Detecting and comparing genomic compartments Cyril Kurylo , Sylvain - - PowerPoint PPT Presentation

Cyril Kurylo, Sylvain Foissac, Matthias Zytnicki

Detecting and comparing genomic compartments

Genomic structures

Doğan & Liu, 2018

Chromosome territories

Segregation of untangled chromosomes

A/B compartments

Impact on gene expression

Topologically Associating Domains

Co-regulation domains

Loops

Interaction of regulatory elements

Chromatin

Compaction of DNA Accessibility to transcription

DNA

Genetic information 1

Analysing compartmentalization

Motivations

Compartment changes linked to transcriptional changes Large White neonatal mortality due to poor fetal development 2

Using Hi-C to expose compartments

Rao et al., 2014

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Using Hi-C to expose compartments

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Using Hi-C to expose compartments

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Using Hi-C to expose compartments

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Using Hi-C to expose compartments

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Using Hi-C to expose compartments

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Using Hi-C to expose compartments

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Analysing compartmentalization

Ambitions

Computationally detect compartments Using replicates Providing a confidence measure Statistical comparison across conditions

Data

2 conditions — 90 and 110 days of development 3 Hi-C replicates per condition 6

Hi-C DOC: Detection Of Compartments with replicates

Matrix normalization Compartment detection

Cyclic Loess Knight- Ruiz Distance Loess Regression Constrained k-means

Comparison

Concordance Measure

available at github.com/mzytnicki/HiCDOC 7

P-value

Correctly normalizing Hi-C matrices

Technical biases

Sequencing depth Restriction enzyme Cross-linking conditions Experiment quality

Cyclic Loess Knight- Ruiz Distance Loess

Cyclic Loess multiHiCcompare

Genomic distance in bins Difference Difference Genomic distance in bins

8

Matrix normalization Compartment detection Comparison

Correctly normalizing Hi-C matrices

Biological biases

GC content Restriction site distribution Repeated sequences

Double stochastic transformation Knight-Ruiz 8 3 6 9 5 8 3 6 9 5 1 1 1 1 1 1 1 1 1 1

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Cyclic Loess Knight- Ruiz Distance Loess

Matrix normalization Compartment detection Comparison

Correctly normalizing Hi-C matrices

Distance effect

Proximity between regions

Loess regression

Distance Interaction

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Cyclic Loess Knight- Ruiz Distance Loess

Matrix normalization Compartment detection Comparison

Detecting compartments

B compartment A compartment Constrained k-means K = 2 Predicted compartments 11

Matrix normalization Compartment detection Comparison

Comparing compartmentalization between conditions

Concordance at 90 days Concordance at 110 days B compartment A compartment B compartment A compartment

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Matrix normalization Compartment detection Comparison

Distribution of the differences when the compartment doesn’t change Median differences for predicted compartment changes (with constrained k-means) 2.5% 2.5%

Comparing compartmentalization between conditions

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Matrix normalization Compartment detection Comparison P-value

Probability of observing a difference between concordances as extreme or more extreme when the compartment doesn’t change Differences between concordances

90 days 110 days

Conclusion and perspectives

Ambitions achieved

Computationally detect compartments Using replicates Providing a quantitative measure Statistical comparison across conditions

Preliminary Results

Predicted compartment changes Ongoing statistical analysis

Perspectives

Analyse genes in switching regions Publish method and results for our data 14

Thank You

github.com/mzytnicki/HiCDOC

Concordance comparison

Gene density

90 days 110 days

Gene density (# genes / kb) Compartment

PCA detection

Lieberman-Aiden et al., 2009