Relevant bibliographies by topics / Bioinformatics, RNA

Contents

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

Academic literature on the topic 'Bioinformatics, RNA'

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

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Journal articles on the topic "Bioinformatics, RNA"

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Backofen, Rolf, Fabian Amman, Fabrizio Costa, Sven Findeiß, Andreas S. Richter, and Peter F. Stadler. "Bioinformatics of prokaryotic RNAs." RNA Biology 11, no. 5 (2014): 470–83. http://dx.doi.org/10.4161/rna.28647.

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Ji, Fei, and Ruslan I. Sadreyev. "RNA-seq: Basic Bioinformatics Analysis." Current Protocols in Molecular Biology 124, no. 1 (2018): e68. http://dx.doi.org/10.1002/cpmb.68.

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Marz, Manja, Niko Beerenwinkel, Christian Drosten, et al. "Challenges in RNA virus bioinformatics." Bioinformatics 30, no. 13 (2014): 1793–99. http://dx.doi.org/10.1093/bioinformatics/btu105.

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Zok, Tomasz. "BioCommons: a robust java library for RNA structural bioinformatics." Bioinformatics 37, no. 17 (2021): 2766–67. http://dx.doi.org/10.1093/bioinformatics/btab069.

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Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.
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Muckstein, U., H. Tafer, J. Hackermuller, S. H. Bernhart, P. F. Stadler, and I. L. Hofacker. "Thermodynamics of RNA-RNA binding." Bioinformatics 22, no. 10 (2006): 1177–82. http://dx.doi.org/10.1093/bioinformatics/btl024.

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Menzel, Peter, Stefan E. Seemann, and Jan Gorodkin. "RILogo: visualizing RNA–RNA interactions." Bioinformatics 28, no. 19 (2012): 2523–26. http://dx.doi.org/10.1093/bioinformatics/bts461.

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Huntley, Rachael P., Barbara Kramarz, Tony Sawford, et al. "Expanding the horizons of microRNA bioinformatics." RNA 24, no. 8 (2018): 1005–17. http://dx.doi.org/10.1261/rna.065565.118.

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Gawronski, Alexander R., Michael Uhl, Yajia Zhang, et al. "MechRNA: prediction of lncRNA mechanisms from RNA–RNA and RNA–protein interactions." Bioinformatics 34, no. 18 (2018): 3101–10. http://dx.doi.org/10.1093/bioinformatics/bty208.

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Evers, D., and R. Giegerich. "RNA movies: visualizing RNA secondary structure spaces." Bioinformatics 15, no. 1 (1999): 32–37. http://dx.doi.org/10.1093/bioinformatics/15.1.32.

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Sühnel, Jürgen. "Bioinformatik. Methoden zur Vorhersage von RNA- und Proteinstrukturen (Bioinformatics. Methods for RNA and protein structure prediction)." Bioelectrochemistry 61, no. 1-2 (2003): 110–11. http://dx.doi.org/10.1016/j.bioelechem.2003.09.003.

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Dissertations / Theses on the topic "Bioinformatics, RNA"

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Mathew, Sumi. "A method to identify the non-coding RNA gene for U1 RNA in species in which it has not yet been found." Thesis, University of Skövde, School of Humanities and Informatics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-37.

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<p>Background</p><p>Non coding RNAs are the RNA molecules that do not code for proteins but play structural, catalytic or regulatory roles in the organisms in which they are found. These RNAs generally conserve their secondary structure more than their primary sequence. It is possible to look for protein coding genes using sequence signals like promoters, terminators, start and stop codons etc. However, this is not the case with non coding RNAs since these signals are weakly conserved in them. Hence the situation with non coding RNAs is more challenging. Therefore a protocol is devised to identify U1 RNA in species not previously known to have it.</p><p>Results</p><p>It is sufficient to use the covariance models to identify non coding RNAs but they are very slow and hence a filtering step is needed before using the covariance models to reduce the search space for identifying these genes. The protocol for identifying U1 RNA genes employs for the filtering a pattern matcher RNABOB that can conduct secondary structure pattern searches. The descriptor for RNABOB is made automatically such that it can also represent the bulges and interior loops in helices of RNA. The protocol is compared with the Rfam and Weinberg & Ruzzo approaches and has been able to identify new U1 RNA homologues in the Apicomplexan group where it has not previously been found.</p><p>Conclusions</p><p>The method has been used to identify the gene for U1 RNA in certain species in which it has not been detected previously. The identified genes may be further analyzed by wet laboratory techniques for the confirmation of their existence.</p><p>4</p>
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Liu, Tsunglin. "Physics and bioinformatics of RNA." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1141407392.

