Histone-Net: a multi-paradigm computational framework for histone occupancy and modification prediction

Abstract

Deep exploration of histone occupancy and covalent post-translational modifications (e.g., acetylation, methylation) is essential to decode gene expression regulation, chromosome packaging, DNA damage, and transcriptional activation. Existing computational approaches are unable to precisely predict histone occupancy and modifications mainly due to the use of sub-optimal statistical representation of histone sequences. For the establishment of an improved histone occupancy and modification landscape for multiple histone markers, the paper in hand presents an end-to-end computational multi-paradigm framework “Histone-Net”. To learn local and global residue context aware sequence representation, Histone-Net generates unsupervised higher order residue embeddings (DNA2Vec) and presents a different application of language modelling, where it encapsulates histone occupancy and modification information while generating higher order residue embeddings (SuperDNA2Vec) in a supervised manner. We perform an intrinsic and extrinsic evaluation of both presented distributed representation learning schemes. A comprehensive empirical evaluation of Histone-Net over ten benchmark histone markers data sets for three different histone sequence analysis tasks indicates that SuperDNA2Vec sequence representation and softmax classifier-based approach outperforms state-of-the-art approach by an average accuracy of 7%. To eliminate the overhead of training separate binary classifiers for all ten histone markers, Histone-Net is evaluated in multi-label classification paradigm, where it produces decent performance for simultaneous prediction of histone occupancy, acetylation, and methylation.

TransPTM: a Transformer-Based Model for Non-Histone Acetylation Site Prediction

“…Upon evaluating the model's prediction performance via 10-fold cross-validation and independent testing set, the accuracies of 84.95% and 84.87% were achieved respectively. Meanwhile, Muhammad et al proposed Histone-Net [14], a novel deep learning predictor capable of predicting histone occupancy, methylation, and acetylation levels across multiple datasets in intra-domain and cross-domain binary classification paradigms. This model outperforms state-of-the-art approaches by an average accuracy of 7% across ten different datasets.…”