Relevant bibliographies by topics / Long non-coding RNAs (IncRNAs)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Long non-coding RNAs (IncRNAs)'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Long non-coding RNAs (IncRNAs)"

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Wang, Li, Zhenhong Chen, Li An, Yajuan Wang, Zhijian Zhang, Yinghua Guo, and Changting Liu. "Analysis of Long Non-Coding RNA Expression Profiles in Non-Small Cell Lung Cancer." Cellular Physiology and Biochemistry 38, no. 6 (2016): 2389–400. http://dx.doi.org/10.1159/000445591.

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Background/Aims: Long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. However, the role of lncRNA expression in human Non-small cell lung cancer (NSCLC) biology, prognosis and molecular classification remains unknown. Methods: We established the IncRNA profile in NSCLC by re-annotation of microarrays from the Gene expression omnibus database. Quantitative real-time PCR was used to determine expression of LINC00342. Results: 6066 differentially expressed IncRNAs were identified and we found a novel IncRNA, LINC00342 was significantly up-regulated in NSCLC tissues compared with normal tissues. We confirmed the over-expression of LINC00342 in a cohort of NSCLC patients and found LINC00342 expression level was positively correlated with lymph node metastasis and TNM stages. Furthermore, in a large online database of 1942 NSCLC patients, high expression of LINC00342 indicated poor Overall survival (HR = 1.28, 95% CI: 1.13-1.45) and post progression survival (HR = 1.43, 95% CI: 1.09-1.88). Bioinformatics analyses showed that LINC00342 was co-expressed with different protein-coding genes in NSCLC and normal tissues. Additionally, gene set enrichment analyses found that PTEN and P53 pathways genes were enriched in the groups with higher LINC00342 expression level. By small interfering RNAs mediated silence of LINC00342, proliferation ability was significantly inhibited in lung cancer cell line. Conclusion: To summary, our findings indicate that a set of IncRNAs are differentially expressed in NSCLC and we characterized a novel IncRNA, LINC00342 which is significantly up-regulated in NSCLC and could be a prognostic biomarker.
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Rothzerg, Emel, Xuan Dung Ho, Jiake Xu, David Wood, Aare Märtson, and Sulev Kõks. "Upregulation of 15 Antisense Long Non-Coding RNAs in Osteosarcoma." Genes 12, no. 8 (July 26, 2021): 1132. http://dx.doi.org/10.3390/genes12081132.

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The human genome encodes thousands of natural antisense long noncoding RNAs (lncRNAs); they play the essential role in regulation of gene expression at multiple levels, including replication, transcription and translation. Dysregulation of antisense lncRNAs plays indispensable roles in numerous biological progress, such as tumour progression, metastasis and resistance to therapeutic agents. To date, there have been several studies analysing antisense lncRNAs expression profiles in cancer, but not enough to highlight the complexity of the disease. In this study, we investigated the expression patterns of antisense lncRNAs from osteosarcoma and healthy bone samples (24 tumour-16 bone samples) using RNA sequencing. We identified 15 antisense lncRNAs (RUSC1-AS1, TBX2-AS1, PTOV1-AS1, UBE2D3-AS1, ERCC8-AS1, ZMIZ1-AS1, RNF144A-AS1, RDH10-AS1, TRG-AS1, GSN-AS1, HMGA2-AS1, ZNF528-AS1, OTUD6B-AS1, COX10-AS1 and SLC16A1-AS1) that were upregulated in tumour samples compared to bone sample controls. Further, we performed real-time polymerase chain reaction (RT-qPCR) to validate the expressions of the antisense lncRNAs in 8 different osteosarcoma cell lines (SaOS-2, G-292, HOS, U2-OS, 143B, SJSA-1, MG-63, and MNNG/HOS) compared to hFOB (human osteoblast cell line). These differentially expressed IncRNAs can be considered biomarkers and potential therapeutic targets for osteosarcoma.
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Zong, Zhen, Hui Li, Zhuo-Min Yu, Fu-Xin Tang, Xiao-Jian Zhu, Hua-Kai Tian, Tai-Cheng Zhou, and He Wang. "Prognostic thirteen-long non-coding RNAs (IncRNAs) could improve the survival prediction of gastric cancer." Gastroenterología y Hepatología 43, no. 10 (December 2020): 598–606. http://dx.doi.org/10.1016/j.gastrohep.2020.01.016.

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Zong, Zhen, Hui Li, Zhuo-Min Yu, Fu-Xin Tang, Xiao-Jian Zhu, Hua-Kai Tian, Tai-Cheng Zhou, and He Wang. "Prognostic thirteen-long non-coding RNAs (IncRNAs) could improve the survival prediction of gastric cancer." Gastroenterología y Hepatología (English Edition) 43, no. 10 (December 2020): 598–606. http://dx.doi.org/10.1016/j.gastre.2020.01.019.

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Butova, Romana, Petra Vychytilova-Faltejskova, Adela Souckova, Sabina Sevcikova, and Roman Hajek. "Long Non-Coding RNAs in Multiple Myeloma." Non-Coding RNA 5, no. 1 (January 24, 2019): 13. http://dx.doi.org/10.3390/ncrna5010013.

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Multiple myeloma (MM) is the second most common hematooncological disease of malignant plasma cells in the bone marrow. While new treatment brought unprecedented increase of survival of patients, MM pathogenesis is yet to be clarified. Increasing evidence of expression of long non-coding RNA molecules (lncRNA) linked to development and progression of many tumors suggested their important role in tumorigenesis. To date, over 15,000 lncRNA molecules characterized by diversity of function and specificity of cell distribution were identified in the human genome. Due to their involvement in proliferation, apoptosis, metabolism, and differentiation, they have a key role in the biological processes and pathogenesis of many diseases, including MM. This review summarizes current knowledge of non-coding RNAs (ncRNA), especially lncRNAs, and their role in MM pathogenesis. Undeniable involvement of lncRNAs in MM development suggests their potential as biomarkers.
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Levakov, S. A., G. Ya Azadova, A. E. Mamedova, Kh R. Movtaeva, M. I. Maslyakova, M. S. Pavlyukov, M. I. Shakhparonov, and N. V. Antipova. "Expression of long non-coding RNAs ROR and MALAT1 in uterine fibroids." Voprosy ginekologii, akušerstva i perinatologii 20, no. 4 (2021): 17–21. http://dx.doi.org/10.20953/1726-1678-2021-4-17-21.

