Relevant bibliographies by topics / Non-coding RNA detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Non-coding RNA detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Non-coding RNA detection"

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Kazimierczyk, Marek, and Jan Wrzesinski. "Long Non-Coding RNA Epigenetics." International Journal of Molecular Sciences 22, no. 11 (June 7, 2021): 6166. http://dx.doi.org/10.3390/ijms22116166.

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Long noncoding RNAs exceeding a length of 200 nucleotides play an important role in ensuring cell functions and proper organism development by interacting with cellular compounds such as miRNA, mRNA, DNA and proteins. However, there is an additional level of lncRNA regulation, called lncRNA epigenetics, in gene expression control. In this review, we describe the most common modified nucleosides found in lncRNA, 6-methyladenosine, 5-methylcytidine, pseudouridine and inosine. The biosynthetic pathways of these nucleosides modified by the writer, eraser and reader enzymes are important to understanding these processes. The characteristics of the individual methylases, pseudouridine synthases and adenine–inosine editing enzymes and the methods of lncRNA epigenetics for the detection of modified nucleosides, as well as the advantages and disadvantages of these methods, are discussed in detail. The final sections are devoted to the role of modifications in the most abundant lncRNAs and their functions in pathogenic processes.
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Romano, Giulia, Michela Saviana, Patricia Le, Howard Li, Lavender Micalo, Giovanni Nigita, Mario Acunzo, and Patrick Nana-Sinkam. "Non-Coding RNA Editing in Cancer Pathogenesis." Cancers 12, no. 7 (July 8, 2020): 1845. http://dx.doi.org/10.3390/cancers12071845.

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In the last two decades, RNA post-transcriptional modifications, including RNA editing, have been the subject of increasing interest among the scientific community. The efforts of the Human Genome Project combined with the development of new sequencing technologies and dedicated bioinformatic approaches created to detect and profile RNA transcripts have served to further our understanding of RNA editing. Investigators have determined that non-coding RNA (ncRNA) A-to-I editing is often deregulated in cancer. This discovery has led to an increased number of published studies in the field. However, the eventual clinical application for these findings remains a work in progress. In this review, we provide an overview of the ncRNA editing phenomenon in cancer. We discuss the bioinformatic strategies for RNA editing detection as well as the potential roles for ncRNA A to I editing in tumor immunity and as clinical biomarkers.
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Falahi, Sedigheh, Hossain-Ali Rafiee-Pour, Mashaalah Zarejousheghani, Parvaneh Rahimi, and Yvonne Joseph. "Non-Coding RNA-Based Biosensors for Early Detection of Liver Cancer." Biomedicines 9, no. 8 (August 5, 2021): 964. http://dx.doi.org/10.3390/biomedicines9080964.

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Primary liver cancer is an aggressive, lethal malignancy that ranks as the fourth leading cause of cancer-related death worldwide. Its 5-year mortality rate is estimated to be more than 95%. This significant low survival rate is due to poor diagnosis, which can be referred to as the lack of sufficient and early-stage detection methods. Many liver cancer-associated non-coding RNAs (ncRNAs) have been extensively examined to serve as promising biomarkers for precise diagnostics, prognostics, and the evaluation of the therapeutic progress. For the simple, rapid, and selective ncRNA detection, various nanomaterial-enhanced biosensors have been developed based on electrochemical, optical, and electromechanical detection methods. This review presents ncRNAs as the potential biomarkers for the early-stage diagnosis of liver cancer. Moreover, a comprehensive overview of recent developments in nanobiosensors for liver cancer-related ncRNA detection is provided.
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Nacher, Jose C. "Community structure of non-coding RNA interaction network." Journal of Integrative Bioinformatics 10, no. 2 (June 1, 2013): 24–34. http://dx.doi.org/10.1515/jib-2013-217.

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Summary Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between noncoding RNAs (ncRNAs) and proteins related bio-macrolecules (PRMs) using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell.
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Boivin, Vincent, Gaspard Reulet, Olivier Boisvert, Sonia Couture, Sherif Abou Elela, and Michelle S. Scott. "Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA." Nucleic Acids Research 48, no. 5 (January 25, 2020): 2271–86. http://dx.doi.org/10.1093/nar/gkaa028.

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Abstract The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 111 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new snoRNA and tRNA-like genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distorts the perceived architecture of the human transcriptome.
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Islam, Md Nazmul, Sofia Moriam, Muhammad Umer, Hoang-Phuong Phan, Carlos Salomon, Richard Kline, Nam-Trung Nguyen, and Muhammad J. A. Shiddiky. "Naked-eye and electrochemical detection of isothermally amplified HOTAIR long non-coding RNA." Analyst 143, no. 13 (2018): 3021–28. http://dx.doi.org/10.1039/c7an02109g.

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Tabei, Yasuo, and Kiyoshi Asai. "A local multiple alignment method for detection of non-coding RNA sequences." Bioinformatics 25, no. 12 (April 17, 2009): 1498–505. http://dx.doi.org/10.1093/bioinformatics/btp261.

