scholarly journals OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Kashish Chetal ◽  
Sarath Chandra Janga
Keyword(s):  
User Interface ◽  
Operon Structure ◽  
Rna Seq ◽  
Bacterial Genomes ◽  
Substantial Fraction ◽  
Experimental Conditions ◽  
Transcriptomic Data ◽  
Operon Prediction ◽  
Wide Range ◽  
Prokaryotic Genomes

Background. In prokaryotic organisms, a substantial fraction of adjacent genes are organized into operons—codirectionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Although several available operon databases provide information with varying levels of reliability, very few resources provide experimentally supported results. Therefore, we believe that the biological community could benefit from having a new operon prediction database with operons predicted using next-generation RNA-seq datasets.Description. We present operomeDB, a database which provides an ensemble of all the predicted operons for bacterial genomes using available RNA-sequencing datasets across a wide range of experimental conditions. Although several studies have recently confirmed that prokaryotic operon structure is dynamic with significant alterations across environmental and experimental conditions, there are no comprehensive databases for studying such variations across prokaryotic transcriptomes. Currently our database contains nine bacterial organisms and 168 transcriptomes for which we predicted operons. User interface is simple and easy to use, in terms of visualization, downloading, and querying of data. In addition, because of its ability to load custom datasets, users can also compare their datasets with publicly available transcriptomic data of an organism.Conclusion. OperomeDB as a database should not only aid experimental groups working on transcriptome analysis of specific organisms but also enable studies related to computational and comparative operomics.

scholarly journals Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

2006 ◽  
Vol 73 (3) ◽  
pp. 846-854 ◽  
Author(s):  
Nicholas H. Bergman ◽  
Karla D. Passalacqua ◽  
Philip C. Hanna ◽  
Zhaohui S. Qin
Keyword(s):  
Bacterial Genome ◽  
Experimental Information ◽  
Prediction Algorithm ◽  
Comparative Genomic ◽  
Small Subset ◽  
Bacterial Genomes ◽  
Functional Relationships ◽  
Operon Prediction ◽  
Wide Range ◽  
Generic Set

ABSTRACT Various computational approaches have been proposed for operon prediction, but most algorithms rely on experimental or functional data that are only available for a small subset of sequenced genomes. In this study, we explored the possibility of using phylogenetic information to aid in operon prediction, and we constructed a Bayesian hidden Markov model that incorporates comparative genomic data with traditional predictors, such as intergenic distances. The prediction algorithm performs as well as the best previously reported method, with several significant advantages. It uses fewer data sources and so it is easier to implement, and the method is more broadly applicable than previous methods—it can be applied to essentially every gene in any sequenced bacterial genome. Furthermore, we show that near-optimal performance is easily reached with a generic set of comparative genomes and does not depend on a specific relationship between the subject genome and the comparative set. We applied the algorithm to the Bacillus anthracis genome and found that it successfully predicted all previously verified B. anthracis operons. To further test its performance, we chose a predicted operon (BA1489-92) containing several genes with little apparent functional relatedness and tested their cotranscriptional nature. Experimental evidence shows that these genes are cotranscribed, and the data have interesting implications for B. anthracis biology. Overall, our findings show that this algorithm is capable of highly sensitive and accurate operon prediction in a wide range of bacterial genomes and that these predictions can lead to the rapid discovery of new functional relationships among genes.

scholarly journals Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data

2015 ◽  
Vol 14 (6) ◽  
Author(s):  
Thomas Thorne
Keyword(s):  
Empirical Likelihood ◽  
Gold Standard ◽  
R Package ◽  
Parametric Model ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Transcriptomic Data ◽  
Nonparametric Approach ◽  
Nonparametric Detection ◽  
Computational Resources