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Objective. To study the expression level of long non-coding RNAs ROR and MALAT1 in tissue samples of uterine fibroids. Patients and methods. Samples of myomatous nodes and tissues of normal myometrium in 28 women of reproductive age were examined. The analysis of the expression of long non-coding RNAs was carried out using a real-time reverse-transcription polymerase chain reaction (RT-PCR) with specific primers. Results. There was a significant decrease in the expression level of long non-coding RNA ROR and an increase in the MALAT1 expression in tissue samples of uterine fibroids relative to the control group. Conclusion. The results obtained demonstrate a possible role of long non-coding RNAs in the development of uterine fibroids and correlate with the data which we obtained for patients with endometriosis. Detecting the expression level of long non-coding RNAs can improve the existing methods for diagnosing this disease. However, further research is required to determine the clinical significance of MALAT1 and ROR, and the molecular mechanisms underlying the action of these RNAs in uterine fibroid cells. Key words: long non-coding RNAs, uterine fibroids, myomectomy, lncROR, MALAT1
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Dhingra, Sourabh. "Role of Non-coding RNAs in Fungal Pathogenesis and Antifungal Drug Responses." Current Clinical Microbiology Reports 7, no. 4 (October 2, 2020): 133–41. http://dx.doi.org/10.1007/s40588-020-00151-7.

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Abstract Purpose of Review Non-coding RNAs (ncRNAs), including regulatory small RNAs (sRNAs) and long non-coding RNAs (lncRNAs), constitute a significant part of eukaryotic genomes; however, their roles in fungi are just starting to emerge. ncRNAs have been shown to regulate gene expression in response to varying environmental conditions (like stress) and response to chemicals, including antifungal drugs. In this review, I highlighted recent studies focusing on the functional roles of ncRNAs in pathogenic fungi. Recent Findings Emerging evidence suggests sRNAs (small RNAs) and lncRNAs (long non-coding RNAs) play an important role in fungal pathogenesis and antifungal drug response. Their roles include posttranscriptional gene silencing, histone modification, and chromatin remodeling. Fungal pathogens utilize RNA interference (RNAi) mechanisms to regulate pathogenesis-related genes and can also transfer sRNAs inside the host to suppress host immunity genes to increase virulence. Hosts can also transfer sRNAs to induce RNAi in fungal pathogens to reduce virulence. Additionally, sRNAs and lncRNAs also regulate gene expression in response to antifungal drugs increasing resistance (and possibly tolerance) to drugs. Summary Herein, I discuss what is known about ncRNAs in fungal pathogenesis and antifungal drug responses. Advancements in genomic technologies will help identify the ncRNA repertoire in fungal pathogens, and functional studies will elucidate their mechanisms. This will advance our understanding of host-fungal interactions and potentially help develop better treatment strategies.
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Zhang, Zhuo, Sophia Shi, Jingxia Li, and Max Costa. "Long Non-Coding RNA MEG3 in Metal Carcinogenesis." Toxics 11, no. 2 (February 7, 2023): 157. http://dx.doi.org/10.3390/toxics11020157.

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Most transcripts from human genomes are non-coding RNAs (ncRNAs) that are not translated into proteins. ncRNAs are divided into long (lncRNAs) and small non-coding RNAs (sncRNAs). LncRNAs regulate their target genes both transcriptionally and post-transcriptionally through interactions with proteins, RNAs, and DNAs. Maternally expressed gene 3 (MEG3), a lncRNA, functions as a tumor suppressor. MEG3 regulates cell proliferation, cell cycle, apoptosis, hypoxia, autophagy, and many other processes involved in tumor development. MEG3 is downregulated in various cancer cell lines and primary human cancers. Heavy metals, such as hexavalent chromium (Cr(VI)), arsenic, nickel, and cadmium, are confirmed human carcinogens. The exposure of cells to these metals causes a variety of cancers. Among them, lung cancer is the one that can be induced by exposure to all of these metals. In vitro studies have demonstrated that the chronic exposure of normal human bronchial epithelial cells (BEAS-2B) to these metals can cause malignant cell transformation. Metal-transformed cells have the capability to cause an increase in cell proliferation, resistance to apoptosis, elevated migration and invasion, and properties of cancer stem-like cells. Studies have revealed that MEG is downregulated in Cr(VI)-transformed cells, nickel-transformed cells, and cadmium (Cd)-transformed cells. The forced expression of MEG3 reduces the migration and invasion of Cr(VI)-transformed cells through the downregulation of the neuronal precursor of developmentally downregulated protein 9 (NEDD9). MEG3 suppresses the malignant cell transformation of nickel-transformed cells. The overexpression of MEG3 decreases Bcl-xL, causing reduced apoptosis resistance in Cd-transformed cells. This paper reviews the current knowledge of lncRNA MEG3 in metal carcinogenesis.
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Dragomir, Mihnea Paul, Scott Kopetz, Jaffer A. Ajani, and George Adrian Calin. "Non-coding RNAs in GI cancers: from cancer hallmarks to clinical utility." Gut 69, no. 4 (February 7, 2020): 748–63. http://dx.doi.org/10.1136/gutjnl-2019-318279.

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One of the most unexpected discoveries in molecular oncology, in the last decades, was the identification of a new layer of protein coding gene regulation by transcripts that do not codify for proteins, the non-coding RNAs. These represent a heterogeneous category of transcripts that interact with many types of genetic elements, including regulatory DNAs, coding and other non-coding transcripts and directly to proteins. The final outcome, in the malignant context, is the regulation of any of the cancer hallmarks. Non-coding RNAs represent the most abundant type of hormones that contribute significantly to cell-to cell communication, revealing a complex interplay between tumour cells, tumour microenvironment cells and immune cells. Consequently, profiling their abundance in bodily fluids became a mainstream of biomarker identification. Therapeutic targeting of non-coding RNAs represents a new option for clinicians that is currently under development. This review will present the biology and translational value of three of the most studied categories on non-coding RNAs, the microRNAs, the long non-coding RNAs and the circular RNAs. We will also focus on some aspirational concepts that can help in the development of clinical applications related to non-coding RNAs, including using pyknons to discover new non-coding RNAs, targeting human-specific transcripts which are expressed specifically in the tumour cell and using non-coding RNAs to increase the efficiency of immunotherapy.
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Chaabane, Mohamed, Robert M. Williams, Austin T. Stephens, and Juw Won Park. "circDeep: deep learning approach for circular RNA classification from other long non-coding RNA." Bioinformatics 36, no. 1 (July 3, 2019): 73–80. http://dx.doi.org/10.1093/bioinformatics/btz537.

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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.
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Dissertations / Theses on the topic "Long non-coding RNAs (IncRNAs)"

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Molina, Elsa. "An investigation into the relationships between novel Y chromosome-linked long non-coding RNAs and coronary artery disease." Thesis, Federation University Australia, 2016. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/102986.

