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

DNA structural features and variability of complete MHC locus sequences

The major histocompatibility (MHC) locus, also known as the Human Leukocyte Antigen (HLA) genes, is located on the short arm of chromosome 6, and contains three regions (Class I, Class II and Class III). This 5 Mbp locus is one of the most variable regions of the human genome, yet it also encodes a set of highly conserved and important proteins related to immunological response. Genetic variations in this region are responsible for more diseases than in the entire rest of the human genome. However, information on local structural features of the DNA is largely ignored. With recent advances in long-read sequencing technology, it is now becoming possible to sequence the entire 5 Mbp MHC locus, producing complete diploid haplotypes of the whole region. Here, we describe structural maps based on the complete sequences from six different homozygous HLA cell lines. We find long-range structural variability in the different sequences for DNA stacking energy, position preference and curvature, variation in repeats, as well as more local changes in regions forming open chromatin structures, likely to influence gene expression levels. These structural maps can be useful in visualizing large scale structural variation across HLA types, in particular when this can be complemented with epigenetic signals.

iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model

Promoters are essential elements of DNA sequence, usually located in the immediate region of the gene transcription start sites, and play a critical role in the regulation of gene transcription. Its importance in molecular biology and genetics has attracted the research interest of researchers, and it has become a consensus to seek a computational method to efficiently identify promoters. Still, existing methods suffer from imbalanced recognition capabilities for positive and negative samples, and their recognition effect can still be further improved. We conducted research on E. coli promoters and proposed a more advanced prediction model, iProL, based on the Longformer pre-trained model in the field of natural language processing. iProL does not rely on prior biological knowledge but simply uses promoter DNA sequences as plain text to identify promoters. It also combines one-dimensional convolutional neural networks and bidirectional long short-term memory to extract both local and global features. Experimental results show that iProL has a more balanced and superior performance than currently published methods. Additionally, we constructed a novel independent test set following the previous specification and compared iProL with three existing methods on this independent test set.

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI

Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.

CDBProm: the Comprehensive Directory of Bacterial Promoters

The decreasing cost of whole genome sequencing has produced high volumes of genomic information that require annotation. The experimental identification of promoter sequences, pivotal for regulating gene expression, is a laborious and cost-prohibitive task. To expedite this, we introduce the Comprehensive Directory of Bacterial Promoters (CDBProm), a directory of in-silico predicted bacterial promoter sequences. We first identified that an Extreme Gradient Boosting (XGBoost) algorithm would distinguish promoters from random downstream regions with an accuracy of 87%. To capture distinctive promoter signals, we generated a second XGBoost classifier trained on the instances misclassified in our first classifier. The predictor of CDBProm is then fed with over 55 million upstream regions from more than 6000 bacterial genomes. Upon finding potential promoter sequences in upstream regions, each promoter is mapped to the genomic data of the organism, linking the predicted promoter with its coding DNA sequence, and identifying the function of the gene regulated by the promoter. The collection of bacterial promoters available in CDBProm enables the quantitative analysis of a plethora of bacterial promoters. Our collection with over 24 million promoters is publicly available at https://aw.iimas.unam.mx/cdbprom/.

Physical Peculiarity of Two Sites in Human Promoters: Universality and Diverse Usage in Gene Function

Since the discovery of physical peculiarities around transcription start sites (TSSs) and a site corresponding to the TATA box, research has revealed only the average features of these sites. Unsettled enigmas include the individual genes with these features and whether they relate to gene function. Herein, using 10 physical properties of DNA, including duplex DNA free energy, base stacking energy, protein-induced deformability, and stabilizing energy of Z-DNA, we clarified for the first time that approximately 97% of the promoters of 21,056 human protein-coding genes have distinctive physical properties around the TSS and/or position -27; of these, nearly 65% exhibited such properties at both sites. Furthermore, about 55% of the 21,056 genes had a minimum value of regional duplex DNA free energy within TSS-centered ±300 bp regions. Notably, distinctive physical properties within the promoters and free energies of the surrounding regions separated human protein-coding genes into five groups; each contained specific gene ontology (GO) terms. The group represented by immune response genes differed distinctly from the other four regarding the parameter of the free energies of the surrounding regions. A vital suggestion from this study is that physical-feature-based analyses of genomes may reveal new aspects of the organization and regulation of genes.

