Relevant bibliographies by topics / Dati NGS

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Academic literature on the topic 'Dati NGS'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Dati NGS"

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Doan, Tri. "Investigator-Completed NGS Data Analysis." Clinical OMICs 1, no. 10 (September 24, 2014): 22–23. http://dx.doi.org/10.1089/clinomi.01.10.08.

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Cybin, Aleksander, Vadim Sharov, Yuliya Putintseva, Sergey Feranchuk, and Dmitry Kuzmin. "Parallel repeats filtration algorithm of NGS ILLUMINA data." Proceedings of the Russian higher school Academy of sciences, no. 4 (December 20, 2016): 99–110. http://dx.doi.org/10.17212/1727-2769-2016-4-99-110.

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Valverde, Jose R., Jose M. Rodríguez, Alexandro Rodriguez-Rojas, Alejandro Couce, and Jesus Blazquez. "NGS data analysis: the user POV." EMBnet.journal 17, B (February 28, 2012): 15. http://dx.doi.org/10.14806/ej.17.b.263.

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Eberhard, D. "SP008 Clinical reporting of NGS data." European Journal of Cancer 49 (November 2013): S3. http://dx.doi.org/10.1016/s0959-8049(13)70086-8.

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Cantalupo, Paul G., and James M. Pipas. "Detecting viral sequences in NGS data." Current Opinion in Virology 39 (December 2019): 41–48. http://dx.doi.org/10.1016/j.coviro.2019.07.010.

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Pitluk, Zachary. "NGS Big Data Issues for Biomanufacturing." Genetic Engineering & Biotechnology News 37, no. 2 (January 15, 2017): 30–31. http://dx.doi.org/10.1089/gen.37.02.16.

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An, Omer, Kar-Tong Tan, Ying Li, Jia Li, Chan-Shuo Wu, Bin Zhang, Leilei Chen, and Henry Yang. "CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing." International Journal of Molecular Sciences 21, no. 11 (May 28, 2020): 3828. http://dx.doi.org/10.3390/ijms21113828.

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Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and a significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in FASTQ format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal.
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Brookman-Amissah, Nicola. "Generating Robust NGS Data for Personalized Medicine." Clinical OMICs 2, no. 1 (January 2015): 24–26. http://dx.doi.org/10.1089/clinomi.02.01.09.

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Kallio, Aleksi, Taavi Hupponen, Massimiliano Gentile, Jarno Tuimala, Kimmo Mattila, Ari-Matti Saren, Petri Klemelä, Ilari Scheinin, and Eija Korpelainen. "Biologist-friendly analysis software for NGS data." EMBnet.journal 19, A (April 8, 2013): 53. http://dx.doi.org/10.14806/ej.19.a.623.

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Buguliskis, Jeffrey S. "The Big Data Addiction—NGS Has It Bad." Clinical OMICs 2, no. 5 (May 2015): 12–15. http://dx.doi.org/10.1089/clinomi.02.05.06.

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Dissertations / Theses on the topic "Dati NGS"

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LAMONTANARA, ANTONELLA. "Sviluppo ed applicazione di pipilines bioinformatiche per l'analisi di dati NGS." Doctoral thesis, Università Cattolica del Sacro Cuore, 2015. http://hdl.handle.net/10280/6068.