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Coronary artery disease (CAD) is the most common type of cardiovascular disease and is one of the leading causes of morbidity and mortality globally. However, the pathogenesis of atherosclerosis which leads to CAD and results in heart attacks, heart failure and death is not well understood. In this context, studies have demonstrated a positive correlation between increased hepatic free fatty acids (FFAs) in atherosclerosis and CAD. Although CAD has welldefined environmental risk factors, genome-wide association studies (GWAS) have demonstrated a genetic influence on CAD. Previous studies have shown that genetic variation within the human Y chromosome is associated with an increased risk of developing CAD independent of traditional cardiovascular risk factors; possibly through a modulating effect of an adaptive immunity and inflammatory response by macrophages in men. However, no Y chromosome-linked gene has been investigated in this disease so far. Long non-coding RNAs (lncRNAs) have recently gained focused attention as a new class of regulatory RNA molecules involved in cardiovascular function and associated disease, particularly long intergenic noncoding RNAs (lincRNAs), the largest class within the lncRNA group so far. To date, Y chromosome-linked lincRNAs are poorly characterised and the potential link between these non-coding RNA molecules and CAD in men has not been investigated. In this context, I hypothesised that Y chromosome-linked lncRNAs may regulate pathways involved in lipid metabolism and trigger an over accumulation of FFAs in coronary arteries contributing to atherosclerosis, the underlying cause of CAD. The main objective of this thesis was to therefore further investigate the relationship between the Y chromosome, lncRNAs and CAD in light of the deficiencies within the literature to better understand the causative molecular mechanisms of CAD pathophysiology in men. In my first study (Chapter 2), I identified for the first time through gene expression analysis (real-time PCR) the expression of the following (unannotated in PubMed) Y chromosomelinked lincRNA transcripts: lnc-KDM5D-4:1, lnc-ZFY-1:1, lnc-ZFY-1:3, lnc-ZFY-2:1, lnc- RBMY1B-1:1, lnc-RBMY1B-1:4, lnc-RBMY1J-1:1, lnc-RBMY1J-1:2, and lnc-RBMY1J- 1:3, across 21 different normal, human tissues such as adipose, bladder, brain, cervix, colon, esophagus, heart, kidney, liver, lung, ovary, placenta, prostate, skeletal muscle, small intestine, spleen, testes, thymus, thyroid, trachea, and white blood cells (WBCs) (leukocytes). I found that Y-linked lincRNAs were expressed at low levels (with the lowest CT number equal at 24.5) with a high tissue-specificity for some. Also, the Y-linked RNA gene lnc-KDM5D-4 was widely expressed across male tissues while the Y-linked RNA gene lnc-RBMY1J-1 was specific to the testes. Furthermore, this study presents the first evidence through gene expression analysis that the Y chromosome-linked lincRNA transcripts, lnc-KDM5D-4:1, lnc- ZFY-1:1, lnc-ZFY-1:3, lnc-ZFY-2:1, lnc-RBMY1B-1:1, lnc-RBMY1B-1:4, and lnc- RBMY1J-1:3 are expressed in male leukocytes. Hence, these lincRNAs could be potential non-protein coding gene candidates for CAD research. Knowing that the Y chromosome contributes to lipid levels in humans, to further explore the potential function of these Y-linked lincRNAs in CAD in men, I then studied their expression in a fatty liver context (steatosis-associated atherosclerosis) (Chapter 3). This was performed using the human hepatocellular liver carcinoma cell line, HepG2; the human model of liver cells in CAD research. This study showed for the first time that the Y-linked lincRNA transcripts lnc-KDM5D-4:1, lnc-ZFY-1:1, lnc-ZFY-2:1, lnc-RBMY1B-1:1, and lncxix RBMY1B-1:4 were expressed in HepG2 cells, hence in hepatocellular carcinoma (HCC). Furthermore, this study demonstrated that lnc-KDM5D-4 is a nuclear-retained lincRNA using RNA fluorescence in situ hybridisation (RNA FISH), and is upregulated in palmitate-induced steatosis within hepatocytes (Fold Change = 2.16; p-value = 0.00216). The human Atherosclerosis RT2 Profiler™ PCR Array determined that the silencing of lnc-KDM5D-4 in HepG2 cells was triggering the upregulation of the inhibitor of apoptosis (IAP) gene baculoviral IAP repeat containing 3 (BIRC3) (Fold Change = 12.45, p-value = 0.000025), a well-described protein-coding gene expressed by vascular smooth muscle cells and macrophage foam cells of the inflamed vascular wall of atherosclerotic arteries. Furthermore, perilipin 2 (PLIN2), a gene known to be implicated in lipid metabolism, was also found upregulated. Therefore, this study provides the first evidence for the involvement of a Ychromosome- linked lincRNA, lnc-KDM5D-4, in steatosis-associated atherosclerosis and its retained-nuclear cellular localisation in human hepatocytes, suggesting a function which takes place in the cell nucleus and may play a role in regulating metabolic processes in the liver that are implicated in atherosclerosis. Having shown that a Y chromosome-linked lincRNA could be involved in the determination of lipid level and hence atherosclerosis in men, and to further explore the role of lnc-KDM5D- 4, the expression of this Y-linked lincRNA was studied in human coronary artery smooth muscle cells, especially in atherosclerotic coronary artery cells (Chapter 4). The expression of other non-coding RNAs were also studied such as the protein kinase, Y-linked, pseudogene (PRKY) - previously considered as a new functional candidate for the development of CAD. By analysing the transcriptome of human atherosclerotic and non-atherosclerotic coronary artery smooth muscle cells, I established evidence for the implication of the human Y chromosome in atherosclerosis and CAD. This study exposed the general underexpression of the transcripts from the Y chromosome in atherosclerotic cells implicating a loss or a repression of this chromosome in relation to CAD. Furthermore, this research determined by RNA sequencing a significant downregulation of seven transcripts from Y chromosome genes, including RPS4Y1, USP9Y, DDX3Y, TXLNGY, NLGN4Y and PRKY. RNA FISH determined the subcellular localisation of PRKY in smooth muscle cells by showing a nuclear and a cytoplasmic expression. Furthermore, qPCR gene expression analysis demonstrated that lnc- KDM5D-4 is significantly downregulated in atherosclerotic cells in comparison to the nonatherosclerotic cells. Together, these results showed that lnc-KDM5D-4 is a potential regulator of PLIN2 and BIRC3 genes. Therefore, the downregulation of lnc-KDM5D-4 in atherosclerotic coronary artery smooth muscle cells suggests that this downregulation could be linked to the inflammation of the vascular smooth muscle cells in pathophysiology of CAD via the inhibition of apoptosis of the vascular smooth muscle cells triggered by the upregulation of BIRC3 in these cells. Overall, this study is the first to emphasise a potential involvement of a Y-specific lincRNA, called lnc-KDM5D-4, as a potential contributor to physiology in males. Lnc-KDM5D-4 knockdown resulted in an upregulation of anti-apoptosis and lipid metabolism-related genes. Collectively, our data suggest that the male–specific lnc-KDM5D-4 may regulate key processes in cellular inflammation that trigger atherosclerosis and CAD in men. Accordingly, this data suggests that lnc-KDM5D-4 may provide a novel molecular biomarker for atherosclerotic arteries, and could potentially lead to revolutionary treatment modalities on Y-linked lincRNA as therapeutic agents to manipulate CAD-causing genes in men.
Doctor of Philosophy
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Merry, Callie R. "Long Non-coding RNAs in Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1467828387.