Large language models in bioinformatics: applications and perspectives

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will present a summary of the prominent large language models used in natural language processing, such as BERT and GPT, and focus on exploring the applications of large language models at different omics levels in bioinformatics, mainly including applications of large language models in genomics, transcriptomics, proteomics, drug discovery and single cell analysis. Finally, this review summarizes the potential and prospects of large language models in solving bioinformatic problems.

Computational analysis on the dissemination of non-B DNA structural motifs in promoter regions of 1180 cellular genomes

The promoter regions of gene regulation are under evolutionary constraints and earlier studies uncovered that they are characterized by enrichment of functional non-B DNA structural signatures like curved DNA, cruciform DNA, G-quadruplex, triple-helical DNA, slipped DNA structures, and Z-DNA. However, these studies are restricted to a few model organisms, single non-B DNA motif types, or whole genomic sequences, and their comparative accumulation in promoter regions of different domains of life has not been reported comprehensively. In this study, for the first time, we investigated the preponderance of non-B DNA-prone motifs in promoter regions in 1180 genomes belonging to 28 taxonomic groups using the non-B DNA Motif Search Tool (nBMST). The trends suggest that they are predominant in promoters compared to the upstream and downstream regions of all three domains of life and variably linked to taxonomic groups. Cruciform DNA motif is the most abundant form of non-B DNA, spanning from archaea to lower eukaryotes. Curved DNA motifs are prominent in host-associated bacteria, and suppressed in mammals. Triplex-DNA and slipped DNA structure repeats are discretely dispersed in all lineages. G-quadruplex motifs are significantly enriched in mammals. We also observed that the unique enrichment of non-B DNA in promoters is strongly linked to genome GC, size, evolutionary time divergence, and ecological adaptations. Overall, our work systematically reports the unique non-B DNA structural landscape of cellular organisms from the perspective of the cis-regulatory code of genomes.

Identification of oil palm cis-regulatory elements based on DNA free energy and single nucleotide polymorphism density

Transcription control through cis-regulatory elements (CREs) is one of important regulators of gene expression. This study aimed to identify the location of CREs in oil palm (Elaeis guineensis Jacq.) using the combination of DNA free energy and single nucleotide polymorphism (SNP) density approaches. Promoter region sequences were extracted oil palm genome spanning from 1500 nucleotides (nt) upstream to 1000 nt downstream of every annotated transcription start sites (TSS). Free energy profiles of each promoter region were calculated using PromPredict software. Raw reads from the deep sequencing of 59 oil palm origins were used to calculate SNP density of each promoter region. The result showed that the average free energy (AFE) on the upstream region of TSS is about 1.5 kcal/mol higher compared to the downstream region. Using DNA free energy method, 16,281 regions of CREs were predicted. Most of predicted CREs was located between 1 and 500 nt upstream of TSS. Anti-correlation pattern between free energy and SNP density was observed on the predicted regions of CREs. This anti-correlated pattern was also observed on an experimentally determined promoter of the oil palm metallothionein gene, EgMSP1. Considering the increasing use of promoter information on plant biotechnology, an easy and accurate promoter prediction using the combination of free energy and SNP density method could be recommended.

Complete Genome Sequencing and Bacteriocin Functional Characterization of Pediococcus ethanolidurans CP201 from Daqu

This study aims to sequence the whole genome of Pediococcus ethanolidurans CP201 isolated from Daqu and determine the anti-corrosion ability of bacteriocins on chicken breast. The whole genome sequence information of P. ethanolidurans CP201 was analyzed, and its gene structure and function were explored. It was found that gene1164 had annotations in the NR, Pfam, and Swiss-Prot databases, and was related to bacteriocins. The exogenous expression of the bacteriocin gene Pediocin PE-201 was analyzed based on the pET-21b vector and the host BL21, and the corresponding bacteriocin was successfully expressed under the induction of IPTG. After purification by NI-NTA column, enterokinase treatment, membrane dialysis concentration treatment, and SDS-PAGE electrophoresis, the molecular weight was about 6.5 kDa and the purity was above 90%. By applying different concentrations of bacteriocin to chicken breast with different levels of contamination, the control of pathogenic bacteria, the ordinary contamination level (OC) group, and the high contamination level (MC) group could be completely achieved with 25 mg/L bacteriocin. In conclusion, the bacteriocin produced by the newly isolated CP201 can be applied to the preservation of meat products to prevent the risk of food-borne diseases.

PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence

Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and their core elements accurately is a crucial research area in bioinformatics. At present, models based on machine learning and deep learning have been developed for promoter prediction. However, these models cannot mine the deeper biological information of promoter sequences and consider the complex relationship among promoter sequences. In this work, we propose a novel prediction model called PromGER to predict eukaryotic promoter sequences. For a promoter sequence, firstly, PromGER utilizes four types of feature-encoding methods to extract local information within promoter sequences. Secondly, according to the potential relationships among promoter sequences, the whole promoter sequences are constructed as a graph. Furthermore, three different scales of graph-embedding methods are applied for obtaining the global feature information more comprehensively in the graph. Finally, combining local features with global features of sequences, PromGER analyzes and predicts promoter sequences through a tree-based ensemble-learning framework. Compared with seven existing methods, PromGER improved the average specificity of 13%, accuracy of 10%, Matthew's correlation coefficient of 16%, precision of 4%, F1 score of 6%, and AUC of 9%. Specifically, this study interpreted the PromGER by the t-distributed stochastic neighbor embedding (t-SNE) method and SHAPley Additive exPlanations (SHAP) value analysis, which demonstrates the interpretability of the model.

The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights

Antarctic krill (Euphausia superba) is Earth's most abundant wild animal, and its enormous biomass is vital to the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene families associated with molting and energy metabolism, providing insights into adaptations to the cold and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical sites around the Antarctic continent reveals no clear population structure but highlights natural selection associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for future Antarctic research.

Transcriptome-based Mining of the Constitutive Promoters for Tuning Gene Expression in Aspergillus oryzae

Transcriptional regulation has been adopted for developing metabolic engineering tools. The regulatory promoter is a crucial genetic element for strain optimization. In this study, a gene set of Aspergillus oryzae with highly constitutive expression across different growth stages was identified through transcriptome data analysis. The candidate promoters were functionally characterized in A. oryzae by transcriptional control of β-glucuronidase (GUS) as a reporter. The results showed that the glyceraldehyde triphosphate dehydrogenase promoter (PgpdA1) of A. oryzae with a unique structure displayed the most robust strength in constitutively controlling the expression compared to the PgpdA2 and other putative promoters tested. In addition, the ubiquitin promoter (Pubi) of A. oryzae exhibited a moderate expression strength. The deletion analysis revealed that the 5' untranslated regions of gpdA1 and ubi with the length of 1028 and 811 nucleotides, counted from the putative translation start site (ATG), respectively, could efficiently drive the GUS expression. Interestingly, both promoters could function on various carbon sources for cell growth. Glucose was the best fermentable carbon source for allocating high constitutive expressions during cell growth, and the high concentrations (6-8% glucose, w/v) did not repress their functions. It was also demonstrated that the secondary metabolite gene coding for indigoidine could express under the control of PgpdA1 or Pubi promoter. These strong and moderate promoters of A. oryzae provided beneficial options in tuning the transcriptional expression for leveraging the metabolic control towards the targeted products.

Explainable artificial intelligence as a reliable annotator of archaeal promoter regions

Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulation of their gene expression is essential. A key momentum in regulating genes responsible for the life maintenance of archaea is when transcription factor proteins bind to the promoter element. This DNA segment is conserved, which enables its exploration by machine learning techniques. Here, we trained and tested a support vector machine with 3935 known archaeal promoter sequences. All promoter sequences were coded into DNA Duplex Stability. After, we performed a model interpretation task to map the decision pattern of the classification procedure. We also used a dataset of known-promoter sequences for validation. Our results showed that an AT rich region around position - 27 upstream (relative to the start TSS) is the most conserved in the analyzed organisms. In addition, we were able to identify the BRE element (- 33), the PPE (at - 10) and a position at + 3, that provides a more understandable picture of how promoters are organized in all the archaeal organisms. Finally, we used the interpreted model to identify potential promoter sequences of 135 unannotated organisms, delivering regulatory regions annotation of archaea in a scale never accomplished before ( https://pcyt.unam.mx/gene-regulation/ ). We consider that this approach will be useful to understand how gene regulation is achieved in other organisms apart from the already established transcription factor binding sites.

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.