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Raasch, Peter, Ulf Schmitz, Nadja Patenge, Julio Vera, Bernd Kreikemeyer, and Olaf Wolkenhauer. "Non-coding RNA detection methods combined to improve usability, reproducibility and precision." BMC Bioinformatics 11, no. 1 (2010): 491. http://dx.doi.org/10.1186/1471-2105-11-491.

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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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Soda, Narshone, Muhammad Umer, Surasak Kasetsirikul, Carlos Salomon, Richard Kline, Nam-Trung Nguyen, Bernd H. A. Rehm, and Muhammad J. A. Shiddiky. "An amplification-free method for the detection of HOTAIR long non-coding RNA." Analytica Chimica Acta 1132 (October 2020): 66–73. http://dx.doi.org/10.1016/j.aca.2020.07.038.

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Dissertations / Theses on the topic "Non-coding RNA detection"

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Liu, Xuan, and 刘璇. "Workflows for identifying differentially expressed small RNAs and detection of low copy repeats in human." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208038.

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With the rapid development of next-generation sequencing NGS technology, we are able to investigate various aspects biological problems, including genome and transcriptome sequencing, genomic structural variation and the mechanism of regulatory small RNAs, etc. An enormous number of associated computational methods have been proposed to study the biological problems using NGS reads, at a low cost of expense and time. Regulatory small RNAs and genomic structure variations are two main problems that we have studied. In the area of regulatory small RNA, various computational tools have been designed from the prediction of small RNA to target prediction. Regulatory small RNAs play essential roles in plants and bacteria such as in responses to environmental stresses. We focused on sRNAs that in act by base pairing with target mRNA in complementarity. A comprehensive analysis workflow that is able to integrate sRNA-Seq and RNA-Seq analysis and generate regulatory network haven't been designed yet. Thus, we proposed and implemented two small RNA analysis workflow for plants and bacteria respectively. In the area of genomic structural variations (SV), two types of disease-related SVs have been investigated, including complex low copy repeats (LCRs, also termed as segmental duplications) and tandem duplication (TD). LCRs provide structural basis to form a combination of other SVs which may in turn lead to some serious genetic diseases and TDs of specific areas have been reported for patients. Locating LCRs and TDs in human genome can help researchers to further interrogate the mechanism of related diseases. Therefore, we proposed two computational methods to predict novel LCRs and TDs in human genome.
published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
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Coventry, Alex 1972. "Detection of non-coding RNA with comparative genomics and the sequential closure of smooth graphs in Cartesian currents." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/30071.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2003.
Includes bibliographical references (p. 95-99).
In the field of genomics, this thesis presents algorithms for identifying non-coding RNA (ncRNA) genes. It describes a rapid and highly reliable comparative statistical method for identification of functionally significant base pairs in ncRNA genes in multiple sequence alignments of cross-species homologs, a divide-and-conquer approach to optimal assembly of exon predictions with O(n log n) time-complexity, (the standard algorithm for exon assembly has O(n²) time-complexity for ncRNA exon predictions,) and highly accurate statistical tests for exon boundaries based on recognition of non-contiguous patterns in known examples. It also describes a method for scanning cDNA for ncRNA genes. In the field of geometric measure theory, it proves that the set of cartesian currents given by integration over the graphs of smooth functions is dense in the set of all cartesian currents.
by Alex Coventry.
Ph.D.
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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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Huang, Y. H. "Computational detection of non-coding RNAs in genomes." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604701.

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This thesis is devoted to assessing available approaches and trying new solutions for finding ncRNAs in genomes. In the first half of this thesis, reasons that may contribute to the slow progress of genome-wide ncRNA finding are explored. A comprehensive analysis on a genome-wide scale of the credibility of currently used signals for classifying ncRNAs is conducted. Two factors, conservation of ncRNAs in human-mouse synthetic regions and abundance of covariations between human-mouse synteny-conserved ncRNAs, are evaluated. The result reveals that current comparative-genomics-based methods may not be able to find ncRNAs effectively in mammalian genomes. In addition, possible genomic features that could distinguish real ncRNAs from pseudogenes are investigated. Two different criteria, distribution of bit scores and physical clustering in genomes, are applied to filter out tRNA pseudogenes and to enrich bona-fide tRNA genes. Physiological roles of the tRNA genes in human-mouse synteny-conserved clusters are discussed and the degradation patterns of tRNA pseudogenes are analyzed. In the second half of this thesis, computational techniques are applied to model signals that may be potentially useful for genome-wide ncRNA finding. A sparse Bayesian learning algorithm, Eponine, is applied to model the transcription start sites of mammalian ncRNA genes that are transcribed by RNA polymerase III. In addition to modelling cis-regulatory elements for transcription, a new computational module, which extends the capability of Eponine to learn motifs consisting of both primary sequences and RNA secondary structures, is created. The capability of this new module is demonstrated by applying it to analyze several known cases of ncRNA motifs. The strength and the weakness of applying this new computational approach for finding ncRNAs are discussed.
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Kuo, Chao-Chung Verfasser], Filho Ivan Gesteira [Akademischer Betreuer] [Costa, Martin [Akademischer Betreuer] Zenke, and Björn [Akademischer Betreuer] Usadel. "Computational detection of triple helix binding domains in long non-coding RNAs / Chao-Chung Kuo ; Ivan Gesteira Costa Filho, Martin Zenke, Björn Usadel." Aachen : Universitätsbibliothek der RWTH Aachen, 2019. http://d-nb.info/1211487601/34.

