scholarly journals RIblast: An ultrafast RNA-RNA interaction prediction system for comprehensive lncRNA interaction analysis

2016 ◽  
Author(s):  
Tsukasa Fukunaga ◽  
Michiaki Hamada
Keyword(s):  
Large Scale ◽  
Interaction Analysis ◽  
Prediction Method ◽  
Computational Prediction ◽  
Biological Processes ◽  
Computational Costs ◽  
Interaction Prediction ◽  
Suffix Arrays ◽  
Non Coding Rnas ◽  
Rna Interaction

AbstractLong non-coding RNAs (lncRNAs) play important roles in various biological processes. Although more than 58,000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterised. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detection of comprehensive lncRNA-RNA interactions are difficult, computational prediction of lncRNA-RNA interactions is an indispensable technique. However, the high computational costs of existing RNA-RNA interaction prediction tools prevents their application to large-scale lncRNA datasets. Here, we present “RIblast”, an ultrafast RNA-RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs.

scholarly journals Long non-coding RNAs as pan-cancer master gene regulators of associated protein-coding genes: a systems biology approach

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6388 ◽  
Author(s):  
Asanigari Saleembhasha ◽  
Seema Mishra
Keyword(s):  
Gene Expression ◽  
Gene Expression Pattern ◽  
Differentially Expressed ◽  
Regulation Of Transcription ◽  
Biological Processes ◽  
Lncrna Gene ◽  
Cancer Types ◽  
Non Coding Rnas ◽  
Rna Interaction ◽  
Pan Cancer

Despite years of research, we are still unraveling crucial stages of gene expression regulation in cancer. On the basis of major biological hallmarks, we hypothesized that there must be a uniform gene expression pattern and regulation across cancer types. Among non-coding genes, long non-coding RNAs (lncRNAs) are emerging as key gene regulators playing powerful roles in cancer. Using TCGA RNAseq data, we analyzed coding (mRNA) and non-coding (lncRNA) gene expression across 15 and 9 common cancer types, respectively. 70 significantly differentially expressed genes common to all 15 cancer types were enlisted. Correlating with protein expression levels from Human Protein Atlas, we observed 34 positively correlated gene sets which are enriched in gene expression, transcription from RNA Pol-II, regulation of transcription and mitotic cell cycle biological processes. Further, 24 lncRNAs were among common significantly differentially expressed non-coding genes. Using guilt-by-association method, we predicted lncRNAs to be involved in same biological processes. Combining RNA-RNA interaction prediction and transcription regulatory networks, we identified E2F1, FOXM1 and PVT1 regulatory path as recurring pan-cancer regulatory entity. PVT1 is predicted to interact with SYNE1 at 3′-UTR; DNAJC9, RNPS1 at 5′-UTR and ATXN2L, ALAD, FOXM1 and IRAK1 at CDS sites. The key findings are that through E2F1, FOXM1 and PVT1 regulatory axis and possible interactions with different coding genes, PVT1 may be playing a prominent role in pan-cancer development and progression.

Prediction of Protein Ubiquitination Sites in Arabidopsis thaliana

2019 ◽  
Vol 14 (7) ◽  
pp. 614-620 ◽  
Author(s):  
Jiajing Chen ◽  
Jianan Zhao ◽  
Shiping Yang ◽  
Zhen Chen ◽  
Ziding Zhang
Keyword(s):  
Arabidopsis Thaliana ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Feature Selection Method ◽  
Prediction Method ◽  
Support Vector ◽  
Biological Processes ◽  
Protein Ubiquitination ◽  
Ubiquitination Site ◽  
Site Identification

Background: As one of the most important reversible protein post-translation modification types, ubiquitination plays a significant role in the regulation of many biological processes, such as cell division, signal transduction, apoptosis and immune response. Protein ubiquitination usually occurs when ubiquitin molecule is attached to a lysine on a target protein, which is also known as “lysine ubiquitination”. Objective: In order to investigate the molecular mechanisms of ubiquitination-related biological processes, the crucial first step is the identification of ubiquitination sites. However, conventional experimental methods in detecting ubiquitination sites are often time-consuming and a large number of ubiquitination sites remain unidentified. In this study, a ubiquitination site prediction method for Arabidopsis thaliana was developed using a Support Vector Machine (SVM). Methods: We collected 3009 experimentally validated ubiquitination sites on 1607 proteins in A. thaliana to construct the training set. Three feature encoding schemes were used to characterize the sequence patterns around ubiquitination sites, including AAC, Binary and CKSAAP. The maximum Relevance and Minimum Redundancy (mRMR) feature selection method was employed to reduce the dimensionality of input features. Five-fold cross-validation and independent tests were used to evaluate the performance of the established models. Results: As a result, the combination of AAC and CKSAAP encoding schemes yielded the best performance with the accuracy and AUC of 81.35% and 0.868 in the independent test. We also generated an online predictor termed as AraUbiSite, which is freely accessible at: http://systbio.cau.edu.cn/araubisite. Conclusion: We developed a well-performed prediction tool for large-scale ubiquitination site identification in A. thaliana. It is hoped that the current work will speed up the process of identification of ubiquitination sites in A. thaliana and help to further elucidate the molecular mechanisms of ubiquitination in plants.