Structural and functional analysis of stress-inducible genes and their promoters selected from young oil palm (Elaeis guineensis) under salt stress

Background

Soil salinity is a problem in more than 100 countries across all continents. It is one of the abiotic stress that threatens agriculture the most, negatively affecting crops and reducing productivity. Transcriptomics is a technology applied to characterize the transcriptome in a cell, tissue, or organism at a given time via RNA-Seq, also known as full-transcriptome shotgun sequencing. This technology allows the identification of most genes expressed at a particular stage, and different isoforms are separated and transcript expression levels measured. Once determined by this technology, the expression profile of a gene must undergo validation by another, such as quantitative real-time PCR (qRT-PCR). This study aimed to select, annotate, and validate stress-inducible genes—and their promoters—differentially expressed in the leaves of oil palm (Elaeis guineensis) plants under saline stress.

Results

The transcriptome analysis led to the selection of 14 genes that underwent structural and functional annotation, besides having their expression validated using the qRT-PCR technique. When compared, the RNA-Seq and qRT-PCR profiles of those genes resulted in some inconsistencies. The structural and functional annotation analysis of proteins coded by the selected genes showed that some of them are orthologs of genes reported as conferring resistance to salinity in other species. There were those coding for proteins related to the transport of salt into and out of cells, transcriptional regulatory activity, and opening and closing of stomata. The annotation analysis performed on the promoter sequence revealed 22 distinct types of cis-acting elements, and 14 of them are known to be involved in abiotic stress.

Conclusion

This study has helped validate the process of an accurate selection of genes responsive to salt stress with a specific and predefined expression profile and their promoter sequence. Its results also can be used in molecular-genetics-assisted breeding programs. In addition, using the identified genes is a window of opportunity for strategies trying to relieve the damages arising from the salt stress in many glycophyte crops with economic importance.

DeeProPre: A promoter predictor based on deep learning

The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5'end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for RNA polymerase, thus affecting the level of gene expression. Therefore, the correct identification of core promoter and common structural gene is of great significance in the field of biomedicine. At present, many methods have been proposed to improve the accuracy of promoter recognition, but the performances still need to be further improved. In this study, a deep learning algorithm (DeeProPre) based on bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) was proposed. Firstly, the supervised embedding layer was applied to map the sequence to a high-dimensional space. Secondly, two 1D convolutional layers, BiLSTM and attentional mechanism layer were used for extracting features. Finally, the full connection layer activated by Sigmoid function was used to obtain the probability of classification into target categories. This model can identify the promoter region of eukaryotes with high accuracy, providing an analytical basis for further understanding of promoter physiological functions and studies of gene transcription mechanisms. The source code of DeeProPre is freely available at https://github.com/zzwwmmm/DeeProPre/tree/master.

Correlated Expression of the Opsin Retrogene LWS-R and its Host Gene in Two Poeciliid Fishes

The important role of retrogenes in genome evolution and species differentiation is becoming increasingly accepted. One synapomorphy among cyprinodontoid fish is a retrotransposed version of a long-wavelength sensitive (LWS) opsin gene, LWS-R, within an intron of the gephyrin (GPHN) gene. These two genes display opposing orientations. It had been speculated that LWS-R hijacks the cis-regulatory elements of GPHN for transcription, but whether their expression is correlated had remained unclear. Here, in silico predictions identified putative promoters upstream of the translation start site of LWS-R, indicating that its transcription is driven by its own promoter rather than by the GPHN promoter. However, consistent expression ratios of LWS-R:GPHN in the eyeball and brain of fishes indicate that the respective gene transcriptions are correlated. Co-expression is potentially modulated by histone exchange during GPHN transcription. Two isoforms were detected in this study, i.e., intron-free and intron-retaining. Intron-free LWS-R was only expressed in the eyeball of fishes, whereas intron-retaining LWS-R occurred in both eyeball and brain. Expression of vision-associated LWS-R beyond the eyeball supports that it is co-expressed with more ubiquitous GPHN.

Machine learning and statistics shape a novel path in archaeal promoter annotation

Background

Archaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques.

Results and discussions

In this study, experimentally verified promoter sequences of the organisms Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes (i.e. numerical variables) and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control.

Concluding remarks

The classification models were employed to perform genomic annotation into the archaea Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04714-x.