AbstractThe availability of large quantities of transcriptomic data in the form of RNA-seq count data has necessitated the development of methods to identify genes differentially expressed between experimental conditions. Many existing approaches apply a parametric model of gene expression and so place strong assumptions on the distribution of the data. Here we explore an alternate nonparametric approach that applies an empirical likelihood framework, allowing us to define likelihoods without specifying a parametric model of the data. We demonstrate the performance of our method when applied to gold standard datasets, and to existing experimental data. Our approach outperforms or closely matches performance of existing methods in the literature, and requires modest computational resources. An R package, EmpDiff implementing the methods described in the paper is available from:

scholarly journals Genomic GC content drifts slowly downward in most bacterial genomes

2020 ◽  
Author(s):  
Bert Ely
Keyword(s):  
Gene Conversion ◽  
Gc Content ◽  
Bacterial Genomes ◽  
Base Pairs ◽  
Wide Range ◽  
Biased Gene Conversion ◽  
Prokaryotic Genomes ◽  
Genome Content ◽  
Genomic Gc Content ◽  
Eukaryotic Genomes

AbstractIn every kingdom of life, GC->AT transitions occur more frequently than any other type of mutation due to the spontaneous deamination of cytidine. In eukaryotic genomes, this slow loss of GC base pairs is counteracted by biased gene conversion which increases genomic GC content as part of the recombination process. However, this type of biased gene conversion has not been observed in bacterial genomes so we hypothesized that GC->AT transitions cause a reduction of genomic GC content in prokaryotic genomes on an evolutionary time scale. To test this hypothesis, we used a phylogenetic approach to analyze triplets of closely related genomes representing a wide range of the bacterial kingdom. The resulting data indicate that genomic GC content is slowly declining in bacterial genomes where GC base pairs comprise 40% or more of the total genome. In contrast, genomes containing less than 40% GC base pairs have fewer opportunities for GC->AT transitions to occur so genomic GC content is relatively stable or actually increasing at a slow rate. It should be noted that this observed change in genomic GC content is the net change in shared parts of the genome and does not apply to parts of the genome that have been lost or acquired since the genomes being compared shared common ancestor. However, a more detailed analysis of two Caulobacter genomes revealed that the acquisition of mobile elements by the two genomes actually reduced the total genome content as well.

scholarly journals A window into lysogeny: Revealing temperate phage biology with transcriptomics

2019 ◽  
Author(s):  
Siân V. Owen ◽  
Rocío Canals ◽  
Nicolas Wenner ◽  
Disa L. Hammarlöf ◽  
Carsten Kröger ◽  
...  
Keyword(s):  
Molecular Biology ◽  
Sequence Data ◽  
Temperate Phage ◽  
Phage Resistance ◽  
Bacterial Cells ◽  
Rna Seq ◽  
Bacterial Genomes ◽  
Transcriptomic Data ◽  
Dna Sequence Data ◽  
Bacterial Rna

ABSTRACTIntegrated phage elements, known as prophages, are a pervasive feature of bacterial genomes. Prophages can enhance the fitness of their bacterial hosts by conferring beneficial functions, such as virulence, stress tolerance or phage resistance, which are encoded by accessory loci. Whilst the majority of phage-encoded genes are repressed during lysogeny, accessory loci are often highly expressed. However, novel prophage accessory loci are challenging to identify based on DNA sequence data alone. Here, we use bacterial RNA-seq data to examine the transcriptional landscapes of five Salmonella prophages. We show that transcriptomic data can be used to heuristically enrich for prophage features that are highly expressed within bacterial cells and often represent functionally-important accessory loci. Using this approach we identify a novel anti-sense RNA species in prophage BTP1, STnc6030, which mediates superinfection exclusion of phage BTP1 and immunity to closely-related phages. Bacterial transcriptomic datasets are a powerful tool to explore the molecular biology of temperate phages.

scholarly journals Genomic GC content drifts downward in most bacterial genomes

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0244163
Author(s):  
Bert Ely
Keyword(s):  
Gene Conversion ◽  
Gc Content ◽  
Bacterial Genomes ◽  
Evolutionary Time ◽  
Base Pairs ◽  
Wide Range ◽  
Biased Gene Conversion ◽  
Prokaryotic Genomes ◽  
Genomic Gc Content ◽  
Eukaryotic Genomes

