A successful hybrid deep learning model aiming at promoter identification

Abstract

Background The zone adjacent to a transcription start site (TSS), namely, the promoter, is primarily involved in the process of DNA transcription initiation and regulation. As a result, proper promoter identification is critical for further understanding the mechanism of the networks controlling genomic regulation. A number of methodologies for the identification of promoters have been proposed. Nonetheless, due to the great heterogeneity existing in promoters, the results of these procedures are still unsatisfactory. In order to establish additional discriminative characteristics and properly recognize promoters, we developed the hybrid model for promoter identification (HMPI), a hybrid deep learning model that can characterize both the native sequences of promoters and the morphological outline of promoters at the same time. We developed the HMPI to combine a method called the PSFN (promoter sequence features network), which characterizes native promoter sequences and deduces sequence features, with a technique referred to as the DSPN (deep structural profiles network), which is specially structured to model the promoters in terms of their structural profile and to deduce their structural attributes. Results The HMPI was applied to human, plant and Escherichia coli K-12 strain datasets, and the findings showed that the HMPI was successful at extracting the features of the promoter while greatly enhancing the promoter identification performance. In addition, after the improvements of synthetic sampling, transfer learning and label smoothing regularization, the improved HMPI models achieved good results in identifying subtypes of promoters on prokaryotic promoter datasets. Conclusions The results showed that the HMPI was successful at extracting the features of promoters while greatly enhancing the performance of identifying promoters on both eukaryotic and prokaryotic datasets, and the improved HMPI models are good at identifying subtypes of promoters on prokaryotic promoter datasets. The HMPI is additionally adaptable to different biological functional sequences, allowing for the addition of new features or models.

Core promoterome of barley embryo

“…To enhance this dataset and obtain a more complete understanding of transcriptional regulation in barley, we plan to generate and analyze additional CAGE datasets, particularly from floral tissues, which are expected to reveal generative-tissue-specific regulatory mechanisms. Furthermore, we hypothesize that species-specific promoter models, constructed from a limited number of tissue-specific CAGE datasets, could facilitate genome-wide promoter prediction without necessitating the production of an exhaustive array of new datasets [68] .…”

iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module

“…We used a fully connected neural network (Wang. et al, 2022b) to predict the enhancers and their strength.…”

Computational methods for identifying enhancer‐promoter interactions

“…(i) Encoding a DNA sequence as in Section “Vector representations of DNA sequence”. (ii) Constructing a neural network to predict the presence of enhancers or promoters, such as CNN [69,73,76,78,81,85,88,98–100,102,104,111], transfer learning [110], or LSTM [82,88,108]. To establish the right characteristics and increase the accuracy of identifying an enhancer or promoter, the above methods either improve the input layer of DNA feature vector representation (for example, dna2vec) or neural network architectures or change the activation functions.…”