IMGT® Biocuration and Analysis of the Rhesus Monkey IG Loci

The adaptive immune system, along with the innate immune system, are the two main biological processes that protect an organism from pathogens. The adaptive immune system is characterized by the specificity and extreme diversity of its antigen receptors. These antigen receptors are the immunoglobulins (IG) or antibodies of the B cells and the T cell receptors (TR) of the T cells. The IG are proteins that have a dual role in immunity: they recognize antigens and trigger elimination mechanisms, to rid the body of foreign cells. The synthesis of the immunoglobulin heavy and light chains requires gene rearrangements at the DNA level in the IGH, IGK, and IGL loci. The rhesus monkey (Macaca mulatta) is one of the most widely used nonhuman primate species in biomedical research. In this manuscript, we provide a thorough analysis of the three IG loci of the Mmul_10 assembly of rhesus monkey, integrating IMGT previously existing data. Detailed characterization of IG genes includes their localization and position in the loci, the determination of the allele functionality, and the description of the regulatory elements of their promoters as well as the sequences of the conventional recombination signals (RS). This complete annotation of the genomic IG loci of Mmul_10 assembly and the highly detailed IG gene characterization could be used as a model, in additional rhesus monkey assemblies, for the analysis of the IG allelic polymorphism and structural variation, which have been described in rhesus monkeys.

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.

Genome-Wide Prediction of Transcription Start Sites in Conifers

The identification of promoters is an essential step in the genome annotation process, providing a framework for gene regulatory networks and their role in transcription regulation. Despite considerable advances in the high-throughput determination of transcription start sites (TSSs) and transcription factor binding sites (TFBSs), experimental methods are still time-consuming and expensive. Instead, several computational approaches have been developed to provide fast and reliable means for predicting the location of TSSs and regulatory motifs on a genome-wide scale. Numerous studies have been carried out on the regulatory elements of mammalian genomes, but plant promoters, especially in gymnosperms, have been left out of the limelight and, therefore, have been poorly investigated. The aim of this study was to enhance and expand the existing genome annotations using computational approaches for genome-wide prediction of TSSs in the four conifer species: loblolly pine, white spruce, Norway spruce, and Siberian larch. Our pipeline will be useful for TSS predictions in other genomes, especially for draft assemblies, where reliable TSS predictions are not usually available. We also explored some of the features of the nucleotide composition of the predicted promoters and compared the GC properties of conifer genes with model monocot and dicot plants. Here, we demonstrate that even incomplete genome assemblies and partial annotations can be a reliable starting point for TSS annotation. The results of the TSS prediction in four conifer species have been deposited in the Persephone genome browser, which allows smooth visualization and is optimized for large data sets. This work provides the initial basis for future experimental validation and the study of the regulatory regions to understand gene regulation in gymnosperms.

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes

Promoter annotation is an important task in the analysis of a genome. One of the main challenges for this task is locating the border between the promoter region and the transcribing region of the gene, the transcription start site (TSS). The TSS is the reference point to delimit the DNA sequence responsible for the assembly of the transcribing complex. As the same gene can have more than one TSS, so to delimit the promoter region, it is important to locate the closest TSS to the site of the beginning of the translation. This paper presents TSSFinder, a new software for the prediction of the TSS signal of eukaryotic genes that is significantly more accurate than other available software. We currently are the only application to offer pre-trained models for six different eukaryotic organisms: Arabidopsis thaliana, Drosophila melanogaster, Gallus gallus, Homo sapiens, Oryza sativa and Saccharomyces cerevisiae. Additionally, our software can be easily customized for specific organisms using only 125 DNA sequences with a validated TSS signal and corresponding genomic locations as a training set. TSSFinder is a valuable new tool for the annotation of genomes. TSSFinder source code and docker container can be downloaded from http://tssfinder.github.io. Alternatively, TSSFinder is also available as a web service at http://sucest-fun.org/wsapp/tssfinder/.

Characterization of promoters in archaeal genomes based on DNA structural parameters

The transcription machinery of archaea can be roughly classified as a simplified version of eukaryotic organisms. The basal transcription factor machinery binds to the TATA box found around 28 nucleotides upstream of the transcription start site; however, some transcription units lack a clear TATA box and still have TBP/TFB binding over them. This apparent absence of conserved sequences could be a consequence of sequence divergence associated with the upstream region, operon, and gene organization. Furthermore, earlier studies have found that a structural analysis gains more information compared with a simple sequence inspection. In this work, we evaluated and coded 3630 archaeal promoter sequences of three organisms, Haloferax volcanii, Thermococcus kodakarensis, and Sulfolobus solfataricus into DNA duplex stability, enthalpy, curvature, and bendability parameters. We also split our dataset into conserved TATA and degenerated TATA promoters to identify differences among these two classes of promoters. The structural analysis reveals variations in archaeal promoter architecture, that is, a distinctive signal is observed in the TFB, TBP, and TFE binding sites independently of these being TATA-conserved or TATA-degenerated. In addition, the promoter encountering method was validated with upstream regions of 13 other archaea, suggesting that there might be promoter sequences among them. Therefore, we suggest a novel method for locating promoters within the genome of archaea based on DNA energetic/structural features.

ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation

Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.

Symphony of the DNA flexibility and sequence environment orchestrates p53 binding to its responsive elements

The p53 tumor suppressor protein maintains the genome fidelity and integrity by modulating several cellular activities. It regulates these events by interacting with a heterogeneous set of response elements (REs) of regulatory genes in the background of chromatin configuration. At the p53-RE interface, both the base readout and torsional-flexibility of DNA account for high-affinity binding. However, DNA structure is an entanglement of a multitude of physicochemical features, both local and global structure should be considered for dealing with DNA-protein interactions. The goal of current research work is to conceptualize and abstract basic principles of p53-RE binding affinity as a function of structural alterations in DNA such as bending, twisting, and stretching flexibility and shape. For this purpose, we have exploited high throughput in-vitro relative affinity information of responsive elements and genome binding events of p53 from HT-Selex and ChIP-Seq experiments respectively. Our results confirm the role of torsional flexibility in p53 binding, and further, we reveal that DNA axial bending, stretching stiffness, propeller twist, and wedge angles are intimately linked to p53 binding affinity when compared to homeodomain, bZIP, and bHLH proteins. Besides, a similar DNA structural environment is observed in the distal sequences encompassing the actual binding sites of p53 cistrome genes. Additionally, we revealed that p53 cistrome target genes have unique promoter architecture, and the DNA flexibility of genomic sequences around REs in cancer and normal cell types display major differences. Altogether, our work provides a keynote on DNA structural features of REs that shape up the in-vitro and in-vivo high-affinity binding of the p53 transcription factor.

DNA structural and physical properties reveal peculiarities in promoter sequences of the bacterium Escherichia coli K-12

The gene transcription of bacteria starts with a promoter sequence being recognized by a transcription factor found in the RNAP enzyme, this process is assisted through the conservation of nucleotides as well as other factors governing these intergenic regions. Faced with this, the coding of genetic information into physical aspects of the DNA such as enthalpy, stability, and base-pair stacking could suggest promoter activity as well as protrude differentiation of promoter and non-promoter data. In this work, a total of 3131 promoter sequences associated to six different sigma factors in the bacterium E. coli were converted into numeric attributes, a strong set of control sequences referring to a shuffled version of the original sequences as well as coding regions is provided. Then, the parameterized genetic information was normalized, exhaustively analyzed through statistical tests. The results suggest that strong signals in the promoter sequences match the binding site of transcription factor proteins, indicating that promoter activity is well represented by its conversion into physical attributes. Moreover, the features tested in this report conveyed significant variances between promoter and control data, enabling these features to be employed in bacterial promoter classification. The results produced here may aid in bacterial promoter recognition by providing a robust set of biological inferences.

Evolution of Diverse Strategies for Promoter Regulation

DNA is fundamentally important for all cellular organisms due to its role as a store of hereditary genetic information. The precise and accurate regulation of gene transcription depends primarily on promoters, which vary significantly within and between genomes. Some promoters are rich in specific types of bases, while others have more varied, complex sequence characteristics. However, it is not only base sequence but also epigenetic modifications and altered DNA structure that regulate promoter activity. Significantly, many promoters across all organisms contain sequences that can form intrastrand hairpins (cruciforms) or four-stranded structures (G-quadruplex or i-motif). In this review we integrate recent studies on promoter regulation that highlight the importance of DNA structure in the evolutionary adaptation of promoter sequences.

