scholarly journals FINDING NON-CODING RNAs THROUGH GENOME-SCALE CLUSTERING

2009 ◽  
Vol 07 (02) ◽  
pp. 373-388 ◽  
Author(s):  
HUEI-HUN TSENG ◽  
ZASHA WEINBERG ◽  
JEREMY GORE ◽  
RONALD R. BREAKER ◽  
WALTER L. RUZZO
Keyword(s):  
Secondary Structure ◽  
Efficient Method ◽  
Regulatory Mechanisms ◽  
Homology Search ◽  
Substantial Portion ◽  
Primary Sequence ◽  
Clustering Method ◽  
Microbial Genomes ◽  
Non Coding Rnas ◽  
Genome Scale

Non-coding RNAs (ncRNAs) are transcripts that do not code for proteins. Recent findings have shown that RNA-mediated regulatory mechanisms influence a substantial portion of typical microbial genomes. We present an efficient method for finding potential ncRNAs in bacteria by clustering genomic sequences based on homology inferred from both primary sequence and secondary structure. We evaluate our approach using a set of predominantly Firmicutes sequences. Our results showed that, though primary sequence based–homology search was inaccurate for diverged ncRNA sequences, through our clustering method, we were able to infer motifs that recovered nearly all members of most known ncRNA families. Hence, our method shows promise for discovering new families of ncRNA.

393 DECONVOLUTING THE COMPLEXITY OF LONG NON-CODING RNAS AND ESOPHAGEAL CARCINOMA FOR POTENTIAL CLINICAL IMPLICATIONS

2021 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Siyuan Luan ◽  
Yushang Yang ◽  
Shouyue Zhang ◽  
Xiaoxi Zeng ◽  
Xin Xiao ◽  
...  
Keyword(s):  
Esophageal Carcinoma ◽  
Rapid Development ◽  
Direct Interaction ◽  
Clinical Applications ◽  
Regulatory Mechanisms ◽  
Protein Coding ◽  
Functional Roles ◽  
Cancer Hallmarks ◽  
Clinical Scenarios ◽  
Non Coding Rnas

Abstract   Long non-coding RNAs (lncRNAs), a type of transcriptional products with more than 200 nucleotides in length, have been less characterized compared to protein-coding RNAs so far. However, it is increasingly evident that lncRNAs are key players involved in multiple genetic and epigenetic activities during the carcinogenesis of neoplastic diseases. Currently, accumulating data have pointed out the close connection between lncRNAs and esophageal carcinoma (EC), shedding light on further unravelling the complexity of lncRNAs and EC. Methods In this review, we thoroughly collect the evidence regarding original studies on EC-related lncRNAs by searching in MEDLINE/PubMed, Embase and WOS/SCI. We especially focus on summarizing EC-related lncRNAs based upon more updated evidence, and further discuss their different features from various perspectives, including regulatory mechanisms, functional roles in cancer hallmarks, as well as potential diagnostic and therapeutic applications, which would together reveal the complexity of lncRNAs and EC for potential clinical applications. Results We discuss over thirty EC-related lncRNAs in total, most of which function as oncogenes that promote cancer development, while the others function as tumor suppressors. Regulatory mechanisms included sponging miRNAs, direct interaction with proteins, and exosome visicle-based intercellular communication. Based upon these modes of actions, lncRNAs play multiple roles in cancer hallmarks such as uncontrolled cell growth, evasion of programmed cell death, invasion and metastasis. Moreover, lncRNAs packaged in exosomes have unique potency to serve as diagnostic biomarkers; some lncRNAs show great potential to predict patients' chemical resistance and may be crucial targets to improve chemoradiotherapy and targeted therapy. Conclusion Over the past few years, the research of EC-related lncRNAs maintain obviously rapid development, yet further exploration of exact mechanisms and clinical applications that lncRNAs can offer need to be done. Indeed, LncRNAs hold the promise of being applied in multiple clinical scenarios, especially early diagnosis of EC, improvement of sensitivity to chemotherapy/radiotherapy, and development of small-molecule targeted drugs.

scholarly journals RNA secondary structure prediction using deep learning with thermodynamic integration

2020 ◽  
Author(s):  
Kengo Sato ◽  
Manato Akiyama ◽  
Yasubumi Sakakibara
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Secondary Structures ◽  
Thermodynamic Integration ◽  
Rna Secondary Structure Prediction ◽  
Rna Secondary Structures ◽  
Non Coding Rnas

RNA secondary structure prediction is one of the key technologies for revealing the essential roles of functional non-coding RNAs. Although machine learning-based rich-parametrized models have achieved extremely high performance in terms of prediction accuracy, the risk of overfitting for such models has been reported. In this work, we propose a new algorithm for predicting RNA secondary structures that uses deep learning with thermodynamic integration, thereby enabling robust predictions. Similar to our previous work, the folding scores, which are computed by a deep neural network, are integrated with traditional thermodynamic parameters to enable robust predictions. We also propose thermodynamic regularization for training our model without overfitting it to the training data. Our algorithm (MXfold2) achieved the most robust and accurate predictions in computational experiments designed for newly discovered non-coding RNAs, with significant 2–10 % improvements over our previous algorithm (MXfold) and standard algorithms for predicting RNA secondary structures in terms of F-value.

