3-dimensional genome. PartII.�Chromatin features detection.

Based on materials by:

Anna Kononkova

3D organisation of the chromatin revealed by Hi-C

Genome-wide Hi-C interaction map shows intrachromosomal (same as cis-) and interchromosomal (trans-) interactions.

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Chromosome territories

• At the highest-level of spatial organization, trans-interactions are rare.
• Individual chromosomes occupy distinct territories within the nucleus.

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Bonev et al. Nature Reviews 2016

How to deal with interchromosomal contacts?

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Cis-to-trans ratio: m1/m2

There is no strong definition of cis-to-trans ratio. Variations of this one can be calculated in different ways: as average across all cis- divided by the average of all trans-contacts; as feature for genomic bin (ICF); as value for each pair of chromosome.

ICF_j=mean(cis)/mean(trans) – for bin_j

bin_j

Intrachromosomal interactions: TADs

• TADs – topological associated domains: loci belonging to one TAD interact with each other more often than with loci from neighboring TADs

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Bonev et al. Nature Reviews 2016

TADs detection

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Какие ТАДы правильные?

TADs detection

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Armatus allows the wide range of TADs sizes.

Matryoshka is a widely used Armatus-based tool for hierarchical TADs calling

HiCExplorer covers the most part of genome, avoiding strange gaps

Insulation score – a measure of local chromatin density

Convenient implementation: Cooltools

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Directionality index

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A – upstream

B - downstream

E=(A+B)/2

+HMM on the top -> TADs borders

TADs detection

The number and size of TADs depend on:

1. parameters provided by user (gamma in Armatus, window size in IS)
2. resolution
3. organism
4. data preprocessing (linear interpolation, high and zero value restriction)

TADs are hierarchical -> the better the resolution, the smaller TADs we can obtain

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Intrachromosomal interactions: compartments

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Bonev et al. Nature Reviews 2016

Compartment detection:

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Saddle plot

PCA application to the matrix:

the sign of PC1 defines the compartment membership.

Compartment strength

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How to estimate difference in compartments?

More accurate method:

for each bin, the average normalized frequency of interactions with bins belonging to the same compartment was divided by the average normalized contacts  with bins from the other compartment.

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mean of ”reds” devided by mean of “greens”

Subcompartment calling

• Classical approach:

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detection via cis-contacts

(in fact, TADs clusterization)

"resolution enhancement" using neural network approaches, such as autoencoder

Calder: https://doi.org/10.1038/s41467-021-22666-3

Sniper:

https://doi.org/10.1038/

s41467-019-12954-4

Inspectro:

https://github.com/open2c/

inspectro

requires high resolution

(~ 5 billion contacts for human data,

current usual dataset is nearly 10 time less)

Calder

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The problem: compartments are not just TADs interactions. Moreover, in regions with high rate of extrusion TADs and compartmental structure oppose each other. Cis-approach can be not applicable in this case.

Compartmental and extrusion TADs

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compartmental

extrusion

Intrachromosomal interactions: loops

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 https://doi.org/10.1073/pnas.1701291114

Loop-callers: cooltools (python implementation of HICCUPS), MUSTACHE and others

Loop callers

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Algorithms usually consider 2 features:

• whole-genome enrichment: selection of candidate interactions, which is based on the model distribution of contacts (often negative binomial)
• local enrichment: compare selected contact prominence with neighbouring ones )

Another approaches (Mustache) rely on Gaussian smoothing. The algorithm removes noise and details and keeps only the most bright features like loops

+ Mustache, SIP, etc

Personal preference (probably, not the best): cooltools, chromosight

Significant contacts

Apart from loops, there is one more similar, but another type of contacts – significant interactions on Hi-C map.

These can be promoter-promoter, promoter-enhancer or polycomb interactions.

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Loop

Significant contact

Tool for significant contacts detection: FitHiC2

https://github.com/ay-lab/fithic

FitHiC application example�after additional filtration on specific histone modification: fithic+H3K27me3

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Without additional filtration result usually seems to be more random

Resolution

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Today’s data resolution is often not enough for feature detection

Fires: frequently interacting regions

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Scale of interactions: ±200kb for human genome

 https://yunliweb.its.unc.edu/FIREcaller

Firecaller:

from doi: 10.1016/j.csbj.2020.12.026

How to create beautiful average plots?

• By hand ☺
• Coolpappy (https://github.com/open2c/coolpuppy)

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Bad and good quality example

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Same resolution, 40 kb

Rabl: centromer and telomer interactions

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An example of telomer-centromer interactions

in budding yeast

This is a model conformation for

centromer-centromer interactions

Why subcompartments can be detected from trans-interactions

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Trans-interactions

reveal compartment

structure

Practise: cooltools

• https://cooltools.readthedocs.io/en/latest/

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