In silico Identification of Eukaryotic Promoters

DeePromClass: Delineator for Eukaryotic Core Promoters Employing Deep Neural Networks

Computational promoter identification in eukaryotes is a classical biological problem that should be refurbished with the availability of an avalanche of experimental data and emerging deep learning technologies. The current knowledge indicates that eukaryotic core promoters display multifarious signals such as TATA-Box, Inr element, TCT, and Pause-button, etc., and structural motifs such as G-quadruplexes. In the present study, we combined the power of deep learning with a plethora of promoter motifs to delineate promoter and non-promoters gleaned from the statistical properties of DNA sequence arrangement. To this end, we implemented convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for five model systems with [-100 to +50] segments relative to the transcription start site being the core promoter. Unlike previous state-of-the-art tools, which furnish a binary decision of promoter or non-promoter, we classify a chunk of 151mer sequence into a promoter along with the consensus signal type or a non-promoter. The combined CNN-LSTM model; we call "DeePromClass", achieved testing accuracy of 90.6%, 93.6%, 91.8%, 86.5%, and 84.0% for S. cerevisiae, C. elegans, D. melanogaster, Mus musculus, and Homo sapiens respectively. In total, our tool provides an insightful update on next-generation promoter prediction tools for promoter biologists.

Delineation of the DNA Structural Features of Eukaryotic Core Promoter Classes

The eukaryotic transcription is orchestrated from a chunk of the DNA region stated as the core promoter. Multifarious and punctilious core promoter signals, viz., TATA-box, Inr, BREs, and Pause Button, are associated with a subset of genes and regulate their spatiotemporal expression. However, the core promoter architecture linked with these signals has not been investigated exhaustively for several species. In this study, we attempted to envisage the adaptive binding landscape of the transcription initiation machinery as a function of DNA structure. To this end, we deployed a set of k-mer based DNA structural estimates and regular expression models derived from experiments, molecular dynamic simulations, and theoretical frameworks, and high-throughout promoter data sets retrieved from the eukaryotic promoter database. We categorized protein-coding gene core promoters based on characteristic motifs at precise locations and analyzed the B-DNA structural properties and non-B-DNA structural motifs for 15 different eukaryotic genomes. We observed that Inr, BREd, and no-motif classes display common patterns of DNA sequence and structural environment. TATA-containing, BREu, and Pause Button classes show a deviant behavior with the TATA class displaying varied axial and twisting flexibility while BREu and Pause Button leaned toward G-quadruplex motif enrichment. Intriguingly, DNA meltability and shape signals are conserved irrespective of the presence or absence of distinct core promoter motifs in the majority of species. Altogether, here we delineated the conserved DNA structural signals associated with several promoter classes that may contribute to the chromatin configuration, orchestration of transcription machinery, and DNA duplex melting during the transcription process.

Identification of putative promoters in 48 eukaryotic genomes on the basis of DNA free energy

Transcription is an intricate mechanism and is orchestrated at the promoter region. The cognate motifs in the promoters are observed in only a subset of total genes across different domains of life. Hence, sequence-motif based promoter prediction may not be a holistic approach for whole genomes. Conversely, the DNA structural property, duplex stability is a characteristic of promoters and can be used to delineate them from other genomic sequences. In this study, we have used a DNA duplex stability based algorithm ‘PromPredict’ for promoter prediction in a broad range of eukaryotes, representing various species of yeast, worm, fly, fish, and mammal. Efficiency of the software has been tested in promoter regions of 48 eukaryotic systems. PromPredict achieves recall values, which range from 68 to 92% in various eukaryotes. PromPredict performs well in mammals, although their core promoter regions are GC rich. ‘PromPredict’ has also been tested for its ability to predict promoter regions for various transcript classes (coding and non-coding), TATA-containing and TATA-less promoters as well as on promoter sequences belonging to different gene expression variability categories. The results support the idea that differential DNA duplex stability is a potential predictor of promoter regions in various genomes.