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Huang, Xiaolan. "BIOINFORMATICS INVESTIGATION OF RNA PSEUDOKNOTS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1463.

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Pseudoknots are a special kind of RNA structures that play functional roles in a wide variety of biological processes. Pseudoknots are best known for their involvement in the −1 programed ribosomal frameshifting (−1 PRF) and stop codon readthrough translational recoding events as the stimulatory structures. In this dissertation, three large scale bioinformatics investigations were carried out on the roles of pseudoknots in the −1 PRF, as well as stop codon readthrough, recoding mechanisms in viral and human mRNAs. To meet the specific needs of the bioinformatics investigations, a new algorithm and method for the detection of RNA pseudoknots has been developed. The new approach differs from all existing pseudoknot detection programs in that it is capable of identifying all potential pseudoknots in any given RNA sequence with no length limitation, in a time efficient manner. This capability is essential for large scale applications in which large datasets of long RNA sequences are analyzed. The algorithm and method have been implemented, with different flavors, in three large scale sequence analysis investigations. The three datasets of mRNA sequences are: 1) full-length genomic mRNA sequences of all animal viruses known or expected to use the −1 PRF and stop codon readthrough recoding mechanisms for viral protein production; 2) full-length genomic mRNA sequences of 4000 plus different strains of human immunodeficiency virus type-1 (HIV-1); 3) 34,000 plus full-length human mRNA sequences. Results from systematic sequence analysis on these three datasets prove the usefulness and robustness of the newly developed pseudoknot detection approach. A large number of previously unknown potential pseudoknots were detected in the viral and human mRNA sequences under investigation. Post detection analysis leads to new mechanistic insights and hypotheses of pseudoknot dependent translational recoding. Some unifying themes of RNA pseudoknot structures in general are also uncovered. The results provide solid basis for further experimental and bioinformatics studies in the future.
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Freyhult, Eva. "New techniques for analysing RNA structure /." Uppsala, 2004. http://www.math.uu.se/research/pub/Freyhult1.pdf.

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Michalik, Juraj. "Non-redundant sampling in RNA Bioinformatics." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX009/document.

