scholarly journals A General Computational Framework for Prediction of Disease-associated Non-coding RNAs

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Duc-Hau Le
Keyword(s):  
Computational Methods ◽  
Computational Models ◽  
Base Pairs ◽  
Computational Framework ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Small Set ◽  
Computational Aspects ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Since last decade, we have been witnessing the raise of non-coding RNAs (ncRNAs) in biomedical research. Many ncRNAs have been identified and classified into different classes based on their length in number of base pairs (bp). In parallel, our understanding about functions of ncRNAs is gradually increased. However, only small set among tens of thousands of ncRNAs have been well studied about their functions and their roles in development of diseases. This raises a pressing need to develop computational methods to associate diseases and ncRNAs. Two most widely studied ncRNAs are microRNA (miRNA) and long non-coding RNA (lncRNA), since miRNAs are the regulators of most protein-coding genes and lncRNAs are the most ubiquitously found in mammalian. To date, many computational methods have been also proposed for prediction of disease-associated miRNAs and lncRNAs, and recently comprehensively reviewed. However, in the previous reviews, these computational methods were described separately, thus this limits our understanding about their underlying computational aspects. Therefore, in this study, we propose a general computational framework for prediction of disease-associated ncRNAs. The framework demonstrates a whole computational process from data preparation to computational models.

scholarly journals Long Non-Coding RNA-Ribonucleoprotein Networks in the Post-Transcriptional Control of Gene Expression

2020 ◽  
Vol 6 (3) ◽  
pp. 40
Author(s):  
Paola Briata ◽  
Roberto Gherzi
Keyword(s):  
Transcriptional Control ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Huge Number ◽  
Protein Coding ◽  
Control Of Gene Expression ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
And Function ◽  
Long Non Coding Rna

Although mammals possess roughly the same number of protein-coding genes as worms, it is evident that the non-coding transcriptome content has become far broader and more sophisticated during evolution. Indeed, the vital regulatory importance of both short and long non-coding RNAs (lncRNAs) has been demonstrated during the last two decades. RNA binding proteins (RBPs) represent approximately 7.5% of all proteins and regulate the fate and function of a huge number of transcripts thus contributing to ensure cellular homeostasis. Transcriptomic and proteomic studies revealed that RBP-based complexes often include lncRNAs. This review will describe examples of how lncRNA-RBP networks can virtually control all the post-transcriptional events in the cell.

The indirect antiviral potential of long non-coding RNAs encoded by IFITM pseudogenes

2021 ◽  
Author(s):  
Kazi Rahman ◽  
Alex A. Compton
Keyword(s):  
Influenza A ◽  
Virus Entry ◽  
Gene Families ◽  
Mrna Levels ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rna ◽  
Selective Forces ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

The interferon-induced transmembrane ( IFITM ) family performs multiple functions in immunity, including inhibition of virus entry into cells. The IFITM repertoire varies widely between species and consists of protein-coding genes and pseudogenes. The selective forces driving pseudogenization within gene families are rarely understood. In this issue, the human pseudogene IFITM4P is characterized as a virus-induced, long non-coding RNA that contributes to restriction of Influenza A virus by regulating mRNA levels of IFITM1 , IFITM2 , and IFITM3 .

Long non-coding RNAs and splicing

2021 ◽  
Author(s):  
David Staněk
Keyword(s):  
Dominant Role ◽  
Splice Sites ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rna ◽  
Splicing Efficiency ◽  
Non Coding Rnas ◽  
Intron Removal ◽  
Long Non Coding Rna

Abstract In this review I focus on the role of splicing in long non-coding RNA (lncRNA) life. First, I summarize differences between the splicing efficiency of protein-coding genes and lncRNAs and discuss why non-coding RNAs are spliced less efficiently. In the second half of the review, I speculate why splice sites are the most conserved sequences in lncRNAs and what additional roles could splicing play in lncRNA metabolism. I discuss the hypothesis that the splicing machinery can, besides its dominant role in intron removal and exon joining, protect cells from undesired transcripts.

scholarly journals Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

2019 ◽  
Vol 21 (2) ◽  
pp. 637-648 ◽  
Author(s):  
Aritro Nath ◽  
Paul Geeleher ◽  
R Stephanie Huang
Keyword(s):  
Expression Patterns ◽  
Imputation Accuracy ◽  
High Quality ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Cancer Tissues ◽  
Long Non Coding Rna

Abstract Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

scholarly journals Gestational Age Dependence of the Maternal Circulating Long Non-Coding RNA Transcriptome During Normal Pregnancy Highlights Antisense and Pseudogene Transcripts

2021 ◽  
Vol 12 ◽  
Author(s):  
Erica L. Kleinbrink ◽  
Nardhy Gomez-Lopez ◽  
Donghong Ju ◽  
Bogdan Done ◽  
Anton-Scott Goustin ◽  
...  
Keyword(s):  
Human Genome ◽  
Normal Pregnancy ◽  
Differentially Expressed ◽  
Cajal Body ◽  
Protein Coding ◽  
Longitudinal Changes ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Long Non Coding Rna ◽  
In Pregnancy