scholarly journals Use of microRNA to estimate time science death: review

2021 ◽  
Vol 7 (3) ◽  
pp. 132-138
Author(s):  
Airat A. Halikov ◽  
Evgeniy M. Kildyushov ◽  
Kirill O. Kuznetsov ◽  
Laysan R. Iskuzhina ◽  
Gulnaz R. Rahmatullina
Keyword(s):  
High Resistance ◽  
Large Scale ◽  
Scientific Literature ◽  
Clinical Medicine ◽  
Low Molecular Weight ◽  
Biological Processes ◽  
Specific Expression ◽  
Regulate Gene Expression ◽  
Non Coding Rnas ◽  
Potential Use

Death prescription evaluation is still one of the most difficult issues in forensic medical practice. This review aimed to assess the potential use of micro ribonucleic acid (miRNA) in death prescription diagnosis. MiRNAs are small non-coding RNAs that are 1824 nucleotides long and are well preserved in the eukaryotic cells. Their role is to regulate gene expression in biological processes during the post-transcriptional phase. MiRNA was proven to be effective in clinical medicine for various disease diagnoses, with its possible use in forensic medicine as a marker for death prescription assessment due to its low molecular weight, tissue-specific expression, and high resistance to external and internal environmental factors. The analysis results of scientific literature revealed that the internal characteristics of miRNA molecules and their high resistance to degradation make them suitable as biomarkers for the duration of death assessment, especially in the late postmortem period; however, further large-scale studies on cadaveric material are necessary.

Soil/Tire Interaction Analysis Using FEM and FVM

2005 ◽  
Vol 33 (1) ◽  
pp. 38-62 ◽  
Author(s):  
S. Oida ◽  
E. Seta ◽  
H. Heguri ◽  
K. Kato
Keyword(s):  
Large Scale ◽  
Interaction Analysis ◽  
Yield Criterion ◽  
Surrounding Medium ◽  
Flow Analysis ◽  
Measurement Data ◽  
Traction Force ◽  
Elastoplastic Material ◽  
The Road ◽  
Soil Flow

Abstract Vehicles, such as an agricultural tractor, construction vehicle, mobile machinery, and 4-wheel drive vehicle, are often operated on unpaved ground. In many cases, the ground is deformable; therefore, the deformation should be taken into consideration in order to assess the off-the-road performance of a tire. Recent progress in computational mechanics enabled us to simulate the large scale coupling problem, in which the deformation of tire structure and of surrounding medium can be interactively considered. Using this technology, hydroplaning phenomena and tire traction on snow have been predicted. In this paper, the simulation methodology of tire/soil coupling problems is developed for pneumatic tires of arbitrary tread patterns. The Finite Element Method (FEM) and the Finite Volume Method (FVM) are used for structural and for soil-flow analysis, respectively. The soil is modeled as an elastoplastic material with a specified yield criterion and a nonlinear elasticity. The material constants are referred to measurement data, so that the cone penetration resistance and the shear resistance are represented. Finally, the traction force of the tire in a cultivated field is predicted, and a good correlation with experiments is obtained.

LncRNAs a New Target for Post-Stroke Recovery

2020 ◽  
Vol 26 (26) ◽  
pp. 3115-3121
Author(s):  
Jun Yang ◽  
Jingjing Zhao ◽  
Xu Liu ◽  
Ruixia Zhu
Keyword(s):  
Ischemic Stroke ◽  
Microglial Activation ◽  
Vital Role ◽  
Stroke Recovery ◽  
Biological Processes ◽  
Current Development ◽  
Post Stroke ◽  
Cascade Processes ◽  
Non Coding Rnas ◽  
Neuron Injury

LncRNAs (long non-coding RNAs) are endogenous molecules, involved in complicated biological processes. Increasing evidence has shown that lncRNAs play a vital role in the post-stroke pathophysiology. Furthermore, several lncRNAs were reported to mediate ischemia cascade processes include apoptosis, bloodbrain barier breakdown, angiogenesis, microglial activation induced neuroinflammation which can cause neuron injury and influence neuron recovery after ischemic stroke. In our study, we first summarize current development about lncRNAs and post-stroke, focus on the regulatory roles of lncRNAs on pathophysiology after stroke. We also reviewed genetic variation in lncRNA associated with functional outcome after ischemic stroke. Additionally, lncRNA-based therapeutics offer promising strategies to decrease brain damage and promote neurological recovery following ischemic stroke. We believe that lncRNAs will become promising for the frontier strategies for IS and can open up a new path for the treatment of IS in the future.

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