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Un échantillonnage statistique est central à de nombreuses méthodes algorithmiques pour la bioinformatique structurale des ARNs, où ils sont couramment utilisés pour identifier des modèles structuraux importants, fournir des résumés des espaces de repliement ou approcher des quantités d'intérêt dans l'équilibre thermodynamique. Dans tous ces exemples, la redondance dans l'ensemble échantillonné est non-informative et inefficace, limitant la portée des applications des méthodes existantes. Dans cette thèse, nous introduisons le concept de l'échantillonnage non-redondante et nous explorons ses applications et conséquences en bioinformatique des ARN.Nous commençons par introduire formellement le concept d'échantillonnage non-redondante et nous démontrons que tout algorithme échantillonnant dans la distribution de Boltzmann peut être modifié en une version non-redondante. Son implémentation repose sur une structure de données spécifique et la modification d'une remontée stochastique pour fournir l'ensemble des structures uniques, avec la même complexité.Nous montrons alors une exemple pratique en implémentant le principe d'échantillonnage non-redondant au sein d'un algorithme combinatoire qui échantillonne des structures localement optimales. Nous exploitons cet outil pour étudier la cinétique des ARN, modélisant des espaces de repliement générés à partir des structures localement optimales. Ces structures agissent comme des pièges cinétiques, rendant leur prise en compte essentielle pour analyser la dynamique des ARN. Des résultats empirique montrent que des espaces de repliement générés à partir des échantillons non-redondants sont plus proches de la réalité que ceux obtenus par un échantillonnage classique.Nous considérons ensuite le problème du calcul efficace d'estimateurs statistiques à partir d'échantillons non redondants. L'absence de la redondance signifie que l'estimateur naïf, obtenu en moyennant des quantités observés dans l'échantillon, est erroné. Par contre, nous établissons un estimateur non-trivial non-biaisé spécifique aux échantillons non-redondants suivant la distribution de Boltzmann. Nous montrons que l'estimateur des échantillons non-redondants est plus efficace que l'estimateur naïf, notamment dans les cas où la majorité des l'espace de recherche est échantillonné.Finalement, nous introduisons l'algorithme d'échantillonnage, avec sa contre-partie non-redondante, pour des structures secondaires présentant des pseudonoeuds de type simple. Des pseudonoeuds sont typiquement omis pour des raisons d'efficacité, bien que beaucoup d'entre eux possèdent une grande importance biologique. Nos commençons par proposer une schéma de programmation dynamique qui permet d'énumérer tous les pseudonoeuds composés de deux hélices pouvant contenir des bases non-appariés qui s'entrecroisent. Ce schéma généralise la proposition de Reeders et Giegerich, choisi pour sa base complexité temporelle et spatiale. Par la suite, nous expliquons comment adapter cette décomposition à un algorithme d'échantillonnage statistique pour des pseudonoeuds simples. Finalement, nous présentons des résultats préliminaires et nous discutons sur l'extension de principe non-redondant dnas ce contexte.Le travail présenté dans cette thèse ouvre non seulement la porte à l'analyse cinétique des séquences d'ARN plus longues, mais aussi l'analyse structurale plus détaillée des séquences d'ARN en général. L'échantillonnage non-redondant peut être employé pour analyser des espaces de recherche pour des problèmes combinatoires susceptibles à l'échantillonnage statistique, y inclus virtuellement tous problèmes solvables par la programmation dynamique. Les principes d'échantillonnage non-redondant sont robustes et typiquement faciles à implémenter, comme démontré par l'inclusion d'échantillonnage non-redondant dans les versions récentes de Vienna package populaire<br>Sampling methods are central to many algorithmic methods in structural RNA bioinformatics, where they are routinely used to identify important structural models, provide summarized pictures of the folding landscapes, or approximate quantities of interest at the thermodynamic equilibrium.In all of these examples, redundancy within sampled sets is uninformative and computationally wasteful, limiting the scope of application of existing methods.In this thesis, we introduce the concept of non-redundant sampling, and explore its applications and consequences in RNA bioinformatics.We begin by formally introducing the concept of non-redundant sampling and demonstrate that any algorithm sampling in Boltzmann distribution can be modified into non-redundant variant. Its implementation relies on a specific data structure and a modification of the stochastic backtrack to return the set of unique structures, with the same complexity.We then show a practical example by implementing the non-redundant principle into a combinatorial algorithm that samples locally optimal structures. We use this tool to study the RNA kinetics by modeling the folding landscapes generated from sets of locally optimal structures. These structures act as kinetic traps, influencing the outcome of the RNA kinetics, thus making their presence crucial. Empirical results show that the landscapes generated from the non-redundant samples are closer to the reality than those obtained by classic approaches.We follow by addressing the problem of the efficient computation of the statistical estimates from non-redundant sampling sets. The absence of redundancy means that the naive estimator, obtained by averaging quantities observed in a sample, is erroneous. However we establish a non-trivial unbiased estimator specific to a set of unique Boltzmann distributed secondary structures. We show that the non-redundant sampling estimator performs better than the naive counterpart in most cases, specifically where most of the search space is covered by the sampling.Finally, we introduce a sampling algorithm, along with its non-redundant counterpart, for secondary structures featuring simple-type pseudoknots. Pseudoknots are typically omitted due to complexity reasons, yet many of them have biological relevance. We begin by proposing a dynamic programming scheme that allows to enumerate all recursive pseudoknots consisting of two crossing helices, possibly containing unpaired bases. This scheme generalizes the one proposed by Reeders and Giegerich, chosen for its low time and space complexities. We then explain how to adapt this decomposition into a statistical sampling algorithm for simple pseudoknots. We then present preliminary results, and discuss about extensions of the non-redundant principle in this context.The work presented in this thesis not only opens the door towards kinetics analysis for longer RNA sequences, but also more detailed structural analysis of RNAs in general. Non-redundant sampling can be applied to analyze search spaces for combinatorial problems amenable to statistical sampling, including virtually any problem solved by dynamic programming. Non-redundant sampling principles are robust and typically easy to implement, as demonstrated by the inclusion of non-redundant sampling in recent versions of the popular Vienna package
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Zhou, Yu. "Application of RNA Bioinformatics in decoding RNA structure and regulation." Paris 11, 2008. http://www.theses.fr/2008PA112234.