In every kingdom of life, GC->AT transitions occur more frequently than any other type of mutation due to the spontaneous deamination of cytidine. In eukaryotic genomes, this slow loss of GC base pairs is counteracted by biased gene conversion which increases genomic GC content as part of the recombination process. However, this type of biased gene conversion has not been observed in bacterial genomes, so we hypothesized that GC->AT transitions cause a reduction of genomic GC content in prokaryotic genomes on an evolutionary time scale. To test this hypothesis, we used a phylogenetic approach to analyze triplets of closely related genomes representing a wide range of the bacterial kingdom. The resulting data indicate that genomic GC content is drifting downward in bacterial genomes where GC base pairs comprise 40% or more of the total genome. In contrast, genomes containing less than 40% GC base pairs have fewer opportunities for GC->AT transitions to occur so genomic GC content is relatively stable or actually increasing. It should be noted that this observed change in genomic GC content is the net change in shared parts of the genome and does not apply to parts of the genome that have been lost or acquired since the genomes being compared shared common ancestor. However, a more detailed analysis of two Caulobacter genomes revealed that the acquisition of mobile elements by the two genomes actually reduced the total genomic GC content as well.

scholarly journals Transferable representations of single-cell transcriptomic data

2021 ◽  
Author(s):  
Ethan Weinberger ◽  
Su-In Lee
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Future Research ◽  
Rna Seq ◽  
Cell Type ◽  
Experimental Conditions ◽  
Technical Noise ◽  
Transcriptomic Data ◽  
Low Dimensional ◽  
Computational Resources

Advances in single-cell RNA-seq (scRNA-seq) technologies are enabling the construction of large-scale, human-annotated reference cell atlases, creating unprecedented opportunities to accelerate future research. However, effectively leveraging information from these atlases, such as clustering labels or cell type annotations, remains challenging due to substantial technical noise and sparsity in scRNA-seq measurements. To address this problem, we present HD-AE, a deep autoencoder designed to extract integrated low-dimensional representations of scRNA-seq measurements across datasets from different labs and experimental conditions. Unlike previous approaches, HD-AE's representations successfully transfer to new query datasets without needing to retrain the model. Researchers without substantial computational resources or machine learning expertise can thus leverage the robust representations learned by pretrained HD-AE models to compare embeddings of their own data with previously generated sets of reference embeddings.

Strongly Coupled Redox-Linked Conformational Switching at the Active Site of the Non-Heme Iron-Dependent Dioxygenase, TauD

2019 ◽  
Author(s):  
Christopher John ◽  
Greg M. Swain ◽  
Robert P. Hausinger ◽  
Denis A. Proshlyakov
Keyword(s):  
Active Site ◽  
Structural Model ◽  
Heme Iron ◽  
Chemical Transformations ◽  
Experimental Conditions ◽  
Strongly Coupled ◽  
Non Heme Iron ◽  
Reversible Redox ◽  
Wide Range ◽  
Complex Structural

2-Oxoglutarate (2OG)-dependent dioxygenases catalyze C-H activation while performing a wide range of chemical transformations. In contrast to their heme analogues, non-heme iron centers afford greater structural flexibility with important implications for their diverse catalytic mechanisms. We characterize an <i>in situ</i> structural model of the putative transient ferric intermediate of 2OG:taurine dioxygenase (TauD) by using a combination of spectroelectrochemical and semi-empirical computational methods, demonstrating that the Fe (III/II) transition involves a substantial, fully reversible, redox-linked conformational change at the active site. This rearrangement alters the apparent redox potential of the active site between -127 mV for reduction of the ferric state and 171 mV for oxidation of the ferrous state of the 2OG-Fe-TauD complex. Structural perturbations exhibit limited sensitivity to mediator concentrations and potential pulse duration. Similar changes were observed in the Fe-TauD and taurine-2OG-Fe-TauD complexes, thus attributing the reorganization to the protein moiety rather than the cosubstrates. Redox difference infrared spectra indicate a reorganization of the protein backbone in addition to the involvement of carboxylate and histidine ligands. Quantitative modeling of the transient redox response using two alternative reaction schemes across a variety of experimental conditions strongly supports the proposal for intrinsic protein reorganization as the origin of the experimental observations.

