Prediction of histone post-translational modifications using deep learning

Abstract

Motivation Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. Availability https://github.com/dDipankar/DeepPTM

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

“…In all settings, embedding generation model is trained for 10 epochs, where we tweak the dropout from 0.1 to 0.5 only during SuperDNA2Vec embedding generation. From different batch sizes (32,64,128,256), learning rates (0.001-to-0.008), and decay rates (0.91-to-0.99), proposed deep learning approach performs better when it is trained with a batch size of 64, Adam [57,58] optimizer decay rate of 0.95, and learning rate of 0.008.…”

“…Using 10 different benchmark data sets facilitated by [30] related to histone occupancy, acetylation, and methylation, to date, a number of computational methodologies have been developed [31][32][33][34][35][36]. Prime focus of existing computational approaches [30,[34][35][36] has been to generate a rich statistical representation of histone sequences.…”

“…Prime focus of existing computational approaches [30,[34][35][36] has been to generate a rich statistical representation of histone sequences. In this regard, few researchers have utilized bag of words-based approaches [30,34], whereas others have utilized one hot encoding scheme to generate statistical representation of histone sequences [31][32][33]35]. While, bag of words-based statistical representation only manages to capture residue frequency and neglects rich semantic information.…”

“…Existing approaches [31][32][33][34][35][36] have not been evaluated in cross-domain binary classification paradigm, where for each histone sequence analysis task, model is trained on one type of histone marker and tested on another type of histone marker. We explore the performance potential of proposed Histone-Net approach in cross-domain binary classification paradigm.…”

Seq-RBPPred: Predicting RNA-Binding Proteins from Sequence

“…In recent years, there has been a proliferation of applications employing machine learning in genomics. These applications encompass a wide range of areas, including the prediction of binding sites for DNA and RNA binding proteins, − the identification of cis -regulatory elements such as promoters , and enhancers, , the prediction of DNA methylation patterns, − and histone modifications, , the determination of cellular localization, − the analysis of alternative splicing events, , and the assessment of the impact of genetic variants on gene expression, among others.…”