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.
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.