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Cabili, Nataly Moran. "Integrative Characterization of Human Long Non-Coding RNAs." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11409.

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Since its early discovery as a messenger, RNA has been shown to play a diverse set of regulatory, structural and even catalytic roles. The more recent understanding that the genome is pervasively transcribed stimulated the discovery of a new prevalent class of long non coding RNAs (lncRNAs). While these are lower abundant and relatively less conserved than other class of functional RNAs, lncRNAs are emerging as key players in different cellular processes in development and disease.
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Vučićević, Dubravka [Verfasser]. "Diverse regulatory functions of long non-coding RNAs / Dubravka Vučićević." Berlin : Freie Universität Berlin, 2017. http://d-nb.info/1137509899/34.

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Bussotti, Giovanni 1983. "Detecting and comparing non-coding RNAs." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/128970.

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In recent years there has been a growing interest in the field of non-coding RNA. This surge is a direct consequence of the discovery of a huge number of new non-coding genes, and of the finding that many of these transcripts are involved in key cellular functions. In this context, accurately detecting and comparing RNA sequences becomes extremely important. Aligning nucleotide sequences is one of the main requisite when searching for homologous genes. Accurate alignments reveal evolutionary relationships, conserved regions and more generally, any biologically relevant pattern. Comparing RNA molecules is, however, a challenging task. The nucleotide alphabet is simpler and therefore less informative than that of proteins. Moreover for many non-coding RNAs, evolution is likely to be mostly constrained at the structure level and not on the sequence level. This results in a very poor sequence conservation impeding the comparison of these molecules. These difficulties define a context where new methods are urgently needed in order to exploit experimental results at their full potential. These are the issues I have tried to address in my PhD. I have started by developing a novel algorithm able to reveal the homology relationship of distantly related ncRNA genes, and then I have applied the approach thus defined in combination with other sophisticated data mining tools to discover novel non-coding genes and generate genome-wide ncRNA predictions.
En los últimos años el interés en el campo de los ARN no codificantes ha crecido mucho a causa del enorme aumento de la cantidad de secuencias no codificantes disponibles y a que muchos de estos transcriptos han dado muestra de ser importantes en varias funciones celulares. En este contexto, es fundamental el desarrollo de métodos para la correcta detección y comparativa de secuencias de ARN. Alinear nucleótidos es uno de los enfoques principales para buscar genes homólogos, identificar relaciones evolutivas, regiones conservadas y en general, patrones biológicos importantes. Sin embargo, comparar moléculas de ARN es una tarea difícil. Esto es debido a que el alfabeto de nucleótidos es más simple y por ello menos informativo que el de las proteínas. Además es probable que para muchos ARN la evolución haya mantenido la estructura en mayor grado que la secuencia, y esto hace que las secuencias sean poco conservadas y difícilmente comparables. Por lo tanto, hacen falta nuevos métodos capaces de utilizar otras fuentes de información para generar mejores alineamientos de ARN. En esta tesis doctoral se ha intentado dar respuesta exactamente a estas temáticas. Por un lado desarrollado un nuevo algoritmo para detectar relaciones de homología entre genes de ARN no codificantes evolutivamente lejanos. Por otro lado se ha hecho minería de datos mediante el uso de datos ya disponibles para descubrir nuevos genes y generar perfiles de ARN no codificantes en todo el genoma.
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Schneider, Hugo Wruck. "Distinguishing long non-coding RNAs from protein coding transcripts based on machine learning techniques." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/31264.

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Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.
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Dentre as análises que devem ser realizadas nos projetos de sequenciamento, um problema importante é a distinção entre transcritos codificadores de proteinas (PCTs) e RNAs nãocodificadores longos (lncRNAs). Esse trabalho investiga potenciais características dos lncRNAs e propõe dois métodos para distinção dessas duas classes de transcritos (PCTs e lncRNAs). O primeiro método foi proposto com base em máquinas de vetores de suporte (SVM), enquanto o segundo utilizou técnicas de aprendizado semi-supervisionado. O mé- todo utilizando SVM obteve excelentes resultados, quando comparados a outras propostas existentes na literatura. Esse método foi treinado e testado com dados de humanos, camundongos e peixe-zebra, tendo atingido uma acurácia de ≈ 98% com dados de humanos e camundongos, e de ≈ 96% para os dados do peixe-zebra. Ainda, foram criados modelos utilizando várias espécies, que mostraram classificações melhores para outras espécies diferentes daquelas do treinamento, ou seja, mostraram boa capacidade de generalização. Para validar esse método, foram utilizados dados de ratos, porcos e drosófilas, além de dados de RNA-seq de humanos, gorilas e macacos. Essa validação atingiu uma acurácia de mais de 85%, em todos os casos. Por fim, esse método foi capaz de identificar duas sequências dentro do Swiss-Prot que puderam ser reanotadas. O método baseado em aprendizado semi-supervisionado foi treinado e testado com dados de humanos, camundongos, ornitorrincos, galinhas, gambás, orangotangos e rãs, tendo sido utilizadas cinco técnicas de aprendizado semi-supervisionado. A contribuição desse método foi que ele permitiu a redução do tamanho do conjunto de dados classificados, utilizados no treinamento. No melhor caso, somente 2 sequências bem anotadas foram usadas no treinamento, o que, comparado com outras ferramentas disponíveis na literatura, indica um ganho expressivo. A acurácia obtida pelo método nos melhores casos foram de ≈ 95% para dados de humanos e camundongos, ≈ 90% para dados de galinhas, gambás e orangutangos, e ≈ 80% para dados de ornitorrincos e rãs. Dados de RNA-seq foram utilizados para teste, tendo sido obtida acurácia de mais de 95%. Esses dados foram utilizados para treinamento dos modelos de orangotango e de rã, que também apresentaram acurácias excelentes.
Among the analyses that have to be performed in sequencing projects, an important problem to be addressed is the distinction of protein coding transcripts (PCTs) and long non-coding RNAs (lncRNA). This work investigates potential characteristics of the lncRNAs and proposes two methods for distinguishing these two classes of transcripts (PCTs and lncRNAs). The first methods was based on Support Vector Machine (SVM), while the second one used semi-supervised learning techniques. The SVM based method obtained excellent results when compared to other methods in the literature. This method was trained and tested with data from human, mouse and zebrafish, and reached accuracy of ≈ 98% for human and mouse data, and ≈ 96% for zebrafish data. Besides, models with multiple species were created, which improved the classification for species different from those used in the training phase, i.e., these models could also be used in the classification of species different from those that were used in the training phase. To validate this method, data from rat, pig and drosophila, and RNA-seq data from humans, gorillas and macaque were used. This validation reached an accuracy of more than 85% for all the species. Finally, this method was able to identify two sequences within the Swiss-Prot database that were reannotated. The semi-supervised based method was trained and tested with data from human, mouse, platypus, chicken, opossum, orangutan and xenopus, in five semi-supervised learning techniques. The contribution of this method was the reduction of the size of the classified training data set. In the best scenario, only two annotated sequences were used in the training phase, which is an expressive gain when compared to other tools available in the literature. Accuracies obtained by the method in the best cases were ≈ 95% for human and mouse datasets, ≈ 90% for chicken, opossum and orangutan datasets, and ≈ 80% for data platypus and xenopus datasets. RNA-seq data were used for testing, having obtained more than 95% of accuracy. This data was used to train the orangutan and xenopus models, also leading to an excellent accuracy.
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de, Bony Eric James. "Novel insights into the function and regulation of coding and long non-coding RNAs." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/268600.