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Lo sviluppo delle tecnologie di sequenziamento ha portato alla nascita di strumenti in grado di produrre gigabasi di dati di sequenziamento in una singola corsa. Queste tecnologie, comunemente indicate come Next Generation Sequencing o NGS, producono grandi e complessi dataset la cui analisi comporta diversi problemi a livello bioinformatico. L'analisi di questo tipo di dati richiede la messa a punto di pipelines computazionali il cui sviluppo richiede un lavoro di scripting necessario per concatenare i softwares già esistenti. Questa tesi tratta l'aspetto metodologico dell'analisi di dati NGS ottenuti con tecnologia Illumina. In particolare in essa sono state sviluppate tre pipelines bioinformatiche applicate ai seguenti casi studio: 1) uno studio di espressione genica mediante RNA-seq in "Olea europaea" finalizzato all’indagine dei meccanismi molecolari alla base dell’acclimatazione al freddo in questa specie; 2) uno studio mediante RNA-seq finalizzato all’identificazione dei polimorfismi di sequenza nel trascrittoma di due razze bovine mirato a produrre un ampio catalogo di marcatori di tipo SNPs; 3) il sequenziamento, l’assemblaggio e l’annotazione del genoma di un ceppo di Lactobacillus plantarum che mostrava potenziali proprietà probiotiche.
The advance in sequencing technologies has led to the birth of sequencing platforms able to produce gigabases of sequencing data in a single run. These technologies commonly referred to as Next Generation Sequencing or NGS produce millions of short sequences called “reads” generating large and complex datasets that pose several challenges for Bioinformatics. The analysis of large omics dataset require the development of bioinformatics pipelines that are the organization of the bioinformatics tools in computational chains in which the output of one analysis is the input of the subsequent analysis. A work of scripting is needed to chain together a group of existing software tools.This thesis deals with the methodological aspect of the data analysis in NGS sequencing performed with the Illumina technology. In this thesis three bioinformatics pipelines were developed.to the following cases of study: 1) a global transcriptome profiling of “Oleaeuropeae” during cold acclimation, aimed to unravel the molecular mechanisms of cold acclimation in this species; 2) a SNPs profiling in the transcriptome of two cattle breeds aimed to produce an extensive catalogue of SNPs; 3) the genome sequencing, the assembly and annotation of the genome of a Lactobacillus plantarum strain showing probiotic properties.
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Giannini, Simone. "Strumenti statistici per elaborazione dati su sequenziamenti di genoma umano." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12059/.

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L'analisi del DNA è una delle chiavi per la comprensione della vita e dei suoi funzionamenti. Le tecniche di sequenziamento di nuova generazione NGS permettono una analisi parallela di molte sequenze che hanno reso possibili i sequenziamenti di genomi interi e l'impiego di questi dati in una vasta gamma di studi. In questa tesi verranno descritte le principali tecniche di sequenziamento NGS. Per quanto riguarda il genoma umano si tratteranno alcune tematiche di studio di varianti affrontate dal gruppo 1000Genomes. Nella fase conclusiva si introdurranno definizioni di statistica utili nell'affrontare l'elaborazione dei dati. Inoltre vengono descritti alcuni strumenti che permettono di svolgere questo tipo di analisi.
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DENTI, LUCA. "Algorithms for analyzing genetic variability from Next-Generation Sequencing data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/263551.