scholarly journals Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Xu ◽  
Xiangdong Liu ◽  
Qiming Dai
Keyword(s):  
Machine Learning ◽  
Hypertrophic Cardiomyopathy ◽  
Heart Diseases ◽  
Expression Patterns ◽  
Support Vector ◽  
Rna Seq ◽  
Ppi Network ◽  
Learning Methods ◽  
Transcriptomic Data ◽  
Machine Learning Methods

Abstract Background Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. Methods The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Results Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A. Conclusions This study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

scholarly journals Low-Energy Electron Damage to Condensed-Phase DNA and Its Constituents

2021 ◽  
Vol 22 (15) ◽  
pp. 7879
Author(s):  
Yingxia Gao ◽  
Yi Zheng ◽  
Léon Sanche
Keyword(s):  
Condensed Phase ◽  
Genetic Material ◽  
High Energy ◽  
Dna Lesions ◽  
Low Energy ◽  
Experimental Investigations ◽  
Experimental Conditions ◽  
Strand Breaks ◽  
Sequence Of Events ◽  
Wide Range

The complex physical and chemical reactions between the large number of low-energy (0–30 eV) electrons (LEEs) released by high energy radiation interacting with genetic material can lead to the formation of various DNA lesions such as crosslinks, single strand breaks, base modifications, and cleavage, as well as double strand breaks and other cluster damages. When crosslinks and cluster damages cannot be repaired by the cell, they can cause genetic loss of information, mutations, apoptosis, and promote genomic instability. Through the efforts of many research groups in the past two decades, the study of the interaction between LEEs and DNA under different experimental conditions has unveiled some of the main mechanisms responsible for these damages. In the present review, we focus on experimental investigations in the condensed phase that range from fundamental DNA constituents to oligonucleotides, synthetic duplex DNA, and bacterial (i.e., plasmid) DNA. These targets were irradiated either with LEEs from a monoenergetic-electron or photoelectron source, as sub-monolayer, monolayer, or multilayer films and within clusters or water solutions. Each type of experiment is briefly described, and the observed DNA damages are reported, along with the proposed mechanisms. Defining the role of LEEs within the sequence of events leading to radiobiological lesions contributes to our understanding of the action of radiation on living organisms, over a wide range of initial radiation energies. Applications of the interaction of LEEs with DNA to radiotherapy are briefly summarized.

scholarly journals Review of the Upright Balance Assessment Based on the Force Plate

2021 ◽  
Vol 18 (5) ◽  
pp. 2696
Author(s):  
Baoliang Chen ◽  
Peng Liu ◽  
Feiyun Xiao ◽  
Zhengshi Liu ◽  
Yong Wang
Keyword(s):  
Postural Balance ◽  
Force Plate ◽  
Assessment Method ◽  
Balance Assessment ◽  
Experimental Conditions ◽  
Methodological Research ◽  
Assessment Tests ◽  
Wide Range ◽  
Foot Posture ◽  
Plate System

Quantitative assessment is crucial for the evaluation of human postural balance. The force plate system is the key quantitative balance assessment method. The purpose of this study is to review the important concepts in balance assessment and analyze the experimental conditions, parameter variables, and application scope based on force plate technology. As there is a wide range of balance assessment tests and a variety of commercial force plate systems to choose from, there is room for further improvement of the test details and evaluation variables of the balance assessment. The recommendations presented in this article are the foundation and key part of the postural balance assessment; these recommendations focus on the type of force plate, the subject’s foot posture, and the choice of assessment variables, which further enriches the content of posturography. In order to promote a more reasonable balance assessment method based on force plates, further methodological research and a stronger consensus are still needed.

Sign in / Sign up

Export Citation Format

Share Document