CREWdb: Optimizing Chromatin Readers, Erasers, and Writers Database using Machine Learning-Based Approach

Aberration in heterochromatin and euchromatin states contributes to various disease phenotypes. The transcriptional regulation between these two states is significantly governed by post-translational modifications made by three functional types of chromatin regulators: readers, writers, and erasers. Writers introduce a chemical modification to DNA and histone tails, readers bind the modification to histone tails using specialized domains, and erasers remove the modification introduced by writers. Altered regulation of these chromatin regulators plays a key role in complex diseases such as cancer, neurodevelopmental diseases, myocardial diseases, and embryonic development. Due to the reversible nature of chromatin modifications, we have the opportunity to develop therapeutic approaches targeting chromatin regulators. Currently, a limited amount of chromatin regulators have been identified, and a subset of those identified have been ambiguously classified as multiple chromatin regulator types. Thus, we have applied machine learning-based approaches to predict and classify the functionality of chromatin regulator proteins, optimizing the first comprehensive database of chromatin regulators known as CREWdb. Database URL Source codes are implemented in Python and are freely available for download at: https://github.com/smollah/CREWdb