U2 small nuclear RNA is remarkably conserved between Schizosaccharomyces pombe and mammals

1988 ◽  
Vol 8 (12) ◽  
pp. 5575-5580
Author(s):  
P Brennwald ◽  
G Porter ◽  
J A Wise
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Secondary Structure ◽  
Schizosaccharomyces Pombe ◽  
Small Nuclear Rna ◽  
Primary Sequence ◽  
Cloning And Sequencing ◽  
Sequence Identity ◽  
Human Counterpart ◽  
Nuclear Rna ◽  
Rna Blot Analysis

We report the molecular cloning and sequencing of the most abundant trimethylguanosine-capped small nuclear RNA from the fission yeast Schizosaccharomyces pombe, a highly conserved homolog of mammalian U2 small nuclear RNA. This RNA is 186 nucleotides in length, just 2 nucleotides shorter than its human counterpart; this is in contrast to Saccharomyces cerevisiae U2, which is 1,175 nucleotides long. Moreover, the secondary structure of Schizosaccharomyces pombe U2 is virtually identical to that of mammalian U2, including the 3' half of the RNA, which shows limited primary sequence identity. Northern (RNA) blot analysis revealed that the size of this RNA is conserved not only in fission yeasts but in many organisms, including other ascomycetes.

scholarly journals LncTarD: a manually-curated database of experimentally-supported functional lncRNA–target regulations in human diseases

2019 ◽  
Author(s):  
Hongying Zhao ◽  
Jian Shi ◽  
Yunpeng Zhang ◽  
Aimin Xie ◽  
Lei Yu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Sequence Data ◽  
Human Diseases ◽  
Regulatory Mechanisms ◽  
Biological Functions ◽  
Functional Dynamics ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
User Friendly ◽  
Pan Cancer

Abstract Long non-coding RNAs (lncRNAs) are associated with human diseases. Although lncRNA–disease associations have received significant attention, no online repository is available to collect lncRNA-mediated regulatory mechanisms, key downstream targets, and important biological functions driven by disease-related lncRNAs in human diseases. We thus developed LncTarD (http://biocc.hrbmu.edu.cn/LncTarD/ or http://bio-bigdata.hrbmu.edu.cn/LncTarD), a manually-curated database that provides a comprehensive resource of key lncRNA–target regulations, lncRNA-influenced functions, and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD offers (i) 2822 key lncRNA–target regulations involving 475 lncRNAs and 1039 targets associated with 177 human diseases; (ii) 1613 experimentally-supported functional regulations and 1209 expression associations in human diseases; (iii) important biological functions driven by disease-related lncRNAs in human diseases; (iv) lncRNA–target regulations responsible for drug resistance or sensitivity in human diseases and (v) lncRNA microarray, lncRNA sequence data and transcriptome data of an 11 373 pan-cancer patient cohort from TCGA to help characterize the functional dynamics of these lncRNA–target regulations. LncTarD also provides a user-friendly interface to conveniently browse, search, and download data. LncTarD will be a useful resource platform for the further understanding of functions and molecular mechanisms of lncRNA deregulation in human disease, which will help to identify novel and sensitive biomarkers and therapeutic targets.

scholarly journals Bioinformatic and Physical Characterizations of Genome-Scale Ordered RNA Structure in Mammalian RNA Viruses

2008 ◽  
Vol 82 (23) ◽  
pp. 11824-11836 ◽  
Author(s):  
Matthew Davis ◽  
Selena M. Sagan ◽  
John P. Pezacki ◽  
David J. Evans ◽  
Peter Simmonds
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Large Scale ◽  
Rna Viruses ◽  
Plant Viruses ◽  
Large Set ◽  
Conserved Sequence ◽  
Evolutionarily Conserved ◽  
Genome Scale

ABSTRACT By the analysis of thermodynamic RNA secondary structure predictions, we previously obtained evidence for evolutionarily conserved large-scale ordering of RNA virus genomes (P. Simmonds, A. Tuplin, and D. J. Evans, RNA 10:1337-1351, 2004). Genome-scale ordered RNA structure (GORS) was widely distributed in many animal and plant viruses, much greater in extent than RNA structures required for viral translation or replication, but in mammalian viruses was associated with host persistence. To substantiate the existence of large-scale RNA structure differences between viruses, a large set of alignments of mammalian RNA viruses and rRNA sequences as controls were examined by thermodynamic methods (to calculate minimum free energy differences) and by algorithmically independent RNAz and Pfold methods. These methods produced generally concordant results and identified substantial differences in the degrees of evolutionarily conserved, sequence order-dependent RNA secondary structure between virus genera and groups. A probe hybridization accessibility assay was used to investigate the physical nature of GORS. Transcripts of hepatitis C virus (HCV), hepatitis G virus/GB virus-C (HGV/GBV-C), and murine norovirus, which are predicted to be structured, were largely inaccessible to hybridization in solution, in contrast to the almost universal binding of probes to a range of unstructured virus transcripts irrespective of G+C content. Using atomic force microscopy, HCV and HGV/GBV-C RNA was visualized as tightly compacted prolate spheroids, while under the same experimental conditions the predicted unstructured poliovirus and rubella virus RNA were pleomorphic and had extensively single-stranded RNA on deposition. Bioinformatic and physical characterization methods both identified fundamental differences in the configurations of viral genomic RNA that may modify their interactions with host cell defenses and their ability to persist.

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