This document discusses a deep learning model called ORCA that can predict chromatin structure from DNA sequence. The model uses a neural network with an encoder to extract features from sequence and a decoder to predict Hi-C matrices. It was trained on Hi-C data from multiple cell types and can predict interactions between regions at various resolutions. The model accurately captures features like CTCF-mediated loops and can predict effects of structural variants on chromatin structure. It allows for in silico mutagenesis to study how mutations may alter 3D genome organization.

1. Can deep learning learn chromatin structure from sequence?
Nathalie Vialaneix
nathalie.vialaneix@inrae.fr
http://www.nathalievialaneix.eu
GT Chrocogen
January 20th, 2023

2. What is this chrocotalk about?
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 2

3. Outline
Hi-C data
Deep NN model
Data and training methodology
Results
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 3

4. 3D organization of the genome
courtesy of Sylvain Foissac
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 4

5. Hi-C protocole
again courtesy of Sylvain Foissac
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 5

6. Hi-C matrices
also courtesy of Sylvain Foissac
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 6

7. Hi-C matrices
also courtesy of Sylvain Foissac
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 6

8. Page de pub : [Neuvial et al., 2023]
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 7

9. Question tackled by the article
Can we predict a Hi-C matrix from the DNA sequence?
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 8

10. Understanding binning and resolution
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 9

11. Outline
Hi-C data
Deep NN model
Data and training methodology
Results
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 10

12. The model
availability:
https://github.com/
jzhoulab/orca (trained
model and model to
train... all well
documented)
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 11

13. Two main parts: encoder/decoder
Encoder: from sequence to features
↓
↓
Decoder: from features to Hi-C matrix
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 12

14. Three (multi-resolution) modules trained sequentially
Orca-1Mb: used to predict one 1Mb
region at resolution 4Kb (bin size)a
a
hence, 250 × 250 Hi-C matrices
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 13

15. Three (multi-resolution) modules trained sequentially
Orca-1Mb: used to predict one 1Mb
region at resolution 4Kb (bin size)a
Orca-32Mb (multi-resolution): trained
using results from Orca-1Mb and used to
predict one region (size 1Mb, 2Mb, 4Mb,
. . . , 32Mb) at resolution 8kb, 16kb, . . . ,
128kba
a
hence, 250 × 250 Hi-C matrices
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 13

16. Three (multi-resolution) modules trained sequentially
Orca-1Mb: used to predict one 1Mb
region at resolution 4Kb (bin size)a
Orca-32Mb (multi-resolution): trained
using results from Orca-1Mb and used to
predict one region (size 1Mb, 2Mb, 4Mb,
. . . , 32Mb) at resolution 8kb, 16kb, . . . ,
128kba
Orca-256Mb (multi-resolution): trained
using results from Orca-32Mb and used to
predict one region (size 32Mb, 64Mb,
128Mb, 256Mb) at resolution 128kb,
256kb, 512kb, 1024kba
a
hence, 250 × 250 Hi-C matrices
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 13

17. Other features
▶ all Encoder blocks have a linear subblock and non linear subblock with two
convolutional layer each + ReLU + max pooling
▶ the Encoder block is also trained on an auxiliary task (predicting DNase-seq and
ChiP-seq)
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 14

18. Outline
Hi-C data
Deep NN model
Data and training methodology
Results
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 15

19. Used data
▶ two micro-C datasets (two models trained separately): H1-ESCs and HFF cells
(4D nucleome portal), normalized (matrix balancing) – 1 Hi-C dataset: HCT1116
(cohesin-depleted Hi-C)
▶ sequence: GRCh38/hg38 reference genome
▶ chromatine tracks
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 16

20. Methodology
▶ each model trained by random sampling of regions from training chromosomes
▶ evaluation is made on the same data for holdout (test) chromosomes
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 17

21. Outline
Hi-C data
Deep NN model
Data and training methodology
Results
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 18

22. Overall performance
▶ correlation 0.73-0.85 between prediction and experimental data
intra-chromosomes and 0.47-0.74 inter-chromosomes
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 19

23. Overall performance
▶ predicts well CTCF-based interaction but also polycomb-mediated interactions
and promoter-enhancer interactions
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 19

24. Predicting structural variation effects
▶ transposon-mediated 2kb TAD boundary element insertions (2kb ± 5kb
transposon)
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 20

25. Predicting structural variation effects
▶ 40.5Mb inversion mutation (involved in acute myeloid leukemia) – E/P interaction
demonstrated experimentally
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 20

26. Predicting structural variation effects
▶ multiple deletion, inversion, and duplication variants in the same region (involved
in limb malformation), each ∼ 1Mb – consistent with mechanisms observed in 4C
experimental data
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 20

27. Predicting structural variation effects
▶ regions with multiple variants leading to distinct phenotypes
???
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 20

28. In silico mutagenesis
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 21

29. Structural changes on compartments
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 22

30. Structural changes on compartments
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 22

31. Structural changes on compartments
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 22

32. References
(unofficial) Beamer template made with the help of Thomas Schiex, Matthias Zytnicki and Andreea Dreau:
https://forgemia.inra.fr/nathalie.villa-vialaneix/bainrae
Neuvial, P., Foissac, S., and Vialaneix, N. (2023).
Comprendre l’organisation spatiale de l’ADN à l’aide de la statistique.
In L’Interdisciplinarité. Voyage au-del‘a des Disciplines. CNRS.
Forthcoming (book chapter).
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 23

33. Other credits for figures
▶ DNA sequence (p 8, 12): NIH. https://www.genome.gov
▶ NN features (p 12): Horikawa & Kamitani, Frontiers in Computational Neuroscience, 2017. https://doi.org/10.3389/fncom.2017.00004
▶ Sequence CNN (p 14): Elfermi Rachid https://medium.com/analytics-vidhya/predicting-genes-with-cnn-bdf278504e79
GT Chrocogen
January 20th, 2023 / Nathalie Vialaneix
p. 23