BAM files the Nanopolish software was used to call methylation. This produces a dataset with the following headers:
1. Chromosome, 2. +/- Strand 3. Start vs. 4. End of the CG dinucleotide (the difference between these is 1 or two bases) 5. The read ID the dinucleotide came from 6. the log likelihood ratio calculated from the model embedded in the methylation calling process using 7. log likelihood that it's methylated and 8. log likelihood that it's unmethylated. 8. number of calling strands (always appears to be 1) 9. number of motifs (don't know about this) and finally 10. the sequence surrounding the dinucleotide, which is always short but variable in length.

I can follow the basic interpretation of the log-likelihood... if it's a positive number, there is statistical support that it's methylated. This would probably be about as much as we need, short of a p-value, if we were looking for a specific region, but we are trying to analyze the differences in de/methylation across the entire methylome. How would you suggest using the statistic values provided in our dataset to compare and visualize the significant different modifications of the dinucleotides between strands/samples?

**Tags:** r, methylation, genetics

---

## 1 answers

### Answer by username_1 on 2018-11-12. Score: 2

I assume you don't want to spend 1-2 years developing your own method based on nanopore data, so the path forward is to convert the output into a format used by standard DMR calling programs. There's no standard format for that, annoyingly, but the general things you'll need are:

1. Coordinates of a C/CpG with coverage
2. Number of reads supporting methylation at that site.
3. Number of reads not supporting methylation at that site.

Typically you would do some filtering before/after making such a table so you only have decent quality calls (that would translate into some reasonable log likelihood for your data) and possibly only sites with decent (5x, 10x, 30x, etc.) coverage. You'd then feed that for all of the replicates in all of your groups into programs like [bsseq](https://bioconductor.org/packages/release/bioc/html/bsseq.html), [DSS](http://bioconductor.org/packages/release/bioc/html/DSS.html) or [Metilene](https://www.bioinf.uni-leipzig.de/Software/metilene/) (to not so randomly select 3 examples).

If there aren't DMR finding programs already developed specifically for nanopore data I expect someone is developing it. The technology has a higher error rate, but given that bisulfite conversion as required in Illumina data also leads to a higher error rate that's not necessarily a huge issue if the methylation calling itself is reasonably accurate and the depth is high enough. Note that you probably need 10X coverage as a bare minimum unless your effect size is very very large.


---

