Identification of putative promoters in 48 eukaryotic genomes on the basis of DNA free energy.
Ontology highlight
ABSTRACT:
SUBMITTER: Yella VR
PROVIDER: S-EPMC5852095 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:RNA polymerase II (Pol II) core promoters are specialized DNA sequences at transcription start sites of protein-coding and non-coding genes that support the assembly of the transcription machinery and transcription initiation. They enable the highly regulated transcription of genes by selectively integrating regulatory cues from distal enhancers and their associated regulatory proteins. In this Review, we discuss the defining properties of gene core promoters, including their sequence features, chromatin architecture and transcription initiation patterns. We provide an overview of molecular mechanisms underlying the function and regulation of core promoters and their emerging functional diversity, which defines distinct transcription programmes. On the basis of the established properties of gene core promoters, we discuss transcription start sites within enhancers and integrate recent results obtained from dedicated functional assays to propose a functional model of transcription initiation. This model can explain the nature and function of transcription initiation at gene starts and at enhancers and can explain the different roles of core promoters, of Pol II and its associated factors and of the activating cues provided by enhancers and the transcription factors and cofactors they recruit.
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Project description:A panel of intestinal samples collected from common pheasants (Phasianus colchicus) between 2008 and 2017 was used for metagenomic investigation using an unbiased enrichment protocol and different bioinformatic pipelines. The number of sequence reads in the metagenomic analysis ranged from 1,419,265 to 17,507,704 with a viral sequence read rate ranging from 0.01% to 59%. When considering the sequence reads of eukaryotic viruses, RNA and DNA viruses were identified in the samples, including but not limited to coronaviruses, reoviruses, parvoviruses, and CRESS DNA viruses (i.e., circular Rep-encoding single-stranded DNA viruses). Partial or nearly complete genome sequences were reconstructed of at least three different parvoviruses (dependoparvovirus, aveparvovirus and chaphamaparvovirus), as well as gyroviruses and diverse CRESS DNA viruses. Generating information of virus diversity will serve as a basis for developing specific diagnostic tools and for structured epidemiological investigations, useful to assess the impact of these novel viruses on animal health.
| S-EPMC10812470 | biostudies-literature
Project description:BackgroundThe main function of gene promoters appears to be the integration of different gene products in their biological pathways in order to maintain homeostasis. Generally, promoters have been classified in two major classes, namely TATA and CpG. Nevertheless, many genes using the same combinatorial formation of transcription factors have different gene expression patterns. Accordingly, we tried to ask ourselves some fundamental questions: Why certain genes have an overall predisposition for higher gene expression levels than others? What causes such a predisposition? Is there a structural relationship of these sequences in different tissues? Is there a strong phylogenetic relationship between promoters of closely related species?ResultsIn order to gain valuable insights into different promoter regions, we obtained a series of image-based patterns which allowed us to identify 10 generic classes of promoters. A comprehensive analysis was undertaken for promoter sequences from Arabidopsis thaliana, Drosophila melanogaster, Homo sapiens and Oryza sativa, and a more extensive analysis of tissue-specific promoters in humans. We observed a clear preference for these species to use certain classes of promoters for specific biological processes. Moreover, in humans, we found that different tissues use distinct classes of promoters, reflecting an emerging promoter network. Depending on the tissue type, comparisons made between these classes of promoters reveal a complementarity between their patterns whereas some other classes of promoters have been observed to occur in competition. Furthermore, we also noticed the existence of some transitional states between these classes of promoters that may explain certain evolutionary mechanisms, which suggest a possible predisposition for specific levels of gene expression and perhaps for a different number of factors responsible for triggering gene expression. Our conclusions are based on comprehensive data from three different databases and a new computer model whose core is using Kappa index of coincidence.ConclusionsTo fully understand the connections between gene promoters and gene expression, we analyzed thousands of promoter sequences using our Kappa Index of Coincidence method and a specialized Optical Character Recognition (OCR) neural network. Under our criteria, 10 classes of promoters were detected. In addition, the existence of "transitional" promoters suggests that there is an evolutionary weighted continuum between classes, depending perhaps upon changes in their gene products.
| S-EPMC3549790 | biostudies-literature
Project description:The identification of protein binding sites in promoter sequences is a key problem to understand and control regulation in biochemistry and biotechnological processes. We use a computational method to analyze promoters from a given genome. Our approach is based on a physical model at the mesoscopic level of protein-DNA interaction based on the influence of DNA local conformation on the dynamics of a general particle along the chain. Following the proposed model, the joined dynamics of the protein particle and the DNA portion of interest, only characterized by its base pair sequence, is simulated. The simulation output is analyzed by generating and analyzing the Free Energy Landscape of the system. In order to prove the capacity of prediction of our computational method we have analyzed nine promoters of Anabaena PCC 7120. We are able to identify the transcription starting site of each of the promoters as the most populated macrostate in the dynamics. The developed procedure allows also to characterize promoter macrostates in terms of thermo-statistical magnitudes (free energy and entropy), with valuable biological implications. Our results agree with independent previous experimental results. Thus, our methods appear as a powerful complementary tool for identifying protein binding sites in promoter sequences.