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Il DNA contiene l'informazione genetica che è essenziale per il corretto sviluppo di qualsiasi organismo. Essere in grado di analizzare il DNA risulta indispensabile per comprendere le cause di malattie e tumori e per migliorare la qualità delle nostre vite. Lo sviluppo delle tecniche di sequenziamento del DNA ha rivoluzionato il modo in cui queste analisi sono eseguite. A causa dell'immensa quantità di dati biologici disponibili, oggigiorno l'informatica gioca un ruolo fondamentale nella loro analisi. Fortunatamente in molte applicazioni l'informazione biologica contenuta in una molecola di DNA può essere rappresentata come una stringa nella quale ogni carattere rappresenta un nucleotide. Il concetto di stringa è molto studiato in informatica ed è possibile sfruttare l'estesa letteratura relativa alla memorizzazione e all'analisi di stringhe per migliorare lo studio del DNA. In questo contesto, questa tesi si focalizza su due problemi che emergono dall'analisi di dati di sequenziamento: lo studio della variabilità trascrittomica dovuta allo splicing alternativo e l'analisi della variabilità genetica dovuta a variazioni genetiche quali Single Nucleotide Polymorphisms e indels. Riguardo entrambi i problemi, investighiamo due originali approcci computazionali e ne dimostriamo l'efficacia confrontandoli con i tools più utilizzati nel relativo stato dell'arte. Il nostro obiettivo è lo sviluppo di tool bioinformatici che combinano algoritmi accurati con strutture dati efficienti. Il primo problema che affrontiamo è l'identificazione di eventi di splicing alternativo a partire da dati RNA-Seq. Lo splicing alternativo gioca un ruolo fondamentale in molti aspetti della vita, dal corretto sviluppo di un individuo al sorgere di malattie. Diversamente dagli approcci proposti in letteratura che si basano sulla quantificazione di trascritti o sull'allineamento spliced contro un genoma di riferimento, proponiamo un approccio algoritmico alternativo che sfrutta l'originale concetto di allineamento a un grafo di splicing. Abbiamo implementato il nostro approccio nel tool ASGAL che allinea un sample di RNA-Seq contro il grafo di splicing di un gene e identifica gli eventi di splicing alternativo supportati dal sample andando a confrontare questi ultimi con l'annotazione del gene. ASGAL è il primo tool che allinea RNA-Seq reads a un grafo di splicing e che è in grado di identificare eventi novel di splicing anche quando un singolo trascritto per gene è supportato dal sample in input. I risultati della nostra sperimentazione dimostrano l'utilità di allineare a un grafo di splicing e la capacità del nostro tool nell'identificare eventi di splicing alternativo. Il secondo problema che affrontiamo è la genotipizzazione di un insieme di varianti note (SNPs e indels) a partire da dati di sequenziamento. Un'approfondita analisi di queste variazioni è indispensabile per comprendere la variabilità genetica fra gli individui di una popolazione e il loro fattore di rischio genetico. Gli approcci proposti in letteratura per identificare e genotipizzare varianti includono l'allineamento delle reads, una procedura che risulta computazionalmente troppo onerosa per le tipiche applicazioni cliniche. Quando non si è interessati alla scoperta di nuove varianti, è possibile evitare lo step di allineamento andando a genotipizzare solo un insieme di varianti già note e per le quali è stata già dimostrata una certa rilevanza medica. Per risolvere questo problema, abbiamo ideato un nuovo approccio alignment-free e lo abbiamo implementato nel tool MALVA. MALVA è il primo approccio alignment-free che è in grado di genotipizzare SNPs, indels e varianti multi-alleliche. Grazie alla strategia alignment-free, MALVA è molto più veloce degli approcci basati sull'allineamento, esibendo comunque un'accuratezza simile. Inoltre, rispetto agli approcci più utilizzati in letteratura, MALVA risulta molto più accurato nella genotipizzazione degli indels.
DNA contains the genetic information that is essential for the correct development of any organism. Being able to investigate DNA is of utmost importance for analyzing the reasons behind diseases and for improving the quality of life. Development of DNA sequencing technologies has revolutionized the way this kind of investigation is performed. Due to the huge amount of sequencing data available, nowadays computer science plays a key role in their analysis. Luckily, in many applications, the biological information contained in a DNA molecule can be represented as a string in which each character represents a nucleotide. Strings are a well-known and well-studied notion in computer science and therefore it is possible to exploit the huge literature related to storing and processing strings for improving the analysis of DNA. Within this context, this thesis focuses on two specific problems arising from the analysis of sequencing data: the study of transcript variability due to alternative splicing and the investigation of genetic variability among different individuals due to small variations such as Single Nucleotide Polymorphisms and indels. Regarding both these problems, we investigate two novel computational approaches by devising original strategies and we prove their efficacy by comparing them with the most used state-of-the-art approaches. In both these areas, our focus is on the development of bioinformatics tools that combine accurate algorithms with efficient data structures. The first problem we tackle is the detection of alternative splicing events from RNA-Seq data. Alternative splicing plays an important role in many different life aspects, from the correct evolution of an individual to the development of diseases. Differently from current techniques that rely on the reconstruction of transcripts or on the spliced alignment of RNA-Seq reads against a reference genome, we investigate an alternative algorithmic approach that exploits the novel notion of alignment against a splicing graph. We implemented such an approach in a tool, called ASGAL, that aligns a RNA-Seq sample against the splicing graph of a gene and then detects the alternative splicing events supported by the sample by comparing the alignments with the gene annotation. ASGAL is the first tool that aligns reads against a splicing graph and that is able to detect novel alternative splicing events even when only a single transcript per gene is supported by the sample. The results of our experiments show the usefulness of aligning reads against a splicing graph and prove the ability of the proposed approach in detecting alternative splicing events. The second problem we tackle is the genotyping of a set of known Single Nucleotide Polymorphisms and indels from sequencing data. An in-depth analysis of these variants allows to understand genetic variability among different individuals of a population and their genetic risks factors for diseases. Standard pipelines for variant discovery and genotyping include read alignment, a computationally expensive procedure that is too time consuming for typical clinical applications. When variant discovery is not desired, it is possible to avoid read alignment by genotyping only the set of known variants that are already established to be of medical relevance. To solve this problem, we devised a novel alignment-free algorithmic approach and we implemented it in a bioinformatic tool, called MALVA. MALVA is the first alignment-free approach that is able to genotype SNPs, indels, and multi-allelic variants. Thanks to its alignment-free strategy, MALVA requires one order of magnitude less time than alignment-based pipelines to genotype a donor individual while achieving similar accuracy. Remarkably, on indels it provides even better results than the most widely adopted approaches.
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Bombonato, Juliana Rodrigues. "Dados filogenômicos para inferência de relações evolutivas entre espécies do gênero Cereus Mill. (Cactaceae, Cereeae)." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/59/59139/tde-08062018-160032/.

