# Cross-Species Predictions of Chromatin Annotations using Neural Networks

Noémien Maillard, Christophe Kropp, Donnadieu Cécile, Julie Demars, Raphaël Mourad

► To cite this version:

Noémien Maillard, Christophe Kropp, Donnadieu Cécile, Julie Demars, Raphaël Mourad. Cross-Species Predictions of Chromatin Annotations using Neural Networks. European Conference on Computational Biology 2025 (ECCB 2025), Jul 2025, Liverpool United Kingdom, United Kingdom. hal-05161584

HAL Id: hal-05161584

https://hal.inrae.fr/hal-05161584v1

Submitted on 14 Jul 2025

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# Cross-Species Predictions of Chromatin Annotations using Neural Networks

Authors: MAILLARD Noémien¹, KLOPP Christophe², DONNADIEU Cécile³, DEMARS Julie¹, MOURAD Raphaël⁴,⁵

¹GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France ; ²SIGENAE, Plate-forme Bio-informatique Genotoul, MIAT, INRAE, 31326 Castanet Tolosan, France ; ³INRAE, US 1426, GeT-PlaGe, Genotoul, Castanet Tolosan, France ; ⁴Université Toulouse III - Paul Sabatier, France ; ⁵MIAT, F-31326, Castanet Tolosan, France

## Introduction

A better knowledge of functional annotations of livestock species can be a lever to link genome to phenome. The genomes of most livestock species have already been sequenced. However, data describing gene regulation mechanisms and chromatin state are insufficient. In contrast, abundant human and mouse data allowed the training of powerful deep learning algorithms. Here, we propose to use 3 artificial neural networks (DeepBind, DeepSEA and Enformer), trained with human and mouse data, to predict annotations on the pig, cattle, chicken and European seabass genomes. The predictions are then compared with experimental data to evaluate the cross-species performance of the neural networks.

**Goals:**

* Improve functional annotations of livestock species

* Reduce the generation of experimental data

* Evaluate the cross-species performance of neural networks

**Tools and data:**

* Artificial neural networks: DeepBind, DeepSEA, Enformer

* Reference genomes: mouse, pig, cattle, chicken, European seabass

* Observed data: ENCODE, FAANG

* GERP conservation scores

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## Genome Scale Analysis

<figure><img src="image_8.png" /><figcaption>**Figure 3:** Neural networks predict experiments as well from phylogenetically distant species genomes (Enformer). A and B, AUC-ROC (left) and AUC-PR (right) for the same predictions grouped by experiments (A, purple) or by tissue (B, green). Predictions show similar performances across species for pig, cattle and chicken but predicting the same experiments from European seabass genome clearly show lower performances. This difference of performance is observed while grouping by experiments (A) and tissues (B). Those results also show that while prediction of experiments show some performance variability (Figure 1, A), much less variability is observed across tissues (B). C, Genome browser of Igf2 region, related to imprinting, in pig (Sscr11.1) and chicken (bGalGal1). For CTCF, DNase and H3K4me3 experiments, observed data are shown as well as corresponding Enformer predictions. Annotations of pig and chicken genomes are from Ensembl release 112.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 5:** Well predicted histone mark H3K4me3 AUC-PR are correlated to phylogenetic distance. A, Phylogenetic distance (substitutions per site) of commonly studied species and species of interest (bold). B, Correlation between DeepSEA AUC-PR and phylogenetic distance of species of interest. C, Same as B but with Enformer AUC-PR. B and C show that there is a correlation between AUC-PR and distance with a first decrease between human and mammals, then a plateau-like from mammals to chicken followed by a second strong decrease from chicken to fish.</figcaption></figure>

## Conclusions

To conclude, the use of neural networks to predict cross-species annotations seems a good lever to improve functional annotations of livestock species and reduce experimental data generation to the most important (for example, to validate the predictions). Here, we showed that prediction performances, for the well predicted H3K4me3 experiment, are similar from mammals to chicken but drop for species phylogenetically too far (European seabass). Moreover, we showed that bins containing the lowest GERP conservation scores show the same distribution of prediction scores as bins containing the highest GERP conservation scores. To go further, we would like to evaluate if the predictions can be used to prioritize variants associated with phenotypes of interest.

**Conclusions:**

* Cross-species predictions seem a good first step to annotate genomes

* The functional role of experiments to predict, the quality and the quantity of data are key factors of predictive performances

* Nucleotide conservation does not drive the cross-species prediction performances of neural networks

**Perspectives:**

* Evaluation of the predictions to prioritize variants associated with phenotypes of interest

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