Feature annotation Compartments, TADs and peaks Compartments Recap - - PowerPoint PPT Presentation

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Feature annotation Compartments, TADs and peaks Compartments Recap - - PowerPoint PPT Presentation

Mar 02, 2023 292 likes •557 views

Feature annotation Compartments, TADs and peaks Compartments Recap Learning Identify compartments in HiC matrix Compartments in plants objectives Compartments - A compartment: - active regions (euchromatin) - B compartment:

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SLIDE 1

Feature annotation

Compartments, TADs and peaks

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SLIDE 2

Compartments

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SLIDE 3

Recap

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SLIDE 4

Learning

  • bjectives
  • Identify compartments in HiC matrix
  • Compartments in plants
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SLIDE 5
  • A compartment:
  • active regions (euchromatin)
  • B compartment:
  • inactive regions (heterochromatin)
  • Bin sizes 100kb - 1MB

Compartments

  • PC1
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SLIDE 6
  • Principal Component Analysis
  • The first eigenvector will give the compartmentalization profile
  • Positive values indicate one compartment, negative values indicate other

compartment

  • How to tell if A or B?

PCA

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SLIDE 7

Correlation with other data

  • Show first component together

with other epigenomic tracks:

  • Gene content / TE density
  • Histone modifications:
  • A: H3K27Ac, H3K4me3
  • B: H3K27me3, H3K9me3
  • Transcription
  • RNA seq tracks
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SLIDE 8

Practical

  • Identify compartments with HiCexplorer
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SLIDE 9

TADs

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SLIDE 10

Recap

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SLIDE 11

Learning

  • bjectives
  • Identify TADs with HiCExplorer
  • Discuss challenges in TAD calling
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SLIDE 12

TADs

  • TADs are regions with elevated self interaction frequencies
  • TADs might act as an insulated genomic region that constrains regulatory

interactions

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SLIDE 13

TAD calling in HiCexplorer

1. Transform matrix to Z score (subtract mean contact frequency at each distance): make bins more comparable 2. For each bin, calculate average contacts between w upstream and downstrem bins. 3. Repeat for different values of w, and then take the average. 4. Local minima should be boundaries! 5. To double check: compare the distributions of upstream and downstream “diamonds”.

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SLIDE 14

TAD calling challenges

  • What is a TAD?
  • TAD-like patterns are often hierarchical and overlapping (subTADs, gene mini

domains)

  • There are aprox. 22 different TAD calling algorithms
  • Current approaches often not reproducible
  • Sensible to normalization, bin size, sequencing depth
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SLIDE 15

TAD calling algorithms

  • Linear score
  • Directionality Index
  • Insulation score
  • TopDom
  • HiCExplorer
  • Statistical models
  • TADbit
  • Clustering
  • ClusterTAD
  • Network analysis
  • spectral
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SLIDE 16

Practical:

  • TAD calling with HiCexplorer
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SLIDE 17

Interaction peaks

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SLIDE 18

Recap

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SLIDE 19

Learning

  • bjectives
  • Identify peaks with HiCCUPS
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SLIDE 20

Peaks and biological features

  • Enhancer-promoter interactions
  • CTCF-CTCF and cohesin binding sites
  • Polycomb bodies
  • KNOT region (Arabidopsis)
  • Gene - Gene interactions (Maize)
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SLIDE 21

HICCUPS

  • Local enrichment over four

backgrounds:

  • Donut
  • Horizontal
  • Vertical
  • Lower left corner
  • Compare bin contact frequency

with average contact frequency

  • f each background
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SLIDE 22

Aggregate Peak Analysis

  • Analyse the average interaction profile for all peaks
  • Can be used as QC
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SLIDE 23

Peak calling algorithms

  • Global enrichment
  • Fit-HiC
  • HOMER
  • HICCUPS
  • Local enrichment
  • HICCUPS
  • HiCExplorer
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SLIDE 24

Practical:

  • Peak calling with HiCexplorer
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SLIDE 25

Resources:

  • HiCExplorer: https://hicexplorer.readthedocs.io/
  • HiGlass: http://higlass.io/
  • Deeptools: https://deeptools.readthedocs.io/en/develop/
  • Juicer: https://github.com/aidenlab/juicer/wiki
  • Collection of hic tools: https://github.com/mdozmorov/HiC_tools