SeqMonk Tools for Methylation Analysis Overview

 
SeqMonk tools for
methylation analysis
 
Simon Andrews
simon.andrews@babraham.ac.uk
@simon_Andrews
2016-11
 
1
 
SeqMonk
 
2
 
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
 
3
 
Raw Data
 
Red  = Meth
Blue = Unmeth
 
4
 
Basic Movement Controls
 
Move left right
Drag bottom scrollbar
Mouse scroll wheel
Left/Right arrows
Zoom In
Drag a box and release
Up arrow
Zoom Out
Right mouse button
Down arrow
Find a feature
Edit > Find Feature
Control+F
Change chromosome
Edit > Goto Position
Drag a box in the genome view
 
5
Genome View
Data View
Chromosome View
Quantitations
Reads / Calls
Annotation
 
Raw Data Display
 
View > Data Track Display
 
6
 
Quantitation Model
 
Probe = Location to make a measurement
 
ProbeSet = Collection of probes
 
Quantitation associates a value with each
probe for each data set.
 
Define Probes > Quantitate Probes > Visualise
 
7
 
Probe Generation
 
8
 
Targeting Measurement
Features
 
Data > Define Probes > Feature Probe Generator
 
9
 
Targeting Measurement
Fixed Windows
 
Data > Define Probes > Running Window Generator
 
10
 
Targeting Measurement
Fixed number of calls
 
Data > Define Probes > Even Coverage Probe Generator
 
11
 
Targeting Measurement
Fixed number of call positions
 
Data > Define Probes > Read Position Probe Generator
 
12
 
Quantitation
 
13
 
Methylation Measurement
Simple percentage of all calls
 
Data > Quantitate Existing Probes > Difference Quantitation
 
14
 
Methylation Measurement
More complex corrected measure
 
Data > Quantitation Pipelines > Bisulphite methylation over features
 
15
 
Visualisation of quantitated
methylation
 
View > Data Track Display
 
View > Set Data Zoom Level
 
16
 
Distributions
 
Plots > Probe value histogram
 
Plots > Cumulative Distribution Plot
 
17
 
Plots > Beanplot
 
Comparisons
 
Plots > Scatter plot
 
18
 
Plot is interactive – mouse over a point for information
Double click on a point to move there in the chromosome view
 
Trend Plots
 
Plots > Quantitation Trend Plot
 
19
 
Clustering
 
Plots > Hierarchical Clusters
 
Correlation based (per probe normalised)
 
Euclidean
 
20
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Explore the features and functionalities of SeqMonk for methylation analysis, including data model, raw data visualization, movement controls, quantitation models, probe generation, and targeting measurement methods.

  • Methylation analysis
  • SeqMonk tools
  • Data model
  • Raw data display
  • Probe generation

Uploaded on Nov 25, 2024 | 0 Views


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  1. SeqMonk tools for methylation analysis Simon Andrews simon.andrews@babraham.ac.uk @simon_Andrews 2016-11 1

  2. SeqMonk 2

  3. 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 3

  4. Raw Data Red = Meth Blue = Unmeth 4

  5. Basic Movement Controls Move left right Drag bottom scrollbar Mouse scroll wheel Left/Right arrows Zoom In Drag a box and release Up arrow Zoom Out Right mouse button Down arrow Find a feature Edit > Find Feature Control+F Change chromosome Edit > Goto Position Drag a box in the genome view Data View Genome View Annotation Reads / Calls Quantitations Chromosome View 5

  6. Raw Data Display View > Data Track Display 6

  7. Quantitation Model Probe = Location to make a measurement ProbeSet = Collection of probes Quantitation associates a value with each probe for each data set. Define Probes > Quantitate Probes > Visualise 7

  8. Probe Generation 8

  9. Targeting Measurement Features Data > Define Probes > Feature Probe Generator 9

  10. Targeting Measurement Fixed Windows Data > Define Probes > Running Window Generator 10

  11. Targeting Measurement Fixed number of calls Data > Define Probes > Even Coverage Probe Generator 11

  12. Targeting Measurement Fixed number of call positions Data > Define Probes > Read Position Probe Generator 12

  13. Quantitation 13

  14. Methylation Measurement Simple percentage of all calls Data > Quantitate Existing Probes > Difference Quantitation 14

  15. Methylation Measurement More complex corrected measure Data > Quantitation Pipelines > Bisulphite methylation over features 15

  16. Visualisation of quantitated methylation View > Data Track Display View > Set Data Zoom Level 16

  17. Distributions Plots > Probe value histogram Plots > Cumulative Distribution Plot Plots > Beanplot 17

  18. Comparisons Plots > Scatter plot Plot is interactive mouse over a point for information Double click on a point to move there in the chromosome view 18

  19. Trend Plots Plots > Quantitation Trend Plot 19

  20. Clustering Correlation based (per probe normalised) Euclidean Plots > Hierarchical Clusters 20

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