All analysis was done using a bisulfite sequnecing data analysis
pipeline DNMTools
developed in the Smith lab at USC.
Mapping reads from bisulfite sequencing: Bisulfite treated reads are
mapped to the genomes with the
abismal
program. Input reads are
filtered by their quality, and adapter sequences in the 3' end of
reads are trimmed. This is done with cutadapt. Uniquely mapped reads
with mismatches/indels below given threshold are retained. For
pair-end reads, if the two mates overlap, the overlapping part of the
mate with lower quality is discarded. After mapping, we use the format
command in dnmtools to merge mates for paired-end reads. We use the
dnmtools uniq command to randomly select one from multiple reads
mapped exactly to the same location. Without random oligos as UMIs,
this is our best indication of PCR duplicates.
Estimating methylation levels: After reads are mapped and filtered,
the dnmtools counts command is used to obtain read coverage and
estimate methylation levels at individual cytosine sites. We count the
number of methylated reads (those containing a C) and the number of
unmethylated reads (those containing a T) at each nucleotide in a
mapped read that corresponds to a cytosine in the reference
genome. The methylation level of that cytosine is estimated as the
ratio of methylated to total reads covering that cytosine. For
cytosines in the symmetric CpG sequence context, reads from the both
strands are collapsed to give a single estimate. Very rarely do the
levels differ between strands (typically only if there has been a
substitution, as in a somatic mutation), and this approach gives a
better estimate.
Bisulfite conversion rate: The bisulfite conversion rate for an
experiment is estimated with the dnmtools bsrate command, which
computes the fraction of successfully converted nucleotides in reads
(those read out as Ts) among all nucleotides in the reads mapped that
map over cytosines in the reference genome. This is done either using
a spike-in (e.g., lambda), the mitochondrial DNA, or the nuclear
genome. In the latter case, only non-CpG sites are used. While this
latter approach can be impacted by non-CpG cytosine methylation, in
practice it never amounts to much.
Identifying hypomethylated regions (HMRs): In most mammalian cells,
the majority of the genome has high methylation, and regions of low
methylation are typically the interesting features. (This seems to be
true for essentially all healthy differentiated cell types, but not
cells of very early embryogenesis, various germ cells and precursors,
and placental lineage cells.) These are valleys of low methylation are
called hypomethylated regions (HMR) for historical reasons. To
identify the HMRs, we use the dnmtools hmr command, which uses a
statistical model that accounts for both the methylation level
fluctations and the varying amounts of data available at each CpG
site.
Identifying hypermethylated regions (HyperMRs) and mosaic methylation:
Hyper-methylated regions (which
we call HyperMRs) are of interest in plant methylomes, invertebrate
methylomes and other methylomes showing "mosaic methylation"
pattern. We identify HyperMRs with the dnmtools hypermr command for
those samples showing "mosaic methylation" pattern.
Partially methylated domains: Partially methylated domains are large
genomic regions showing partial methylation observed in immortalized
cell lines and cancerous cells. The pmd program is used to identify
PMDs.
Allele-specific methylation: Allele-Specific methylated regions refers
to regions where the parental allele is differentially methylated
compared to the maternal allele. The program allelic is used to compute
allele-specific methylation score can be computed for each CpG site by
testing the linkage between methylation status of adjacent reads, and
the program amrfinder is used to identify regions with allele-specific
methylation.
For more detailed description of the methods of each step, please
refer to the DNMTools documentation.
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