  Motivation: Bisulphite sequencing enables the detection of cytosine
methylation. The sequence of the methylation states of cytosines on any given
read forms a methylation pattern that carries substantially more information
than merely studying the average methylation level at individual positions. In
order to understand better the complexity of DNA methylation landscapes in
biological samples, it is important to study the diversity of these methylation
patterns. However, the accurate quantification of methylation patterns is
subject to sequencing errors and spurious signals due to incomplete bisulphite
conversion of cytosines. Results: A statistical model is developed which
accounts for the distribution of DNA methylation patterns at any given locus.
The model incorporates the effects of sequencing errors and spurious reads, and
enables estimation of the true underlying distribution of methylation patterns.
Conclusions: Calculation of the estimated distribution over methylation
patterns is implemented in the R Bioconductor package MPFE. Source code and
documentation of the package are also available for download at
http://bioconductor.org/packages/3.0/bioc/html/MPFE.html.