G-quadruplex motifs are functionally conserved in cis-regulatory regions of pathogenic bacteria: An in-silico evaluation

The role of G-quadruplexes in the cellular physiology of human pathogenesis is an intriguing area of research. Nonetheless, their functional roles and evolutionary conservation have not been compared comprehensively in pathogenic forms of various bacterial genera and species. In the current in silico study, we addressed the role of G-quadruplex-forming sequences (G4 motifs) in the context of cis-regulation, expression variation, regulatory networks, gene orthology and ontology. Genome-wide screening across seven pathogenic genomes using the G4Hunter tool revealed the significant prevalence of G4 motifs in cis-regulatory regions compared to the intragenic regions. Significant conservation of G4 motifs was observed in the regulatory region of 300 orthologous genes. Further analysis of published ChIP-Seq data (Minch et al., 2015) of 91 DNA-binding proteins of the M. tuberculosis genome revealed significant links between G4 motifs and target sites of transcriptional regulators. Interestingly, the transcription factors entangled with virulence, in specific, CsoR, Rv0081, DevR/DosR, and TetR family are found to have G4 motifs in their target regulatory regions. Overall the current study applies positional-functional relationship computation to delve into the cis-regulation of G-quadruplex structures in the context of gene orthology in pathogenic bacteria.

Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks

A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core promoter is essential for our understanding of the transcriptional regulation of genes. As a complement to conventional experimental methods, computational techniques with easy-to-use platforms as essential bioinformatics tools can be effectively applied to annotate the functions and physiological roles of promoters. In this work, we propose a deep learning-based method termed Depicter (Deep learning for predicting promoter), for identifying three specific types of promoters, i.e. promoter sequences with the TATA-box (TATA model), promoter sequences without the TATA-box (non-TATA model), and indistinguishable promoters (TATA and non-TATA model). Depicter is developed based on an up-to-date, species-specific dataset which includes Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana promoters. A convolutional neural network coupled with capsule layers is proposed to train and optimize the prediction model of Depicter. Extensive benchmarking and independent tests demonstrate that Depicter achieves an improved predictive performance compared with several state-of-the-art methods. The webserver of Depicter is implemented and freely accessible at https://depicter.erc.monash.edu/.

Delving into Eukaryotic Origins of Replication Using DNA Structural Features

DNA replication in eukaryotes is an intricate process, which is precisely synchronized by a set of regulatory proteins, and the replication fork emanates from discrete sites on chromatin called origins of replication (Oris). These spots are considered as the gateway to chromosomal replication and are stereotyped by sequence motifs. The cognate sequences are noticeable in a small group of entire origin regions or totally absent across different metazoans. Alternatively, the use of DNA secondary structural features can provide additional information compared to the primary sequence. In this article, we report the trends in DNA sequence-based structural properties of origin sequences in nine eukaryotic systems representing different families of life. Biologically relevant DNA secondary structural properties, namely, stability, propeller twist, flexibility, and minor groove shape were studied in the sequences flanking replication start sites. Results indicate that Oris in yeasts show lower stability, more rigidity, and narrow minor groove preferences compared to genomic sequences surrounding them. Yeast Oris also show preference for A-tracts and the promoter element TATA box in the vicinity of replication start sites. On the contrary, Drosophila melanogaster, humans, and Arabidopsis thaliana do not have such features in their Oris, and instead, they show high preponderance of G-rich sequence motifs such as putative G-quadruplexes or i-motifs and CpG islands. Our extensive study applies the DNA structural feature computation to delve into origins of replication across organisms ranging from yeasts to mammals and including a plant. Insights from this study would be significant in understanding origin architecture and help in designing new algorithms for predicting DNA trans-acting factor recognition events.

Metabolomic Analysis of Aspergillus niger Isolated From the International Space Station Reveals Enhanced Production Levels of the Antioxidant Pyranonigrin A

Secondary metabolite (SM) production in Aspergillus niger JSC-093350089, isolated from the International Space Station (ISS), is reported, along with a comparison to the experimentally established strain ATCC 1015. The analysis revealed enhanced production levels of naphtho-γ-pyrones and therapeutically relevant SMs, including bicoumanigrin A, aurasperones A and B, and the antioxidant pyranonigrin A. Genetic variants that may be responsible for increased SM production levels in JSC-093350089 were identified. These findings include INDELs within the predicted promoter region of flbA, which encodes a developmental regulator that modulates pyranonigrin A production via regulation of Fum21. The pyranonigrin A biosynthetic gene cluster was confirmed in A. niger, which revealed the involvement of a previously undescribed gene, pyrE, in its biosynthesis. UVC sensitivity assays enabled characterization of pyranonigrin A as a UV resistance agent in the ISS isolate.