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Estudos filogenômicos usando Sequenciamento de Próxima Geração (do inglês, Next Generation Sequencing - NGS) estão se tornando cada vez mais comuns. O uso de marcadores oriundos do sequenciamento de DNA de uma biblioteca genômica reduzida, neste caso ddRADSeq (do inglês, Double Digestion Restriction Site Associated DNA Sequencing), para este fim é promissor, pelo menos considerando sua relação custo-benefício em grandes conjuntos de dados de grupos não-modelo, bem como a representação genômica recuperada. Aqui usamos ddRADSeq para inferir a filogenia em nível de espécie do gênero Cereus (Cactaceae). Esse gênero compreende em cerca de 25 espécies reconhecidas predominantemente sul-americanas distribuídas em quatro subgêneros. Nossa amostra inclui representantes de Cereus, além de espécies dos gêneros próximos, Cipocereus e Praecereus, além de grupos externos. A biblioteca ddRADSeq foi preparada utilizando as enzimas EcoRI e HPAII. Após o controle de qualidade (tamanho e quantificação dos fragmentos), a biblioteca foi sequenciada no Illumina HiSeq 2500. O processamento de bioinformática a partir de arquivos FASTQ incluiu o controle da presença de adaptadores, filtragem por qualidade (softwares FastQC, MultiQC e SeqyClean) e chamada de SNPs (software iPyRAD). Três cenários de permissividade a dados faltantes foram realizados no iPyRAD, recuperando conjuntos de dados com 333 (até 40% de dados perdidos), 1440 (até 60% de dados perdidos) e 6141 (até 80% de dados faltantes) loci. Para cada conjunto de dados, árvores de Máxima Verossimilhança (MV) foram geradas usando duas supermatrizes: SNPs ligados e Loci. Em geral, observamos algumas inconsistências entre as árvores ML geradas em softwares distintos (IQTree e RaxML) ou baseadas no tipo de matriz distinta (SNPs ligados e Loci). Por outro lado, a precisão e a resolução, foram melhoradas usando o maior conjunto de dados (até 80% de dados perdidos). Em geral, apresentamos uma filogenia com resolução inédita para o gênero Cereus, que foi resolvido como um provável grupo monofilético, composto por quatro clados principais e com alto suporte em suas relações internas. Além disso, nossos dados contribuem para agregar informações sobre o debate sobre o aumento de dados faltantes para conduzir a análise filogenética com loci RAD.
Phylogenomics studies using Next Generation Sequencing (NGS) are becoming increasingly common. The use of Double Digest Restriction Site Associated DNA Sequencing (ddRADSeq) markers to this end is promising, at least considering its cost-effectiveness in large datasets of non-model groups as well as the genome-wide representation recovered in the data. Here we used ddRADSeq to infer the species level phylogeny of genus Cereus (Cactaceae). This genus comprises about 25 species recognized predominantly South American species distributed into four subgenera. Our sample includes representatives of Cereus, in addition to species from the closely allied genera Cipocereus and Praecereus, besides outgroups. The ddRADSeq library was prepared using EcoRI and HPAII enzymes. After the quality control (fragments size and quantification) the library was sequenced in Illumina HiSeq 2500. The bioinformatic processing on raw FASTQ files included adapter trimming, quality filtering (FastQC, MultiQC and SeqyClean softwares) and SNPs calling (iPyRAD software). Three scenarios of permissiveness to missing data were carry out in iPyRAD, recovering datasets with 333 (up tp 40% missing data), 1440 (up to 60% missing data) and 6141 (up to 80% missing data) loci. For each dataset, Maximum Likelihood (ML) trees were generated using two supermatrices: SNPs linked and Loci. In general, we observe few inconsistences between ML trees generated in distinct softwares (IQTree and RaxML) or based in distinctive matrix type (SNP linked and Loci). On the other hand, the accuracy and resolution were improved using the larger dataset (up to 80% missing data). Overall, we present a phylogeny with unprecedent resolution for genus Cereus, which was resolved as a likely monophyletic group, composed by four main clades and with high support in their internal relationships. Further, our data contributes to aggregate information on the debate about to increasing missing data to conduct phylogenetic analysis with RAD loci.
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Alic, Andrei Stefan. "Improved Error Correction of NGS Data." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/67630.

