scholarly journals A conserved long intergenic non-coding RNA containing snoRNA sequences, lncCOBRA1, affects Arabidopsis germination and development

2021 ◽  
Author(s):  
Marianne C Kramer ◽  
Hee Jong Kim ◽  
Kyle R Palos ◽  
Benjamin A Garcia ◽  
Eric Lyons ◽  
...  
Keyword(s):  
Brassica Rapa ◽  
Interaction Network ◽  
Crop Species ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
High Level ◽  
Protein Protein Interaction Network ◽  
Mammalian System

Long non-coding RNAs (lncRNAs) are an increasingly studied group of non-protein-coding transcripts with a wide variety of molecular functions gaining attention for their roles in numerous biological processes. Nearly 6,000 lncRNAs have been identified in Arabidopsis thaliana but many have yet to be studied. Here, we examine a class of previously uncharacterized lncRNAs termed CONSERVED IN BRASSICA RAPA (lncCOBRA) transcripts that were previously identified for their high level of sequence conservation in the related crop species Brassica rapa, their nuclear-localization and protein-bound nature. In particular, we focus on lncCOBRA1 and demonstrate that its abundance is highly tissue and developmental specific, with particularly high levels early in germination. lncCOBRA1 contains two snoRNAs domains within it, making it the first sno-lincRNA example in a non-mammalian system. However, we find that it is processed differently than its mammalian counterparts. We further show that plants lacking lncCOBRA1 display patterns of delayed gemination and are overall smaller than wild-type plants. Lastly, we identify the proteins that interact with lncCOBRA1 and examine the protein-protein interaction network of lncCOBRA1-interacting proteins.

Construction and analysis of mRNA and lncRNA regulatory networks reveal the key genes associated with prostate cancer related fatigue during localized radiation therapy

2020 ◽  
Vol 15 ◽  
Author(s):  
Yechen Wu ◽  
Yaping Gui ◽  
Denglong Wu ◽  
Qiang Wu
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Cancer Related Fatigue ◽  
Non Coding Rnas ◽  
New Biomarkers ◽  
Protein Protein Interaction Network

Background: Localized radiation therapy is the first line option for the treatment of non-metastatic prostate cancer (PCa). Previous studies revealed long non-coding RNAs (lncRNAs) had crucial roles in diseases progression. However, the mechanisms of lncRNAs underlying prostate cancer related fatigue remained largely unclear. Objective: The present study aimed to uncover the hub genes related to PCa related fatigue during localized radiation therapy by constructing mRNA and lncRNA regulatory networks. Methods: We analyzed GSE30174, which included 10 control samples and 40 PCa related fatigue samples, to identify differently expressed lncRNAs and mRNAs in PCa related fatigue. Protein-protein interaction network was constructed to uncover the interactions among mRNAs. Co-expression network analysis was appled to identify the key lncRNAs and reveal the functions of these lncRNAs in PCa related fatigue. Results and Discussion: This research found 1271 dysregulated mRNAs and 205 dysregulated lncRNAs in PCa related fatigue using GSE30174. Bioinformatics analysis showed PCa related fatigue related mRNAs and lncRNAs were associated with inflammatory response and immune response related biological processes. Furthermore, we constructed PPI network and lncRNA co-expression network related to fatigue in PCa. Of note, we observed the dysregulated lncRNAs and mRNAs, such as SEC61A2, ADCY6, LPAR5, COL7A1, ALB, COL1A1, SNHG1, LINC01215, LINC00926, GNG4, LMO7, and COL4A6, in PCa related fatigue could predict the outcome of PCa patients. Conclusions: This research could provide novel mechanisms underlying fatigue and identify new biomarkers for the prognosis of PCa.

scholarly journals A learning-based framework for miRNA-disease association identification using neural networks

2019 ◽  
Vol 35 (21) ◽  
pp. 4364-4371 ◽  
Author(s):  
Jiajie Peng ◽  
Weiwei Hui ◽  
Qianqian Li ◽  
Bolin Chen ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Essential Feature ◽  
Interaction Network ◽  
Disease Association ◽  
Feature Representation ◽  
Supplementary Information ◽  
Similarity Network ◽  
Protein Protein Interaction ◽  
Non Coding Rna ◽  
Disease Similarity ◽  
Protein Protein Interaction Network

Abstract Motivation A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes. Results We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction. Availability and implementation The source code and data are available at https://github.com/Issingjessica/MDA-CNN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals circDeep: deep learning approach for circular RNA classification from other long non-coding RNA

2019 ◽  
Vol 36 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Mohamed Chaabane ◽  
Robert M Williams ◽  
Austin T Stephens ◽  
Juw Won Park
Keyword(s):  
Deep Learning ◽  
Circular Rna ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Gene Transcripts ◽  
Non Coding Rnas ◽  
Increase In Accuracy ◽  
High Level

Abstract Motivation Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation. Results Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA from other lncRNA. circDeep fuses an RCM descriptor, ACNN-BLSTM sequence descriptor and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that circDeep is not only faster than existing tools but also performs at an unprecedented level of accuracy by achieving a 12 percent increase in accuracy over the other tools. Availability and implementation https://github.com/UofLBioinformatics/circDeep. Supplementary information Supplementary data are available at Bioinformatics online.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

scholarly journals Diabetic Retinopathy and Laser Therapy in Rats: A Protein-Protein Interaction Network Analysis

2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Laser Therapy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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