SeqMonk tools for methylation analysis

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Quality Control & Nascent Sequencing

Presentation transcript:

SeqMonk tools for methylation analysis Simon Andrews simon.andrews@babraham.ac.uk @simon_andrews

SeqMonk

SeqMonk Data Model Conventional data (ChIP-Seq, RNA-Seq etc) Data is reads (BAM files etc) Strand indicates genomic strand BS-Seq and related data Data is methylation calls All ‘reads’ are 1bp in length Strand indicates meth state (+=meth -=unmeth) Original strand comes from the imported file

Raw Data Red = Meth Blue = Unmeth

Raw Data Display View > Data Track Display

Targeting Measurement Features Data > Define Probes > Feature Probe Generator

Targeting Measurement Fixed Windows Data > Define Probes > Running Window Generator

Targeting Measurement Fixed number of calls Data > Define Probes > Even Coverage Probe Generator

Targeting Measurement Fixed number of call positions Data > Define Probes > Read Position Probe Generator

Methylation Measurement Simple percentage of all calls Data > Quantitate Existing Probes > Difference Quantitation

Methylation Measurement More complex corrected measure Data > Quantitation Pipelines > Bisulphite methylation over features

Visualisation of quantitated methylation View > Data Track Display View > Set Data Zoom Level

Distributions Plots > Probe value histogram Plots > Cumulative Distribution Plot

Comparisons Plots > Scatter plot

Trend Plots Plots > Quantitation Trend Plot

Clustering Plots > Hierarchical Clusters Correlation based Euclidean Plots > Hierarchical Clusters