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Le dogme central de la biologie repose sur la production de protéines à partir de notre ADN. L’ADN est d’abord transcrit en ARN et celui-ci est ensuite traduit en protéine. C’est donc en cette dernière qu’est localisé le “pouvoir exécutif” de la cellule, ce qui explique le fait que les protéines soient devenues le centre d’attention de la recherche. L’ARN, quant à lui, est donc depuis longtemps considéré comme une molécule intermédiaire, dont l’unique raison d’être est le transfert d’information entre l’ADN et les protéines. Pourtant, ces dernières années, les avancées technologiques ont révélé qu’une majeure partie de notre génome, notre ADN, est transcrit en ARNs dits « noncodants » ne donnant pas lieu à une protéine. Ceux-ci sont impliqués dans de nombreux processus cellulaires et de ce fait participent aux pathologies. D’autre part, de nouvelles technologies ont aussi mené à l’observation que le métabolisme des ARNs, codants ou non, est la cible de nouveaux mécanismes de régulation: les modifications chimiques des ribonucléosides. Analysées de manière conjointe, ces découvertes poussent à la révision du rôle des ARNs au sein des processus cellulaires. Dès lors, dans le cadre de cette thèse nous avons voulu mieux comprendre la fonction et la régulation des molécules d’ARN afin d’en révéler le rôle plus central qu’ils jouent dans les processus cellulaire et en particulier, la cancérogenèse. Pour ce faire cette thèse comporte deux parties, la première décrit comment certains ARNs, dit “longs ARNs non-codants” participent au développement et à l’hétérogénéité du cancer colorectal. En effet ces ARNs exercent des fonctions “exécutives” sans être la source d’une protéine. Nous avons identifié 282 long ARNs non-codants dont les profils d’expression reflètent les différentes caractéristiques rencontrées au travers des différents sous-types de tumeurs colorectales. De plus, nos analyses informatiques ont indiqué que ces ARNs font partie intégrante des réseaux de signalisations les plus importants et les plus souvent dérégulés dans les différents sous-types que présente ce cancer. Enfin, et ce via des expériences in vitro nous soutenons la validité de nos analyses informatiques en confirmant le rôle de lncBLID-5, un long ARN non-codant, dans la régulation du cycle cellulaire et de la transition épithéliale vers mésenchymale un processus cellulaire très important dans les cancers colorectaux. Dans la deuxième partie nous avons étudié la méthylation des cytosines de l’ARN, une modification très récemment identifiée. Nous avons découvert que la protéine SRSF2, un facteur général de l’épissage des ARNs, est capable de se lier aux cytosines méthylées et ce plus fortement qu’aux cytosines non-méthylées. Enfin, nous montrons que la mutation P95H de SRSF2, très fréquente chez les patients atteints de leucémie, empêche SRSF2 de favoriser sa liaison aux cytosines méthylées laissant entrevoir de nouvelles explications à l’épissage défectueux conduisant à ce type de cancer. En conclusion nos travaux apportent de nouvelles informations quant à l’implication et la régulation des ARNs codants et non-codants dans le cadre du cancer. Ces résultats devraient nous mener à revoir le rôle qu’occupe l’ARN au sein des processus cellulaires sains ainsi que pathologiques, ouvrant la porte sur une nouvelle dimension de cibles diagnostiques et thérapeutiques.
Doctorat en Sciences biomédicales et pharmaceutiques (Médecine)
info:eu-repo/semantics/nonPublished
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Alvarez, Juan (Juan Rene Alvarez Dominguez). "Modulation of lineage-specific cell differentiation by long non-coding RNAs." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97280.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references.
Mammalian genomes comprise thousands of non-protein-coding genes. These can produce small non-coding RNAs (such as rRNAs and tRNAs), as well as long non-coding RNAs (lncRNAs), which are >200nt and resemble mRNAs in their biogenesis. Although the functions of the vast majority of lncRNAs remain unknown, many are tissue- and developmental stage-specific, suggesting roles in lineage-specific development. We generated deep transcriptome surveys from differentiating mouse red blood cells, and implemented a computational strategy for de novo lncRNA discovery to comprehensively catalog erythroid-expressed lncRNAs. We found >100 previously unannotated loci, many of which are erythroid-specific and are induced by key erythroid transcription factors during differentiation. We exploited these features to select 12 candidates for loss-of-function studies, and found that depleting 10 out of 12 impaired red cell maturation, inhibiting cell size reduction and subsequent enucleation. To study how lncRNAs regulate erythropoiesis, we focused on EC6, an unpolyadenylated lncRNA needed for silencing neighboring loci encoding NF-kB activators. De-repression of these genes upon EC6 knockdown leads to activation of NF-kB and other immune pathways that antagonize erythropoiesis, resulting in impaired proliferation and elevated apoptosis during differentiation. We showed that EC6 is retained in chromatin and binds the nuclear matrix factor hnRNP U, which may enable co-localization with its targets to mediate their repression. Extending our work to a different lineage, we reconstructed transcriptomes from distinct mouse adipose tissues and identified ~1500 lncRNAs. These included many brown fat-specific loci induced during differentiation which are targets of key adipogenic factors. Inhibiting one of them, lnc-BATE1, compromised brown adipocyte development, impairing activation of brown fat genes, mitochondrial biogenesis, and thermogenic function. We showed that lnc-BATE1 acts in trans and binds hnRNP U, which is also required for proper brown adipocyte maturation. This work demonstrates that lncRNAs modulate lineage-specific cell differentiation by promoting or suppressing competing gene expression programs controlling cell fate.
by Juan Alvarez.
Ph. D.
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Chen, Li. "Functional and evolutionary characterization of flowering-related long non-coding RNAs." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22833.