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Project description:The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
| S-EPMC403725 | biostudies-literature
Project description:Eukaryotic genes can be broadly classified as TATA-containing and TATA-less based on the presence of TATA box in their promoters. Experiments on both classes of genes have revealed a disparity in the regulation of gene expression and cellular functions between the two classes. In this study, we report characteristic differences in promoter sequences and associated structural properties of the two categories of genes in six different eukaryotes. We have analyzed three structural features, DNA duplex stability, bendability, and curvature along with the distribution of A-tracts, G-quadruplex motifs, and CpG islands. The structural feature analyses reveal that while the two classes of gene promoters are distinctly different from each other, the properties are also distinguishable across the six organisms.
| S-EPMC5337902 | biostudies-literature
Project description:BackgroundLTR retrotransposons are a class of mobile genetic elements containing two similar long terminal repeats (LTRs). Currently, LTR retrotransposons are annotated in eukaryotic genomes mainly through the conventional homology searching approach. Hence, it is limited to annotating known elements.ResultsIn this paper, we report a de novo computational method that can identify new LTR retrotransposons without relying on a library of known elements. Specifically, our method identifies intact LTR retrotransposons by using an approximate string matching technique and protein domain analysis. In addition, it identifies partially deleted or solo LTRs using profile Hidden Markov Models (pHMMs). As a result, this method can de novo identify all types of LTR retrotransposons. We tested this method on the two pairs of eukaryotic genomes, C. elegans vs. C. briggsae and D. melanogaster vs. D. pseudoobscura. LTR retrotransposons in C. elegans and D. melanogaster have been intensively studied using conventional annotation methods. Comparing with previous work, we identified new intact LTR retroelements and new putative families, which may imply that there may still be new retroelements that are left to be discovered even in well-studied organisms. To assess the sensitivity and accuracy of our method, we compared our results with a previously published method, LTR_STRUC, which predominantly identifies full-length LTR retrotransposons. In summary, both methods identified comparable number of intact LTR retroelements. But our method can identify nearly all known elements in C. elegans, while LTR_STRUCT missed about 1/3 of them. Our method also identified more known LTR retroelements than LTR_STRUCT in the D. melanogaster genome. We also identified some LTR retroelements in the other two genomes, C. briggsae and D. pseudoobscura, which have not been completely finished. In contrast, the conventional method failed to identify those elements. Finally, the phylogenetic and chromosomal distributions of the identified elements are discussed.ConclusionWe report a novel method for de novo identification of LTR retrotransposons in eukaryotic genomes with favorable performance over the existing methods.
| S-EPMC1858694 | biostudies-literature
Project description:Nucleotide sequences of DNA regions containing eukaryotic ribosomal promoters were analysed using strategies designed to reveal sequence-directed structural features. DNA curvature, duplex stability and pattern of twist angle variation were studied by computer modelling. Although ribosomal promoters are known to lack sequence homology (unless very closely related species are considered), investigation of these structural characteristics uncovered striking homologies in all the taxonomic groups examined so far. This wide conservation of DNA structures, while DNA sequence is not conserved, suggests that the determined structures are fundamental for ribosomal promoter function. Moreover, this result agrees well with the recent observations showing that RNA polymerase I transcription factors have not evolved as intensively as previously suspected.
| S-EPMC145945 | biostudies-other
Project description: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/.
| S-EPMC8522485 | biostudies-literature
Project description:BACKGROUND: Specific binding of proteins to DNA is one of the most common ways gene expression is controlled. Although general rules for the DNA-protein recognition can be derived, the ambiguous and complex nature of this mechanism precludes a simple recognition code, therefore the prediction of DNA target sequences is not straightforward. DNA-protein interactions can be studied using computational methods which can complement the current experimental methods and offer some advantages. In the present work we use physical effective potentials to evaluate the DNA-protein binding affinities for the lambda repressor-DNA complex for which structural and thermodynamic experimental data are available. RESULTS: The binding free energy of two molecules can be expressed as the sum of an intermolecular energy (evaluated using a molecular mechanics forcefield), a solvation free energy term and an entropic term. Different solvation models are used including distance dependent dielectric constants, solvent accessible surface tension models and the Generalized Born model. The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered. The free energy of binding for non-specific complexes, estimated using the best energetic model, agrees with earlier theoretical suggestions. As a results of these analyses, we propose a protocol for the prediction of DNA-binding target sequences. The possibility of searching regulatory elements within the bacteriophage lambda genome using this protocol is explored. Our analysis shows good prediction capabilities, even in absence of any thermodynamic data and information on the naturally recognized sequence. CONCLUSION: This study supports the conclusion that physics-based methods can offer a completely complementary methodology to sequence-based methods for the identification of DNA-binding protein target sequences.
| S-EPMC2194778 | biostudies-literature