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Ma thèse porte sur le développement de méthodologies informatiques et bioinformatiques pour résoudre des problèmes provenant de questions biologiques liées à l’ARN, telles que la prédiction de structures, l’identification de structures communes, la découverte de cibles des micro-ARN, la prédiction de la régulation de l’épissage, et le design (ou repliement inverse) d'ARN. Le premier chapitre concerne la mise en place d’une méthode itérative pour la prédiction des structures secondaires des introns de groupes 1, incluant les pseudo-nœuds, et la développement d’une base de données complète sur les introns de groupe 1. Dans le deuxième chapitre, je décris mon travail sur l’analyse bioinformatique de la structure des sites d’incorporation de la Pyrrolysine, le 22ème acide aminé, dans des gènes d’archae. Les troisième et quatrième chapitres sont consacrés au développement et à la mise en œuvre de deux méthodes d’analyse de données expérimentales pour la recherche, dans les séquences d’ARN, de cibles de micro-ARN, et de sites de fixation de protéines impliquées dans le processus d’épissage des introns. Enfin, le cinquième chapitre présente un algorithme de design de structures d’ARN avec des contraintes de motifs, faisant appel à des manipulations d’automates et de grammaires non contextuelles<br>My thesis focuses on the application of RNA bioinformatics analysis to solve the problems originated from biological requirements, ranging from structure prediction, common structure identification, microRNA target discovery, splicing regulation prediction, and RNA design (inverse folding). The first chapter concerns the establishment of an iterative method for the secondary structure prediction of group I introns including pseudo-knots, and the development of a comprehensive group I intron sequence and structure database. In the second chapter, I describe my work on bioinformatics analysis of the Pyrrolysine (Pyl, 22nd amino acid) insertion structure in Pyl-associated genes in archaea. The third and fourth chapters are devoted to develop two methods of experimental data analysis for identification of micro-RNA target sites, and for determination of binding sites of a RNA binding protein implicated in pre-mRNA splicing, independently. Finally, the fifth chapter presents an algorithm for RNA design under motif constraints, involving manipulation of automata and context-free grammars
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Baez, William David. "RNA Secondary Structures: from Biophysics to Bioinformatics." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1525714439675315.

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Freyhult, Eva. "A Study in RNA Bioinformatics : Identification, Prediction and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8305.

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Rahrig, Ryan Robert. "Automated Alignment of RNA 3D Structures." Bowling Green State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1276873588.

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Starmer, Joshua Mr. "What can RNA hybrids tell us about translation?" NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-10202006-155443/.

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Molecular biologists have been observing interactions between messenger RNA (mRNA) molecules and other non-coding RNA molecules for quite some time. Here I revisit some of the classical hybridizations between the 16S ribosomal RNA (rRNA) and mRNA during initiation, as well as investigate the interactions between small interfering RNA (siRNA) molecules and mRNA. In reviewing rRNA-mRNA interactions, I observed that the majority of both bacterial and eukaryote genes can bind at the start codon. This novel result lead to a method for improving genome annotation as well as a new theory of translation initiation. The examination of siRNA-mRNA interactions lead to new criteria for predicting an siRNA's efficacy.
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Books on the topic "Bioinformatics, RNA"

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Picardi, Ernesto, ed. RNA Bioinformatics. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2291-8.

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Picardi, Ernesto, ed. RNA Bioinformatics. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1307-8.

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Bioinformatics: Genomics and post-genomics. John Wiley & Sons, 2006.

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Bioinformatics: The impact of accurate quantification on proteomic and genetic analysis and research. Apple Academic Press, 2014.

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Ecole d'été de physique théorique (Les Houches, Haute-Savoie, France) (82nd 2004). Multiple aspects of DNA and RNA: From biophysics to bioinformatics ; École d'été de physique des Houches, session LXXXII, 2-27 August 2004. Elsevier, 2005.

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RNA sequence, structure, and function: Computational and bioinformatic methods. Humana Press, 2014.

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Gorodkin, Jan, and Walter L. Ruzzo, eds. RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods. Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-709-9.

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T, Batey Robert, Woodson Sarah A, and SpringerLink (Online service), eds. Non-Protein Coding RNAs. Springer Berlin Heidelberg, 2009.

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Bioinformatics. John Wiley & Sons, Ltd., 2007.

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Dardel, Frédéric, and François Képès. Bioinformatics: Genomics and Post-Genomics. Wiley, 2006.

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Book chapters on the topic "Bioinformatics, RNA"

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Rna." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-4-4.

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Introduction to Bioinformatics." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-1-1.