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Genomweite Bemühungen haben eine große Anzahl langer nichtkodierender RNAs (lncRNAs) identifiziert, obwohl ihre möglichen Funktionen weitgehend rätselhaft bleiben. Hier verwendeten wir ein System zur synchronisierten Blüteninduktion in Arabidopsis, um 4106 blütenbezogene lange intergene RNAs (lincRNAs) zu identifizieren. Blütenbezogene lincRNAs sind typischerweise mit funktionellen Enhancern assoziiert, die bidirektional transkribiert werden und mit verschiedenen funktionellen Genmodulen assoziiert sind, die mit der Entwicklung von Blütenorganen zusammenhängen, die durch Koexpressionsnetzwerkanalyse aufgedeckt wurden. Die Master-regulatorischen Transkriptionsfaktoren (TFs) APETALA1 (AP1) und SEPALLATA3 (SEP3) binden an lincRNA-assoziierte Enhancer. Die Bindung dieser TFs korreliert mit der Zunahme der lincRNA-Transkription und fördert möglicherweise die Zugänglichkeit von Chromatin an Enhancern, gefolgt von der Aktivierung einer Untergruppe von Zielgenen. Darüber hinaus ist die Evolutionsdynamik von lincRNAs in Pflanzen, einschließlich nicht blühender Pflanzen, noch nicht bekannt, und das Expressionsmuster in verschiedenen Pflanzenarten war ziemlich unbekannt. Hier identifizierten wir Tausende von lincRNAs in 26 Pflanzenarten, einschließlich nicht blühender Pflanzen. Ein direkter Vergleich von lincRNAs zeigt, dass die meisten lincRNAs speziesspezifisch sind und das Expressionsmuster von lincRNAs einen hohen Transkriptionsumsatz nahe legt. Darüber hinaus zeigen konservierte lincRNAs eine aktive Regulation durch Transkriptionsfaktoren wie AP1 und SEP3. Konservierte lincRNAs zeigen eine konservierte blütenbezogene Funktionalität sowohl in der Brassicaceae- als auch in der Grasfamilie. Die Evolutionslandschaft von lincRNAs in Pflanzen liefert wichtige Einblicke in die Erhaltung und Funktionalität von lincRNAs.
Genome-wide efforts have identified a large number of long non-coding RNAs (lncRNAs), although their potential functions remain largely enigmatic. Here, we used a system for synchronized floral induction in Arabidopsis to identify 4106 flower-related long intergenic RNAs (lincRNAs). Flower-related lincRNAs are typically associated with functional enhancers which are bi-directionally transcribed and are associated with diverse functional gene modules related to floral organ development revealed by co-expression network analysis. The master regulatory transcription factors (TFs) APETALA1 (AP1) and SEPALLATA3 (SEP3) bind to lincRNA-associated enhancers. The binding of these TFs is correlated with the increase in lincRNA transcription and potentially promotes chromatin accessibility at enhancers, followed by activation of a subset of target genes. Furthermore, the evolutionary dynamics of lincRNAs in plants including non-flowering plants still remain to be elusive and the expression pattern in different plant species was quite unknown. Here, we identified thousands of lincRNAs in 26 plant species including non-flowering plants, and allow us to infer sequence conserved and synteny based homolog lincRNAs, and explore conserved characteristics of lincRNAs during plants evolution. Direct comparison of lincRNAs reveals most lincRNAs are species-specific and the expression pattern of lincRNAs suggests their high evolutionary gain and loss. Moreover, conserved lincRNAs show active regulation by transcriptional factors such as AP1 and SEP3. Conserved lincRNAs demonstrate conserved flower related functionality in both the Brassicaceae and grass family. The evolutionary landscape of lincRNAs in plants provide important insights into the conservation and functionality of lincRNAs.
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Coyne, Victoria. "Characterization of long non-coding RNAs in the Hox complex of Drosophila." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/characterization-of-long-noncoding-rnas-in-the-hox-complex-of-drosophila(733e3dec-3f7b-4d6e-a1bc-674a8786246d).html.

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Long non-coding RNAs (lncRNAs) are often defined as transcripts >200nts that have no discernable protein-coding ability (Quinn and Chang, 2016). Although relatively little is understood about the molecular mechanisms of lncRNA function, they have established roles in regulation of gene expression during development, cell differentiation and pluripotency (Fatica and Bozzoni, 2014; Luo et al., 2016; Quinn and Chang, 2016; Rinn and Chang, 2012) across vastly diverse organisms ranging from plants to humans (Ulitsky and Bartel, 2013). LncRNAs have also been associated with numerous pathological conditions, such as cancers (Brunner et al., 2012), cardiovascular disease and neurodegeneration (Chen et al., 2013). Investigations into lncRNAs in wide ranging organisms, have revealed that many influence gene activity by forming ribonucleoprotein complexes that affect the conformational state of chromatin (Rinn and Chang, 2012). A genomic region that has revealed several functional lncRNAs in diverse organisms is the Hox complex (Pauli et al., 2011; Pettini, 2012; Rinn et al., 2007). The Hox complex encodes a set of transcription factors (TFs), physically clustered in the genome, which provide morphological identity along the anterior to posterior axis of developing embryos (Mallo and Alonso, 2013), throughout the majority of bilatarian animals (Moreno et al., 2011). Misexpression or mutation of Hox genes causes morphological and pathophysiological defects (Quinonez and Innis, 2014). We investigated clustering of lncRNAs throughout the D. melanogaster genome using available annotations and carried out RNA-seq in D. virilis to expand the repertoire of lncRNAs and identify clusters of lncRNAs. We found the Hox complex to be heavily enriched with lncRNAs in both organisms, and syntenic transcripts from D. melanogaster could be identified in D. pseudoobscura and D. virilis. Several lncRNAs aligned with polycomb response elements (PREs); transcription of PREs has previously been linked to a switch in their activity (Herzog et al., 2014). However, we found that transcribed PREs in D. melanogaster move positions relative to the protein-coding genes in other drosophilids, whilst the transcriptional units remain in the same syntenic region. Conservation of syntenic transcripts without evidence of remaining a PRE suggest that the transcription is not linked to PRE function, agreeing with recent findings that transcription of PREs does not affect their function (Kassis and Muller, 2015). We investigated functions of a novel lncRNA and adjacent PRE in the Hox complex by ectopic expression and utilization of other genetic manipulation tools. Overexpression of either the lncRNA or PRE and partial duplication of the lncRNA caused phenotypes such as missing halteres and/or T3 legs, misshaped T3 legs or malformed abdominal segments. The observations that ectopic expression of this lncRNA and an adjacent regulatory element from the Hox complex causes phenotypes that can be linked to adjacent Hox gene misregulation, Antp and Ubx, suggest that they are likely to have roles in the regulation of at least one of these Hox genes.
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Books on the topic "Long non-coding RNAs (IncRNAs)"

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Zhang, Lin, and Xiaowen Hu, eds. Long Non-Coding RNAs. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1697-0.

