scholarly journals Hairpin: a tool for predicting structural non-coding RNA genes ad initio

2019 ◽  
Author(s):  
Jakob Peder Pettersen
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Secondary Structure ◽  
Dna Sequences ◽  
Secondary Structures ◽  
Biological Signal ◽  
Non Coding Rna ◽  
A Genome ◽  
K 12 ◽  
Rna Genes

AbstractBackgroundStructural RNA genes play important and various roles in gene expression and its regulation. Finding such RNA genes in a genome poses a challenge, which in most cases is solved by homology approaches. Ab intio methods for prediction exist, but are not that much explored.ResultsWe introduce hairpin which identify potential structural RNA genes only based on the sequence. We use the algorithm to predict RNA genes in Escherichia coli K-12. When looking at very short regions of the genome, we do not get results differing very much from a random shuffling of the genome. However, at longer stretches it is a clear biological signal. It turns out that none of the regions predicted to code for RNA genes have such an annotation in literature.ConclusionsArbitrary DNA sequences seem to give rise to transcripts with secondary structures similar to real ncRNA. We therefore conclude that exclusively looking at secondary structure base-parings is in general a futile approach.

scholarly journals Yeast DNA sequences initiating gene expression in Escherichia coli

2004 ◽  
Vol 159 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Astrid Lewin ◽  
Thi Tuyen Tran ◽  
Daniela Jacob ◽  
Martin Mayer ◽  
Barbara Freytag ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Dna Sequences ◽  
Expression In Escherichia Coli

scholarly journals Deep learning predicts short non-coding RNA functions from only raw sequence data

2020 ◽  
Vol 16 (11) ◽  
pp. e1008415
Author(s):  
Teresa Maria Rosaria Noviello ◽  
Francesco Ceccarelli ◽  
Michele Ceccarelli ◽  
Luigi Cerulo
Keyword(s):  
Secondary Structure ◽  
Sequence Data ◽  
Computational Cost ◽  
Large Data ◽  
Sequence Information ◽  
Structure Information ◽  
Non Coding Rna ◽  
A Genome ◽  
Data Volume ◽  
Biological Functionality

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.

scholarly journals Global gene expression analysis of Escherichia coli K-12 DH5α after exposure to 2.4 GHz wireless fidelity radiation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ilham H. Said-Salman ◽  
Fatima A. Jebaii ◽  
Hoda H. Yusef ◽  
Mohamed E. Moustafa
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Cell Adhesion ◽  
Metabolic Pathways ◽  
Enrichment Analysis ◽  
Global Gene Expression ◽  
Enrichment Score ◽  
Pcr Analysis ◽  
Radiofrequency Radiation ◽  
K 12

Abstract This study investigated the non-thermal effects of Wi-Fi radiofrequency radiation of 2.4 GHz on global gene expression in Escherichia coli K-12 DH5α. High-throughput RNA-sequencing of 2.4 GHz exposed and non-exposed bacteria revealed that 101 genes were differentially expressed (DEGs) at P ≤ 0.05. The up-regulated genes were 52 while the down-regulated ones were 49. QRT-PCR analysis of pgaD, fliC, cheY, malP, malZ, motB, alsC, alsK, appB and appX confirmed the RNA-seq results. About 7% of DEGs are involved in cellular component organization, 6% in response to stress stimulus, 6% in biological regulation, 6% in localization, 5% in locomotion and 3% in cell adhesion. Database for annotation, visualization and integrated discovery (DAVID) functional clustering revealed that DEGs with high enrichment score included genes for localization of cell, locomotion, chemotaxis, response to external stimulus and cell adhesion. Kyoto encyclopedia of genes and genomes (KEGG) pathways analysis showed that the pathways for flagellar assembly, chemotaxis and two-component system were affected. Go enrichment analysis indicated that the up-regulated DEGs are involved in metabolic pathways, transposition, response to stimuli, motility, chemotaxis and cell adhesion. The down-regulated DEGs are associated with metabolic pathways and localization of ions and organic molecules. Therefore, the exposure of E. coli DH5α to Wi-Fi radiofrequency radiation for 5 hours influenced several bacterial cellular and metabolic processes.

scholarly journals Identification of Escherichia coli Genes That Are Specifically Expressed in a Murine Model of Septicemic Infection

2002 ◽  
Vol 70 (7) ◽  
pp. 3404-3412 ◽  
Author(s):  
Muhammad A. Khan ◽  
Richard E. Isaacson
Keyword(s):  
Escherichia Coli ◽  
Dna Sequences ◽  
Anaerobic Respiration ◽  
Disease Process ◽  
Antimicrobial Drugs ◽  
Reverse Transcription Pcr ◽  
Successful Infection ◽  
K 12

ABSTRACT Identification and characterization of bacterial genes that are induced during the disease process are important in understanding the molecular mechanism of disease and can be useful in designing antimicrobial drugs to control the disease. The identification of in vivo induced (ivi) genes of an Escherichia coli septicemia strain by using antibiotic-based in vivo expression technology is described. Bacterial clones resistant to chloramphenicol in vivo were recovered from the livers of infected mice. Most of the ivi clones were sensitive to chloramphenicol when grown in vitro. Using reverse transcription-PCR, it was demonstrated that selected ivi clones expressed cat in the livers of infected mice but not during in vitro growth. A total of 750 colonies were recovered after three successive rounds of in vivo selection, and 168 isolated ivi clones were sequenced. The sequence analysis revealed that 37 clones encoded hypothetical proteins found in E. coli K-12, whereas 10 clones contained genes that had no significant homology to DNA sequences in GenBank. Two clones were found to contain transposon-related functions. Other clones contained genes required for amino acid metabolism, anaerobic respiration, DNA repair, the heat shock response, and the cellular repressor of the SOS response. In addition, one clone contained the aerobactin biosynthesis gene iucA. Mutations were introduced in to seven of the identified ivi genes. An in vivo mouse challenge-competition assay was used to determine if the mutants were attenuated. The results suggested that these ivi genes were important for survival in vivo, and three of the seven mutant ivi clones were required for successful infection of mice.

scholarly journals Metabolic regulation of Irp gene expression in Escherichia coli K-12

Microbiology ◽  
1997 ◽  
Vol 143 (6) ◽  
pp. 2079-2084 ◽  
Author(s):  
C. F. Chen ◽  
J. Lan ◽  
M. Korovine ◽  
Z. Q. Shao ◽  
L. Tao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Metabolic Regulation ◽  
Expression In Escherichia Coli ◽  
K 12

scholarly journals Translational Regulation of Gene Expression by an Anaerobically Induced Small Non-coding RNA in Escherichia coli

2010 ◽  
Vol 285 (14) ◽  
pp. 10690-10702 ◽  
Author(s):  
Anders Boysen ◽  
Jakob Møller-Jensen ◽  
Birgitte Kallipolitis ◽  
Poul Valentin-Hansen ◽  
Martin Overgaard
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Translational Regulation ◽  
Regulation Of Gene Expression ◽  
Non Coding Rna
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