SssI

SssI models expected MBD pulldown alignments from SssI-treated DNA to generate calculated control data that improve DNA methylation prediction from MBD-seq reads using BayMeth.

Key Features:

• Modeling Pulldown Alignments: Constructs a model of expected MBD pulldown reads based on DNA treated with SssI, which methylates CpGs.
• Calculated SssI Control Data: Produces simulated control data that can substitute for observed SssI-treated control experiments in downstream analysis.
• Integration with BayMeth: Substitutes calculated SssI control data into the BayMeth algorithm to estimate absolute methylation levels.
• Adaptability: Allows adjustment of parameters such as average fragment length to accommodate external datasets and different experimental conditions.
• Validation Against Observed Controls: Performance was evaluated by comparison to real observed SssI control datasets and found to be superior or comparable in various contexts.
• Reduction of Experimental Burden: Reduces the need for additional SssI-treated control experiments by providing modeled control data for methylation estimation.

Scientific Applications:

• Enhanced Methylation Prediction: Improves precision of DNA methylation estimates from MBD-seq/MBD pulldown data when used with BayMeth.
• Control Substitution for Epigenetic Studies: Enables use of calculated SssI control data in studies requiring absolute methylation level estimation, reducing reliance on laboratory-generated controls.

Methodology:

Constructs a model of expected MBD pulldown alignments from SssI-treated DNA to calculate simulated SssI control data, substitutes these calculated controls into BayMeth for methylation estimation, and adjusts parameters such as average fragment length; validation was performed by comparison to observed SssI control datasets.