What it does
MethylDackel (formerly named PileOMeth, which was a temporary name derived due to it using a PILEup to extract METHylation metrics) will process a coordinate-sorted and indexed BAM or CRAM file containing some form of BS-seq alignments and extract per-base methylation metrics from them. MethylDackel requires an indexed fasta file containing the reference genome as well.
By default, MethylDackel will only calculate metrics for Cytosines in a CpG context, but metrics for those in CHG and CHH contexts are supported as well.
Methylation context
MethylDackel groups all Cytosines into one of three sequence contexts: CpG, CHG, and CHH. Here, H is the IUPAC ambiguity code for any nucleotide other than G. If an N is encountered in the reference sequence, then the context will be assigned to CHG or CHH, as appropriate (e.g., CNG would be categorized as in a CHG context and CNC as in a CHH context). If a Cytosine is close enough to the end of a chromosome/contig such that its context can't be inferred, then it is categorized as CHH (e.g., a Cytosine as the last base of a chromosome is considered as being in a CHH context).
Output information
If no methylation can be found, the output will be empty.
Otherwise a variant of bedGraph that's similar to the "coverage" file is produced. In short, each line consists of 6 tab separated columns:
All coordinates are 0-based half open, which conforms to the bedGraph definition. When paired-end reads are aligned, it can often occur that their alignments overlap. In such cases, MethylDackel will not count both reads of the pair in its output, as doing so would lead to incorrect downstream statistical results.
An example of the output is below:
#track type="bedGraph" description="SRR1182519.sorted CpG methylation levels" #1 25115 25116 100 3 0 #1 29336 29337 50 1 1
Note the header line, which starts with "track". The "description" field is used as a label in programs such as IGV. Each of the subsequent lines describe single Cytosines, the 25116th and 29337th base on chromosome 1, respectively. The first position has 3 alignments (or pairs of alignments) indicating methylation and 0 indicating unmethylation (100% methylation) and the second position has 1 alignment each supporting methylation and unmethylation (50% methylation).
Per-CpG/CHG metrics
In many circumstances, it's desireable for metrics from individual Cytosines in a CpG to be merged, producing per-CpG metrics rather than per-Cytosine metrics. This can be accomplished with the Merge per-Cytosine parameter. If this is used, then this output:
#track type="bedGraph" description="SRR1182519.sorted CpG methylation levels" #1 25114 25115 100 2 1 #1 25115 25116 100 3 0
is changed to this:
#track type="bedGraph" description="SRR1182519.sorted merged CpG methylation levels" #1 25114 25116 100 5 1
This also works for CHG-level metrics. If bedGraph files containing per-Cytosine metrics already exist, they can be converted to instead contain per-CpG/CHG metrics with MethylDackel mergeContext.
Methylation bias plotting and correction
In an ideal experiment, we expect that the probability of observing a methylated C is constant across the length of any given read. In practice, however, there are often increases/decreases in observed methylation rate at the ends of reads and/or more global changes. These are termed methylation bias and including such regions in the extracted methylation metrics will result in noisier and less accurate data. For this reason, users are strongly encouraged to make a methylation bias plot.
That command will create a methylation bias (mbias for short) plot for each of the strands for which there are valid alignments. The resulting mbias graphs are in SVG format and can be viewed in most modern web browsers.
If you have paired-end data, both reads in the pair will be shown separately, as is the case above. The program will suggest regions for inclusion ("--OT 2,0,0,98" above) and mark them on the plot, if applicable. The format of this output is described in MethylDackel extract -h. These suggestions should not be accepted blindly; users are strongly encouraged to have a look for themselves and tweak the actual bounds as appropriate. The lines indicate the average methylation percentage at a given position and the shaded regions the 99.9% confidence interval around it. This is useful in gauging how many methylation calls a given position has relative to its neighbors. Note the spike in methylation at the end of read #2 and the corresponding dip at the beginning of read #1. This is common and these regions can be ignored with the suggested trimming bounds. Note also that the numbers refer to the first and last base that should be included during methylation extraction, not the last and first base to ignore!.
Excluding low-coverage regions
If your downstream analysis requires an absolute minimum coverage (here, defined as the number of methylation calls kept after filtering for MAPQ, phred score, etc.), you can use the --minDepth option to achieve this. By default, MethylDackel extract will output all methylation metrics as long as the coverage is at least 1. If you use --minDepth 10, then only sites covered at least 10x will be output. This works in conjunction with the --mergeContext option, above. So if you request per-CpG context output (i.e., with --mergeContext) and --minDepth 10 then only CpGs with a minimum coverage of 10 will be output.
Logit, fraction, and counts only output
The standard output described above can be modified if you supply the --fraction, --counts, or --logit options to MethylDackel extract.
The --fraction option essentially produces the first 4 columns of the standard output described above. The only other difference is that the range of the 4th column is now between 0 and 1, instead of 0 and 100. Instead of producing a file ending simply in .bedGraph, one ending in .meth.bedGraph will instead be produced.
The --counts option produces the first three columns of the standard output followed by a column of total coverage counts. This last column is equivalent to the sum of the 5th and 6th columns of the standard output. The resulting file ends in .counts.bedGraph rather than simply .bedGraph.
The --logit option produces the first three columns of the standard output followed by the logit transformed methylation fraction. The logit transformation is log(Methylation fraction/(1-Methylation fraction)). Note that log uses base e. Logit transformed methylation values range between +/- infinity, rather than [0,1]. The resulting file ends in .logit.bedGraph rather than simply .bedGraph.
Note that these options may be combined with --mergeContext. However, MethylDackel mergeContext can not be used after the fact to combine these.
methylKit-compatible output
methylKit has its own format, which can be produced with the --methylKit option. Merging Cs into CpGs or CHGs is forbidden in this format. Likewise, this option is mutually exclusive with --logit et al.
Excluding likely variant sites
If your samples are not genetically homogenous, it can sometimes be advantageous to exclude likely variant sites from methylation extraction. As an example, since unmethylated Cs are read as Ts, extracting methylation from a position with a C->T mutation will cause incorrect results. In such a case, the opposite strand will have an A rather than a G (in the non-variant case, there would be a G regardless of methylation status). MethylDackel tracks the number of non-Gs on the strand opposite of Cs in the reference sequence. If the fraction of these exceeds the --maxVariantFrac option, then that position will be excluded from output. To exclude cases where the --maxVariantFrac value is exceeded only due to low coverage, the opposite strand must have a depth of coverage of at least --minOppositeDepth. Note that the default value for --minOppositeDepth is 0, indicating that the variant site exclusion process is skipped.
Note that if one additionally specifies --mergeContext, that a given CpG or CHG will be excluded from output if either of its individual Cs would be excluded given the specified --minOppositeDepth and --maxVariantFrac.
MethylDackel is a Free and Open Source Software, see more details on the MethylDackel Website.