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Ugarkovic, Durdica, ed. Long Non-Coding RNAs. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16502-3.

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Feng, Yi, and Lin Zhang, eds. Long Non-Coding RNAs. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5.

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Chekanova, Julia A., and Hsiao-Lin V. Wang, eds. Plant Long Non-Coding RNAs. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9045-0.

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Navarro, Alfons, ed. Long Non-Coding RNAs in Cancer. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1581-2.

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Morris, Kevin V., ed. Long Non-coding RNAs in Human Disease. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23907-1.

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Khalil, Ahmad M., and Jeff Coller, eds. Molecular Biology of Long Non-coding RNAs. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8621-3.

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Khalil, Ahmad M., ed. Molecular Biology of Long Non-coding RNAs. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17086-8.

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Cao, Haiming, ed. Functional Analysis of Long Non-Coding RNAs. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1158-6.

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Song, Erwei, ed. The Long and Short Non-coding RNAs in Cancer Biology. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1498-7.

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Book chapters on the topic "Long non-coding RNAs (IncRNAs)"

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Zhang, Yang, Li Yang, and Ling-Ling Chen. "Characterization of Circular RNAs." In Long Non-Coding RNAs, 215–27. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_17.

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Wang, Yueying, Mu Xu, Jiao Yuan, Zhongyi Hu, Youyou Zhang, Lin Zhang, and Xiaowen Hu. "Detection of Long Non-coding RNA Expression by Non-radioactive." In Long Non-Coding RNAs, 145–56. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1697-0_13.

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Zhao, Yi, Jiao Yuan, and Runsheng Chen. "NONCODEv4: Annotation of Noncoding RNAs with Emphasis on Long Noncoding RNAs." In Long Non-Coding RNAs, 243–54. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_19.

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Xing, Zhen, Chunru Lin, and Liuqing Yang. "LncRNA Pulldown Combined with Mass Spectrometry to Identify the Novel LncRNA-Associated Proteins." In Long Non-Coding RNAs, 1–9. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_1.

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Orjalo, Arturo V., and Hans E. Johansson. "Stellaris® RNA Fluorescence In Situ Hybridization for the Simultaneous Detection of Immature and Mature Long Noncoding RNAs in Adherent Cells." In Long Non-Coding RNAs, 119–34. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_10.

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Lai, Lan-Tian, Zhenyu Meng, Fangwei Shao, and Li-Feng Zhang. "Simultaneous RNA–DNA FISH." In Long Non-Coding RNAs, 135–45. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_11.

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Hinten, Michael, Emily Maclary, Srimonta Gayen, Clair Harris, and Sundeep Kalantry. "Visualizing Long Noncoding RNAs on Chromatin." In Long Non-Coding RNAs, 147–64. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_12.

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Maqsodi, Botoul, and Corina Nikoloff. "Non-isotopic Method for In Situ LncRNA Visualization and Quantitation." In Long Non-Coding RNAs, 165–76. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_13.

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Hu, Xiaowen, Yi Feng, Zhongyi Hu, Youyou Zhang, Chao-Xing Yuan, Xiaowei Xu, and Lin Zhang. "Detection of Long Noncoding RNA Expression by Nonradioactive Northern Blots." In Long Non-Coding RNAs, 177–88. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_14.

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Hatakeyama, Hiroto, Sherry Y. Wu, Lingegowda S. Mangala, Gabriel Lopez-Berestein, and Anil K. Sood. "Assessment of In Vivo siRNA Delivery in Cancer Mouse Models." In Long Non-Coding RNAs, 189–97. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3378-5_15.

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Conference papers on the topic "Long non-coding RNAs (IncRNAs)"

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Gozukirmizi, Nermin, and Elif Karlik. "New gene expression regulators: Long non-coding RNAs." In PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5125523.

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Opattova, Alena, Fábio Miguel Ferreira, Jozef Horak, Sona Vodenkova, and Pavel Vodicka. "Abstract 3496: Long non-coding RNAs in colorectal cancer." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-3496.

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Cristiano, Francesca, Pierangelo Veltri, Mattia Prosperi, and Giuseppe Tradigo. "On the identification of long non-coding RNAs from RNA-seq." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822675.

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Niknafs, Yashar S., Matthew K. Iyer, and Arul M. Chinnaiyan. "Abstract 2992: The landscape of long non-coding RNAs in cancer." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-2992.

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Shen, Jing, Abby B. Siegel, Helen Remotti, Qiao Wang, Yueyue Shen, and Regina M. Santella. "Abstract 3818: Deregulated long non-coding RNAs in hepatocellular carcinoma (HCC)." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-3818.

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Rodriguez, Daniel A., Jeffim N. Kuznetsov, Margaret I. Sanchez, Stefan Kurtenbach, and J. William Harbour. "Abstract 4244: Novel expressed long non-coding RNAs in uveal melanoma." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-4244.

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Rodriguez, Daniel A., Jeffim N. Kuznetsov, Margaret I. Sanchez, Stefan Kurtenbach, and J. William Harbour. "Abstract 4244: Novel expressed long non-coding RNAs in uveal melanoma." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-4244.

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Zhao, Pengfei, Qinke Peng, Zhibo Zhu, Tian Han, Rida Dong, and Huijun Huang. "lncDML: Identification of long non-coding RNAs by Deep Metric Learning." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623112.

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Nath, Aritro, and R. Stephanie Huang. "Abstract 3897: Pharmacogenomic landscape of long non-coding RNAs in human cancers." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-3897.

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Mamun, Abdullah Al, Wenrui Duan, and Ananda Mohan Mondal. "Pan-cancer Feature Selection and Classification Reveals Important Long Non-coding RNAs." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313332.

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Reports on the topic "Long non-coding RNAs (IncRNAs)"

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Tianzi, Zhang. The Emerging Roles of Long Non-coding RNAs in the Pathogenesis of Breast Cancer. Envirarxiv, November 2022. http://dx.doi.org/10.55800/envirarxiv488.

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Zhong, Xiaoling, Qin Guo, Jing Zhao, Yinyue Li, Xue Li, Min Ren, and Min Shu. Diagnostic significance of long non-coding RNAs expression in TB patients: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2020. http://dx.doi.org/10.37766/inplasy2020.7.0043.