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "New Areas of Bioinformatics." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-8-8.

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Kavanaugh, Laura A., and Uwe Ohler. "Predicting Non-coding RNA Transcripts." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_4.

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Scott, Lincoln G., and Mirko Hennig. "RNA Structure Determination by NMR." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_2.

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Göbel, Ulrike, Christian V. Forst, and Peter Schuster. "Structural constraints and neutrality in RNA." In Bioinformatics. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033214.

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Phylogenetics." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-3-3.

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Pseudoknot." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-5-5.

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Pseudoknot Grammar." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-7-7.

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Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Glossary." In Bioinformatics and RNA. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-nan-9.

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Conference papers on the topic "Bioinformatics, RNA"

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Ramirez, Miguel, Cristian Alejandro Rojas-Quintero, and Nelson Enrique Vera-Parra. "RNA-Seq UD: A bioinformatics plattform for RNA-Seq analysis." In 2015 10th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2015. http://dx.doi.org/10.1109/cisti.2015.7170565.

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Cheung, Kwan-Yau, Kwok-Kit Tong, Kin-Hong Lee, and Kwong-Sak Leung. "RIPGA: RNA-RNA interaction prediction using genetic algorithm." In 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2013. http://dx.doi.org/10.1109/cibcb.2013.6595401.

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Sarkar, A. K., J. Sarzynska, and A. Lahiri. "Effect of 2-Thiouridine on RNA Conformation." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.70.

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Gohil, Mahendra Kumar, Arindam Chakraborty, and Bhaskar Dasgupta. "Hyper-redundant Robots and bioinformatics: Modelling loops in RNA." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2016. http://dx.doi.org/10.1109/smc.2016.7844808.

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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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Erho, Nicholas, and Kay Wiese. "An exploration of individual RNA structural elements in RNA gene finding." In 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2010. http://dx.doi.org/10.1109/cibcb.2010.5510328.

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"Study of the root transcriptome of bread wheat using high-throughput RNA sequencing (RNA-SEQ)." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-226.

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Lijun, Jiang, Wenxian Yang, Rongshan Yu, Shiqian Wu, and Anisha Anil Lekshmy. "Comparison of three bioinformatics pipelines for DNA/RNA data processing." In 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2017. http://dx.doi.org/10.1109/iciea.2017.8282818.

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Baulin, E., P. Tikhonova, and M. Roytberg. "Machine learning for Mg2+-binding sites prediction in RNA structures." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.35.

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Benslimane, Fatiha M., Hebah Al Khatib, Dana Albatesh, et al. "Nanopore Sequencing SARS-CoV-2 Genome in Qatar." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0289.

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Background: The current pandemic, COVID-19, is cause by an RNA Coronavirus that was recently identified as SARS-CoV-2. RNA viruses tend to have a high mutation rate; the rate is around a million times greater than that of their hosts. The mutagenic potential of the virus depends on many factors, including the fidelity of nucleic acid-replicating viral enzymes, such as SARSCoV-2 RNA dependent RNA polymerase (RdRp). The rate of mutation drives viral evolution and genome variability, consequently allowing viruses to escape the immunity of the host and develop resistance to drugs. Therefore, the characterization of SARS-CoV-2 variants might lead to implement better therapeutics treatments, vaccines design and identify new diagnostics approaches. Aim: The aim of this study was to establish a fast sequencing method to identify SARS-CoV-2 mutations in Qatar. This will help to assess if there are new viral variants that are spreading in country. Methods: RNA was isolated from samples collected from Qatar COVID-19 positive patients. The Artic Network V3 primer scheme and Oxford Nanopore ligation sequencing kit were used to prepare the sequencing libraries. Libraries were loaded on to R9.4.1 flow cells and ran on a GridION. Bioinformatics analysis was done following the Artic Network SARA-CoV-2 bioinformatics tools. Results: Genome coverage of sequenced samples was &gt;80% and the depth was average at 200x. The coverage was highly dependable on sample viral load; samples of CT value lower than 30 resulted in better sequence coverage. The sequenced genomes were deposited in GISAID and were mainly clustering with genomes deposited from the UK. Sequences were compared to Illumina and sanger sequences and they showed compatible results. Conclusion: The use of ONT to sequence SARA-CoV-2 is a quick, affordable, and reliable technique to determine viral mutation. Using this technique, the first sequences from Qatar were deposited in to GISAID. Up to date, 700 genomes have been sequenced from Qatari samples.
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