A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features

Author:

Rozenwald Michal B.1,Galitsyna Aleksandra A.2,Sapunov Grigory V.13,Khrameeva Ekaterina E.2,Gelfand Mikhail S.24

Affiliation:

1. Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia

2. Skolkovo Institute of Science and Technology, Moscow, Russia

3. Intento, Inc., Berkeley, CA, USA

4. A.A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow, Russia

Abstract

Technological advances have lead to the creation of large epigenetic datasets, including information about DNA binding proteins and DNA spatial structure. Hi-C experiments have revealed that chromosomes are subdivided into sets of self-interacting domains called Topologically Associating Domains (TADs). TADs are involved in the regulation of gene expression activity, but the mechanisms of their formation are not yet fully understood. Here, we focus on machine learning methods to characterize DNA folding patterns in Drosophila based on chromatin marks across three cell lines. We present linear regression models with four types of regularization, gradient boosting, and recurrent neural networks (RNN) as tools to study chromatin folding characteristics associated with TADs given epigenetic chromatin immunoprecipitation data. The bidirectional long short-term memory RNN architecture produced the best prediction scores and identified biologically relevant features. Distribution of protein Chriz (Chromator) and histone modification H3K4me3 were selected as the most informative features for the prediction of TADs characteristics. This approach may be adapted to any similar biological dataset of chromatin features across various cell lines and species. The code for the implemented pipeline, Hi-ChiP-ML, is publicly available: https://github.com/MichalRozenwald/Hi-ChIP-ML

Funder

Russian Science Foundation

Skoltech Fellowship in Systems Biology

Publisher

PeerJ

Subject

General Computer Science

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