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Kuo, Chao-Chung [Verfasser], Filho Ivan Gesteira [Akademischer Betreuer] Costa, Martin [Akademischer Betreuer] Zenke, and Björn [Akademischer Betreuer] Usadel. "Computational detection of triple helix binding domains in long non-coding RNAs / Chao-Chung Kuo ; Ivan Gesteira Costa Filho, Martin Zenke, Björn Usadel." Aachen : Universitätsbibliothek der RWTH Aachen, 2019. http://d-nb.info/1211487601/34.

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Mosig, Axel, Katrin Sameith, and Peter F. Stadler. "Fragrep: An Efficient Search Tool for Fragmented Patterns in Genomic Sequences." 2006. https://ul.qucosa.de/id/qucosa%3A32010.

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Many classes of non-coding RNAs (ncRNAs; including Y RNAs, vault RNAs, RNase P RNAs, and MRP RNAs, as well as a novel class recently discovered in Dictyostelium discoideum) can be characterized by a pattern of short but well-conserved sequence elements that are separated by poorly conserved regions of sometimes highly variable lengths. Local alignment algorithms such as BLAST are therefore ill-suited for the discovery of new homologs of such ncRNAs in genomic sequences. The Fragrep tool instead implements an efficient algorithm for detecting the pattern fragments that occur in a given order. For each pattern fragment, the mismatch tolerance and bounds on the length of the intervening sequences can be specified separately. Furthermore, matches can be ranked by a statistically well-motivated scoring scheme.
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Book chapters on the topic "Non-coding RNA detection"

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Huang, Rui, Yihao Wang, Yaqi Deng, and Jianfeng Shen. "Detection of Long Noncoding RNA Expression by." In Long Non-Coding RNAs, 35–42. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1697-0_5.

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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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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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Lindemann, Jennifer, Irene K. Yan, and Tushar Patel. "Detection of Circulating RNA Using Nanopore Sequencing." In Long Non-Coding RNAs in Cancer, 273–84. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1581-2_19.

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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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Trotter, Megan, Clair Harris, Marissa Cloutier, Milan Samanta, and Sundeep Kalantry. "Highly Resolved Detection of Long Non-coding RNAs In Situ." In Long Non-Coding RNAs, 123–44. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1697-0_12.

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Dalmay, Tamas. "Detection of Small Non-coding RNAs." In Plant Developmental Biology, 265–74. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-765-5_18.

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Perdomo, Catalina, Joshua Campbell, and Frank Schembri. "Detecting Noncoding RNA Expression: From Arrays to Next-Generation Sequencing." In Non-coding RNAs and Cancer, 25–44. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8444-8_3.

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Nielsen, Boye Schnack, Jesper Larsen, Jakob Høffding, Son Ly Nhat, Natasha Helleberg Madsen, Trine Møller, Bjørn Holst, and Kim Holmstrøm. "Detection of lncRNA by LNA-Based In Situ Hybridization in Paraffin-Embedded Cancer Cell Spheroids." In Long Non-Coding RNAs in Cancer, 123–37. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1581-2_8.

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Bourgeois, Gabrielle, Florian Chardon, Anne-Sophie Tillault, and Magali Blaud. "Detection and Labeling of Small Non-Coding RNAs by Splinted Ligation." In Methods in Molecular Biology, 65–72. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2547-6_7.

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Conference papers on the topic "Non-coding RNA detection"

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Reilly, Christopher, Rochelle A. Perera, Joseph Mazar, and Ranjan J. Perera. "Abstract 2106: Long non-coding RNA signatures for melanoma detection in humans." In Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/1538-7445.am2012-2106.

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Orjalo, Arturo V., and Hans E. Johansson. "Abstract A2-44: Stellaris® RNA fluorescence in situ hybridization (RNA FISH) for the detection of long non coding RNA biomarkers." In Abstracts: AACR Special Conference: Translation of the Cancer Genome; February 7-9, 2015; San Francisco, CA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.transcagen-a2-44.

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Lv, Jie, Hongbo Liu, and Qiong Wu. "Long intergenic non-coding RNA detection benefited from integrative modeling of (Epi)genomic data." In 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2013. http://dx.doi.org/10.1109/fskd.2013.6816292.

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Schulte, Christian, Temo Barwari, Abhishek Joshi, Xiaoke Yin, Anna Zampetaki, Konstantinos Theofilatos, Javier Barallobre-Barreiro, et al. "122 Non-coding rnas versus protein biomarkers for early detection of myocardial injury." In British Cardiovascular Society Annual Conference ‘High Performing Teams’, 4–6 June 2018, Manchester, UK. BMJ Publishing Group Ltd and British Cardiovascular Society, 2018. http://dx.doi.org/10.1136/heartjnl-2018-bcs.121.

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