
The recent expansion of machine learning applications to molecular biology proved to have a significant contribution to our understanding of biological systems, and genome functioning in particular. Technological advances enabled the collection of large epigenetic datasets, including information about various DNA binding factors (ChIP-Seq) and DNA spatial structure (Hi-C). Several studies have confirmed the correlation between DNA binding factors and Topologically Associating Domains (TADs) in DNA structure. However, the information about physical proximity represented by genomic coordinate was not yet used for the improvement of the prediction models.

 In this research, we focus on Machine Learning methods for prediction of folding patterns of DNA in a classical model organism Drosophila melanogaster. The paper considers linear models with four types of regularization, Gradient Boosting and Recurrent Neural Networks for the prediction of chromatin folding patterns from epigenetic marks. The bidirectional LSTM RNN model outperformed all the models and gained the best prediction scores. This demonstrates the utilization of complex models and the importance of memory of sequential DNA states for the chromatin folding. We identify informative epigenetic features that lead to the further conclusion of their biological significance. Machine Learning algorithms are used nowadays in multiple disciplines. In particular, the utilization of these methods in molecular biology has a significant impact on our understanding of cell processes (Eraslan et al., 2019) . Investigating the large-scale DNA structure, i.e. the spatial organization of the genome, or chromatin, is one of the challenging tasks in the field. The relevance of this research is supported by multiple observations of interconnections between gene regulation, inheritance, disease and chromatin structure (Lupiáñez et al., 2016) . Although the chromatin structure is folded 10 4 − 10 5 times, it maintains fundamental and vital processes of the cell. Various regulation mechanisms were shown to act through the three-dimensional structure formation. High-throughput experiments capturing contacting fragments of the genome, such as Hi-C, have unravelled many principles of chromosomal folding (Lieberman-Aiden et al., 2009) . Although Hi-C-like techniques were developed ten years ago, the experiments of high quality started to be published mainly during the last several years, and the protocol is still elaborate and expensive. Hi-C has also revealed that chromosomes are subdivided into a set of self-interacting domains called Topologically Associating Domains (TADs) (Ulianov et al., 2016 ) that can be seen in Figure 1 . TADs were shown to correlate with units of replication timing regulation in mammals (Pope et al., 2014) , as well as with either active or repressed epigenetic domains in Drosophila (Sexton et al., 2012) . Various factors were shown to contribute to structure formation. ChIP-Seq is one of the highthroughput experiments dedicated to the detection of factors binding on the DNA in vivo. The rapid growth of its data enables exploring the chromatin structure with more sophisticated and complex methods such as Machine Learning. The datasets for various factors such as ChIP-Seq experiments for histone modifications become increasingly available in public databases (Ho et al., 2014) . The relationship between TADs and epigenetics marks has been investigated recently (Ulianov et al., 2016) . However, the mechanisms that underlie partitioning of the genome into TADs remain poorly understood. Moreover, there is no comprehensive work investigating all the factors that are publicly available yet. Figure 1: Typical representation of Hi-C interaction map as a genome-wide contact matrix, or a heatmap. Bright triangles can be visible across the diagonal. These structures are called TADs (topologically associating domains) and interpreted as compact globules of interacting chromatin. Drosophila melanogaster S2-DRSC cells This study focuses on bringing insights into the 3D chromatin structure using Machine Learning. The goal is to explore the principles of TAD folding and the role of epigenetics in this process. To that end, the analysis of Drosophila melanogaster chromatin was performed using Linear Regression models and Recurrent Neural Networks. Quality metrics were calculated, and informative features were investigated to identify which of chromatin marks are most significant in predicting information about TADs. In addition, the same techniques might be used to explore the 3D chromatin structure of mammals and humans in particular. Such reconstruction of the information about Hi-C map might be useful not only for understanding the chromatin structure formation but can also have various practical medical applications. For example, gliomagenesis and limb malformations in humans were demonstrated to be caused by chromosomal topology disruption (Krijger & De Laat, 2016) . The ChIP-Seq data usage for chromatin folding patterns prediction was confirmed by training ML models with dignified evaluation scores. Moreover, the results were interpretable and biologically relevant. Linear Regression models, Gradient Boosting Trees and Recurrent Neural Networks were for the first time applied to our new dataset of chromatin characteristics. All models have performed better than constant prediction with the mean value of the training dataset. The utilization of memory of previous states linearly ordered by DNA molecule improves the prediction significantly as the best results were obtained by bidirectional LSTM RNN model. The optimal input window size was also equal to six which has a biological meaning as it strongly aligns with the average TAD length. Feature importance analysis of the input ChIP-Seq data was conducted. The Linear models weights provided a biologically meaningful prioritization of the ChIP-Seq. Moreover, after training Linear Regression with L1 regularization detected one ChIP-Seq feature Chriz on both of the datasets as the most influencing. The results of applying Neural Network models allowed the evaluation of the biological impact of the features. Exploration of the transferability of the models between different cell types and species might be an interesting development of this work. More input features of different biological nature, such as DNA sequence itself, is another direction of research. The code is open sourced and the implemented pipeline can be easily adapted to any similar biological dataset of chromatin features. A APPENDIX Figure Figure 10: MSE, MAE, R 2 , weighted MSE metrics for various ML models experiments. Here "LR" stands for Linear Regression models, "GB-X" -Grad Boosting models with X estimators, "* best" means that the presented scores for the best of models of type *.
We apply RNN to solve the biological problem of chromatin folding patterns prediction from epigenetic marks and demonstrate for the first time that utilization of memory of sequential states on DNA molecule is significant for the best performance.
{"src": "scitldr-AIC_train", "paper_id": "Bkel6ertwS"}