In the post-genomic era, our understanding of the molecular regulators of physiologic and pathologic processes in pregnancy is expanding at the whole-genome level. Longitudinal changes in the known protein-coding transcriptome during normal pregnancy, which we recently reported (Gomez-Lopez et al., 2019), have improved our definition of the major operant networks, yet pregnancy-related functions of the non-coding RNA transcriptome remain poorly understood. A key finding of the ENCODE (Encyclopedia of DNA Elements) Consortium, the successor of the Human Genome Project, was that the human genome contains approximately 60,000 genes, the majority of which do not encode proteins. The total transcriptional output of non-protein-coding RNA genes, collectively referred to as the non-coding transcriptome, is comprised mainly of long non-coding RNA (lncRNA) transcripts (Derrien et al., 2012). Although the ncRNA transcriptome eclipses its protein-coding counterpart in abundance, it has until recently lacked a comprehensive, unbiased, genome-scale characterization over the timecourse of normal human pregnancy. Here, we annotated, characterized, and selectively validated the longitudinal changes in the non-coding transcriptome of maternal whole blood during normal pregnancy to term. We identified nine long non-coding RNAs (lncRNAs), including long intergenic non-coding RNAs (lincRNAs) as well as lncRNAs antisense to or otherwise in the immediate vicinity of protein-coding genes, that were differentially expressed with advancing gestation in normal pregnancy: AL355711, BC039551 (expressed mainly in the placenta), JHDM1D-AS1, A2M-AS1, MANEA-AS1, NR_034004, LINC00649, LINC00861, and LINC01094. By cross-referencing our dataset against major public pseudogene catalogs, we also identified six transcribed pseudogenes that were differentially expressed over time during normal pregnancy in maternal blood: UBBP4, FOXO3B, two Makorin (MKRN) pseudogenes (MKRN9P and LOC441455), PSME2P2, and YBX3P1. We also identified three non-coding RNAs belonging to other classes that were modulated during gestation: the microRNA MIR4439, the small nucleolar RNA (snoRNA) SNORD41, and the small Cajal-body specific ncRNA SCARNA2. The expression profiles of most hits were broadly suggestive of functions in pregnancy. These time-dependent changes of the non-coding transcriptome during normal pregnancy, which may confer specific regulatory impacts on their protein-coding gene targets, will facilitate a deeper molecular understanding of pregnancy and lncRNA-mediated molecular pathways at the maternal-fetal interface and of how these pathways impact maternal and fetal health.

scholarly journals Volatile Evolution of Long Non-Coding RNA Repertoire in Retinal Pigment Epithelium: Insights from Comparison of Bovine and Human RNA Expression Profiles

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 205 ◽  
Author(s):  
Olga A. Postnikova ◽  
Igor B. Rogozin ◽  
William Samuel ◽  
German Nudelman ◽  
Vladimir N. Babenko ◽  
...  
Keyword(s):  
Retinal Pigment Epithelium ◽  
Expression Profiles ◽  
Pigment Epithelium ◽  
Retinal Pigment ◽  
Non Coding Rna ◽  
Small Set ◽  
Flexible Adaptation ◽  
Non Coding Rnas ◽  
Whole Genome Alignment ◽  
Long Non Coding Rna

Currently, several long non-coding RNAs (lncRNAs) (TUG1, MALAT1, MEG3 and others) have been discovered to regulate normal visual function and may potentially contribute to dysfunction of the retina. We decided to extend these analyses of lncRNA genes to the retinal pigment epithelium (RPE) to determine whether there is conservation of RPE-expressed lncRNA between human and bovine genomes. We reconstructed bovine RPE lncRNAs based on genome-guided assembly. Next, we predicted homologous human transcripts based on whole genome alignment. We found a small set of conserved lncRNAs that could be involved in signature RPE functions that are conserved across mammals. However, the fraction of conserved lncRNAs in the overall pool of lncRNA found in RPE appeared to be very small (less than 5%), perhaps reflecting a fast and flexible adaptation of the mammalian eye to various environmental conditions.

scholarly journals Exosomal Long Non-Coding RNA: Interaction Between Cancer Cells and Non-Cancer Cells

2021 ◽  
Vol 10 ◽  
Author(s):  
Shenqi Han ◽  
Yongqiang Qi ◽  
Yiming Luo ◽  
Xiaoping Chen ◽  
Huifang Liang
Keyword(s):  
Cancer Cells ◽  
Cell Communication ◽  
Deep Understanding ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Fundamental Biology ◽  
Non Coding Rnas ◽  
Limited Protein ◽  
Rna Interaction ◽  
Long Non Coding Rna

