You are right that a single molecule in a single position is either methylated or not methylated. However:
First, assuming your organism of interest is diploid (or of higher ploidy) one of the chromosomes could be methylated, the other not. That would give you a level of 0.5 and can be found in imprinted regions (where the paternally inherited chromosome is ...
Methylation levels have high local correlation, so Fisher's method would be problematic. Having said that, you have no reason to use Fisher's method after a paired t-test. A paired t-test will give you a single p-value per gene, which is what you want. Do be sure to only include CpG with some minimal coverage in both group.
EPIC data can be processed in the same manner as the previous iteration of methylation array data from Illumina (450k). This means that starting with .idat files, normalization should be performed (for example, via the minfi package). A recent paper from the creators of minfi is particularly helpful because it makes clear that normalized EPIC data from their ...
If I'm understanding you correctly, by "types of DNA methylation" you mean "nucleotide contexts where DNA methylation occurs".
This is going to be a function of the methyltransferase proteins involved in the methylation process, and this largely depends on the organism, and even cell type being studied.
Generally, what has been observed is:
The phenomenon ...
Not really an answer but an extended comment... and most likely something you don't like to hear
I guess by technical replicates, it means taking the same biological sample and making 2 methylation libraries. If this is used as replicates in deseq2 or edger, the variance you are estimating is the technical variation that comes with preparing the library, ...
Permutation as suggested by @StupidWolf's comment is essential to understand what's going on. If permutation makes this pattern go away, then you have a problem with your model specification, there's something uncorrected.
If your data are weird, well, that's just how they are. But this argues to me that something else is going on confounding your ...
The best solution I could find was to use the \shaderegion function on all the sequences so it ended up looking something like this:
Then I cast the rest of shading similarity to grey so the CG pairs were more easily distinguishable.
Two examples one with \...
The strand the bismark reports is related to the strand from which the read originated, not necessarily how it's aligned. So, alignments on the + strand shouldn't have calls overlapping those on the - strand, since you can't have a C in the same place on the same strand. One should often see Z/z next to each other on opposite strand, like in your example, ...
You'd be best off by starting with -rfg and -rdg as is and reverting bismark's change of --score-min back to the default for bowtie2. That alone will allow for much longer indels. If that still doesn't suffice, then I'd play around more with --score-min before messing with the gap open/extend penalties. If you do need to play with those, then increase the ...
I guess QSEA should be an obvious answer:
QSEA (quantitative sequencing enrichment analysis) was developed as
the successor of the MEDIPS package for analyzing data derived from
methylated DNA immunoprecipitation (MeDIP) experiments followed by
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:
Coordinates of a C/CpG with coverage
Number of reads supporting methylation at ...
Methylation can be cell-specific, which makes it difficult to evaluate accuracy on a bulk-cell level (even within the same tissue). How can you tell that the differences you're seeing are due to platform differences, or due to biological variation?
I find that adding more haystacks doesn't help much in working out the truth of a dataset. If you want to ...
Assuming that it is the postprocessing script calculate_methylation_frequency.py that you are using, user Wouter de Coster's comment is correct that the methylation event is filtered out at the frequency estimation stage if it has a low LLR. It will have a low LLR if it is considered not confident enough to report. This means not only LLR > T * N, but ...
I would assess directionality and accuracy of prediction by 1) WGBS predicting ONT and then 2) ONT predicting WGBS.
Firstly, I would use deep learning (or machine learning) and train WGBS against ONT, parameterise and then test. Then conversely train ONT against WGBS, parameterise and then test. The approach with highest accuracy of prediction (using the '...
In my experience, the most common approach to find DMR is to use the DSS R package to call and merge DML into regions.
DMC/DML (Differential Methylation Cytosine/Loci) is often calculated by comparing the counts of methylated and unmethylated cytosine at one position between samples.
Steps are to first call DMLs with the callDML() function. Then call DMRs ...
I'm not certain about your piRNA approach, maybe others will be able to help with that question.
As for multi-mapped reads, from Bismark's documentation you can find the --ambig-bam flag:
For reads that have multiple alignments a random alignment is written out to a special file ending in .ambiguous.bam. The alignments are in Bowtie2 format and do not any ...
You can get specific sequences efficiently using the following packages:
Methylation_data <- tribble(
You may run edgeR for methylation analysis without replicates (https://f1000research.com/articles/6-2055). But I also recommend you to have a look at the R DSS package (http://bioconductor.org/packages/release/bioc/html/DSS.html). It has a smoothing step which allows to modelize the biological variability by taking information from neighboring sites.
The error message is pretty clear: R requires unique row names on data frames and some of the "values" that you are trying to set as row names are non-unique.
You will need to provide a vector i) of length the same as your row number, ii) that is composed of non-unique values.
It's likely that they accomplished this by using the UCSC Table Browser to:
Find the loci of the two isoforms you mention
Finding CpG islands (Group = Regulation, Track = CpG Islands) near those transcription start sites
Design primers using Applied Biosystem's software
Alternatively, they list the exact primers used in Table 1.
They don't detail exactly ...
After a more extensive search, I found a useful answer that I'll share in case anyone else wonders how to do this. I'm using MethylKit package for differential methylation expression and it is quite easy to integrate with Genomic Ranges and rtracklayer in R to extract particular element methylation differences:
element.of.interest <- import.bed("FILENAME....
What is typically done in methylation analysis is to assess the islands of methylations.
Check this workflow, in the section linked it uses some predefined islands for instance. I am no expert on this area but you could asses if the islands or certain regions are more methylated than expected.