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Zhou, Xuefeng, Wenjing Liu, Zhenhuan Yang, Wei'e Zhou, and Ping Li. Long non-coding RNAs, one of candidate biomarkers in diabetic kidney disease A systematic review protocol of profiling studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0136.

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Wu, Zilong, Zihao Xu, Boyao Yu, Jing tao Zhang, and Bentong Yu. The potential diagnostic value of exosomal long non-coding RNAs in solid tumours: a meta-analysis and systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2020. http://dx.doi.org/10.37766/inplasy2020.6.0083.

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Dubcovsky, Jorge, Tzion Fahima, Ann Blechl, and Phillip San Miguel. Validation of a candidate gene for increased grain protein content in wheat. United States Department of Agriculture, January 2007. http://dx.doi.org/10.32747/2007.7695857.bard.

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Abstract:
High Grain Protein Content (GPC) of wheat is important for improved nutritional value and industrial quality. However, selection for this trait is limited by our poor understanding of the genes involved in the accumulation of protein in the grain. A gene with a large effect on GPC was detected on the short arm of chromosome 6B in a Triticum turgidum ssp. dicoccoides accession from Israel (DIC, hereafter). During the previous BARD project we constructed a half-million clones Bacterial Artificial Chromosome (BAC) library of tetraploid wheat including the high GPC allele from DIC and mapped the GPC-B1 locus within a 0.3-cM interval. Our long-term goal is to provide a better understanding of the genes controlling grain protein content in wheat. The specific objectives of the current project were to: (1) complete the positional cloning of the GPC-B1 candidate gene; (2) characterize the allelic variation and (3) expression profile of the candidate gene; and (4) validate this gene by using a transgenic RNAi approach to reduce the GPC transcript levels. To achieve these goals we constructed a 245-kb physical map of the GPC-B1 region. Tetraploid and hexaploid wheat lines carrying this 245-kb DIC segment showed delayed senescence and increased GPC and grain micronutrients. The complete sequencing of this region revealed five genes. A high-resolution genetic map, based on approximately 9,000 gametes and new molecular markers enabled us to delimit the GPC-B1 locus to a 7.4-kb region. Complete linkage of the 7.4-kb region with earlier senescence and increase in GPC, Zn, and Fe concentrations in the grain suggested that GPC-B1 is a single gene with multiple pleiotropic effects. The annotation of this 7.4-kb region identified a single gene, encoding a NAC transcription factor, designated as NAM-B1. Allelic variation studies demonstrated that the ancestral wild wheat allele encodes a functional NAC transcription factor whereas modern wheat varieties carry a non-functional NAM-B1 allele. Quantitative PCR showed that transcript levels for the multiple NAMhomologues were low in flag leaves prior to anthesis, after which their levels increased significantly towards grain maturity. Reduction in RNA levels of the multiple NAMhomologues by RNA interference delayed senescence by over three weeks and reduced wheat grain protein, Zn, and Fe content by over 30%. In the transgenic RNAi plants, residual N, Zn and Fe in the dry leaves was significantly higher than in the control plants, confirming a more efficient nutrient remobilization in the presence of higher levels of GPC. The multiple pleiotropic effects of NAM genes suggest a central role for these genes as transcriptional regulators of multiple processes during leaf senescence, including nutrient remobilization to the developing grain. The cloning of GPC-B1 provides a direct link between the regulation of senescence and nutrient remobilization and an entry point to characterize the genes regulating these two processes. This may contribute to their more efficient manipulation in crops and translate into food with enhanced nutritional value. The characterization of the GPC-B1 gene will have a significant impact on wheat production in many regions of the world and will open the door for the identification of additional genes involved in the accumulation of protein in the grain.
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VanderGheynst, Jean, Michael Raviv, Jim Stapleton, and Dror Minz. Effect of Combined Solarization and in Solum Compost Decomposition on Soil Health. United States Department of Agriculture, October 2013. http://dx.doi.org/10.32747/2013.7594388.bard.

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In soil solarization, moist soil is covered with a transparent plastic film, resulting in passive solar heating which inactivates soil-borne pathogen/weed propagules. Although solarization is an effective alternative to soil fumigation and chemical pesticide application, it is not widely used due to its long duration, which coincides with the growing season of some crops, thereby causing a loss of income. The basis of this project was that solarization of amended soil would be utilized more widely if growers could adopt the practice without losing production. In this research we examined three factors expected to contribute to greater utilization of solarization: 1) investigation of techniques that increase soil temperature, thereby reducing the time required for solarization; 2) development and validation of predictive soil heating models to enable informed decisions regarding soil and solarization management that accommodate the crop production cycle, and 3) elucidation of the contributions of microbial activity and microbial community structure to soil heating during solarization. Laboratory studies and a field trial were performed to determine heat generation in soil amended with compost during solarization. Respiration was measured in amended soil samples prior to and following solarization as a function of soil depth. Additionally, phytotoxicity was estimated through measurement of germination and early growth of lettuce seedlings in greenhouse assays, and samples were subjected to 16S ribosomal RNA gene sequencing to characterize microbial communities. Amendment of soil with 10% (g/g) compost containing 16.9 mg CO2/g dry weight organic carbon resulted in soil temperatures that were 2oC to 4oC higher than soil alone. Approximately 85% of total organic carbon within the amended soil was exhausted during 22 days of solarization. There was no significant difference in residual respiration with soil depth down to 17.4 cm. Although freshly amended soil proved highly inhibitory to lettuce seed germination and seedling growth, phytotoxicity was not detected in solarized amended soil after 22 days of field solarization. The sequencing data obtained from field samples revealed similar microbial species richness and evenness in both solarized amended and non-amended soil. However, amendment led to enrichment of a community different from that of non-amended soil after solarization. Moreover, community structure varied by soil depth in solarized soil. Coupled with temperature data from soil during solarization, community data highlighted how thermal gradients in soil influence community structure and indicated microorganisms that may contribute to increased soil heating during solarization. Reliable predictive tools are necessary to characterize the solarization process and to minimize the opportunity cost incurred by farmers due to growing season abbreviation, however, current models do not accurately predict temperatures for soils with internal heat generation associated with the microbial breakdown of the soil amendment. To address the need for a more robust model, a first-order source term was developed to model the internal heat source during amended soil solarization. This source term was then incorporated into an existing “soil only” model and validated against data collected from amended soil field trials. The expanded model outperformed both the existing stable-soil model and a constant source term model, predicting daily peak temperatures to within 0.1°C during the critical first week of solarization. Overall the results suggest that amendment of soil with compost prior to solarization may be of value in agricultural soil disinfestations operations, however additional work is needed to determine the effects of soil type and organic matter source on efficacy. Furthermore, models can be developed to predict soil temperature during solarization, however, additional work is needed to couple heat transfer models with pathogen and weed inactivation models to better estimate solarization duration necessary for disinfestation.
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