Exosomes are small membranous vesicles released by many kinds of cells, and are indispensable in cell-to-cell communication by delivering functional biological components both locally and systemically. Long non-coding RNAs (lncRNAs) are long transcripts over 200 nucleotides that exhibit no or limited protein-coding potentials. LncRNAs are dramatic gene expression regulators, and can be selectively sorted into exosomes. Exosomal lncRNAs derived from cancer cells and stromal cells can mediate the generation of pre-metastatic niches (PMNs) and thus promote the progression of cancer. In this review, we summarized the fundamental biology and characteristics of exosomal lncRNAs. Besides, we provided an overview of current research on functions of exosomal lncRNAs between cancer cells and non-cancer cells. A deep understanding of exosomal lncRNAs’ role in cancer will be facilitated to find important implications for cancer development and treatment.

scholarly journals Long Non-Coding RNAs in Biliary Tract Cancer—An Up-to-Date Review

2020 ◽  
Vol 9 (4) ◽  
pp. 1200 ◽  
Author(s):  
Dino Bekric ◽  
Daniel Neureiter ◽  
Markus Ritter ◽  
Martin Jakab ◽  
Martin Gaisberger ◽  
...  
Keyword(s):  
Biliary Tract ◽  
Biliary Tract Cancer ◽  
Base Pairs ◽  
Aberrant Expression ◽  
Protein Coding ◽  
Tract Cancer ◽  
Non Coding Rna ◽  
Cancer Types ◽  
Regulatory Functions ◽  
Long Non Coding Rna

The term long non-coding RNA (lncRNA) describes non protein-coding transcripts with a length greater than 200 base pairs. The ongoing discovery, characterization and functional categorization of lncRNAs has led to a better understanding of the involvement of lncRNAs in diverse biological and pathological processes including cancer. Aberrant expression of specific lncRNA species was demonstrated in various cancer types and associated with unfavorable clinical characteristics. Recent studies suggest that lncRNAs are also involved in the development and progression of biliary tract cancer, a rare disease with high mortality and limited therapeutic options. In this review, we summarize current findings regarding the manifold roles of lncRNAs in biliary tract cancer and give an overview of the clinical and molecular consequences of aberrant lncRNA expression as well as of underlying regulatory functions of selected lncRNA species in the context of biliary tract cancer.

scholarly journals Long non-coding RNA LINC00485 acts as a microRNA-195 sponge to regulate the chemotherapy sensitivity of lung adenocarcinoma cells to cisplatin by regulating CHEK1

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wei Zuo ◽  
Wei Zhang ◽  
Fei Xu ◽  
Jing Zhou ◽  
Wei Bai
Keyword(s):  
Lung Adenocarcinoma ◽  
Protein Coding ◽  
Chemotherapy Sensitivity ◽  
Over Expression ◽  
Non Coding Rna ◽  
Adenocarcinoma Cells ◽  
Non Coding Rnas ◽  
Long Non Coding Rna ◽  
Competitive Endogenous Rna

Abstract Background Long non-coding RNAs (lncRNAs) are a family of non-protein-coding RNAs, which have the ability to influence the chemo-resistance of lung adenocarcinoma (LAC). In this study, we explored the mechanism by which LINC00485 competitively binds to microRNA-195 (miR-195) in the regulation of the chemotherapy sensitivity in LAC by regulating checkpoint kinase 1 (CHEK1). Methods Microarray analysis was used to screen out LAC related genes, and interaction between CHEK1 and miR-195, as well as that between miR-195 and LINC00485, was further confirmed by RNA-pull down and RIP. LINC00485 expression in LAC cells (A549 and H1299) was determined. The cells were then introduced with miR-195, anta-miR-195, LINC00485 or si-LINC00485 to identify the role of miR-195 and LINC00485 in LAC through evaluating the expression of CHEK1, CHEK1, Bax, Bcl-2, VEGF and HIF-1α in LAC cells by either RT-qPCR or Western blot analysis. After being treated with different concentration of cisplatin, cell proliferation, colony formation and apoptosis were assessed. Results LINC00485 acted as a competitive endogenous RNA against miR-195, and miR-195 directly targeted CHEK1. The expression of LINC00485 was higher in LAC cells. The down-regulation of LINC00485 or the up-regulation of miR-195 decreased the expression of CHEK1, Bcl-2, VEGF and HIF-1α, while also increasing the expression of Bax. Moreover, the over-expression of miR-195, or the silencing of LINC00485 enhanced the sensitivity of LAC cells to cisplatin, thereby promoting the apoptosis of LAC cells while suppressing the proliferation. Conclusion LINC00485 competitively binds to miR-195 to elevate CHEK1 expression in LAC cells, suggesting that LINC00485 is a novel direction for therapeutic strategies of LAC.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Sign in / Sign up

Export Citation Format

Share Document