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[EN] The work done for this doctorate thesis focuses on error correction of Next Generation Sequencing (NGS) data in the context of High Performance Computing (HPC). Due to the reduction in sequencing cost, the increasing output of the sequencers and the advancements in the biological and medical sciences, the amount of NGS data has increased tremendously. Humans alone are not able to keep pace with this explosion of information, therefore computers must assist them to ease the handle of the deluge of information generated by the sequencing machines. Since NGS is no longer just a research topic (used in clinical routine to detect cancer mutations, for instance), requirements in performance and accuracy are more stringent. For sequencing to be useful outside research, the analysis software must work accurately and fast. This is where HPC comes into play. NGS processing tools should leverage the full potential of multi-core and even distributed computing, as those platforms are extensively available. Moreover, as the performance of the individual core has hit a barrier, current computing tendencies focus on adding more cores and explicitly split the computation to take advantage of them. This thesis starts with a deep analysis of all these problems in a general and comprehensive way (to reach out to a very wide audience), in the form of an exhaustive and objective review of the NGS error correction field. We dedicate a chapter to this topic to introduce the reader gradually and gently into the world of sequencing. It presents real problems and applications of NGS that demonstrate the impact this technology has on science. The review results in the following conclusions: the need of understanding of the specificities of NGS data samples (given the high variety of technologies and features) and the need of flexible, efficient and accurate tools for error correction as a preliminary step of any NGS postprocessing. As a result of the explosion of NGS data, we introduce MuffinInfo. It is a piece of software capable of extracting information from the raw data produced by the sequencer to help the user understand the data. MuffinInfo uses HTML5, therefore it runs in almost any software and hardware environment. It supports custom statistics to mould itself to specific requirements. MuffinInfo can reload the results of a run which are stored in JSON format for easier integration with third party applications. Finally, our application uses threads to perform the calculations, to load the data from the disk and to handle the UI. In continuation to our research and as a result of the single core performance limitation, we leverage the power of multi-core computers to develop a new error correction tool. The error correction of the NGS data is normally the first step of any analysis targeting NGS. As we conclude from the review performed within the frame of this thesis, many projects in different real-life applications have opted for this step before further analysis. In this sense, we propose MuffinEC, a multi-technology (Illumina, Roche 454, Ion Torrent and PacBio -experimental), any-type-of-error handling (mismatches, deletions insertions and unknown values) corrector. It surpasses other similar software by providing higher accuracy (demonstrated by three type of tests) and using less computational resources. It follows a multi-steps approach that starts by grouping all the reads using a k-mers based metric. Next, it employs the powerful Smith-Waterman algorithm to refine the groups and generate Multiple Sequence Alignments (MSAs). These MSAs are corrected by taking each column and looking for the correct base, determined by a user-adjustable percentage. This manuscript is structured in chapters based on material that has been previously published in prestigious journals indexed by the Journal of Citation Reports (on outstanding positions) and relevant congresses.
[ES] El trabajo realizado en el marco de esta tesis doctoral se centra en la corrección de errores en datos provenientes de técnicas NGS utilizando técnicas de computación intensiva. Debido a la reducción de costes y el incremento en las prestaciones de los secuenciadores, la cantidad de datos disponibles en NGS se ha incrementado notablemente. La utilización de computadores en el análisis de estas muestras se hace imprescindible para poder dar respuesta a la avalancha de información generada por estas técnicas. El uso de NGS transciende la investigación con numerosos ejemplos de uso clínico y agronómico, por lo que aparecen nuevas necesidades en cuanto al tiempo de proceso y la fiabilidad de los resultados. Para maximizar su aplicabilidad clínica, las técnicas de proceso de datos de NGS deben acelerarse y producir datos más precisos. En este contexto es en el que las técnicas de comptuación intensiva juegan un papel relevante. En la actualidad, es común disponer de computadores con varios núcleos de proceso e incluso utilizar múltiples computadores mediante técnicas de computación paralela distribuida. Las tendencias actuales hacia arquitecturas con un mayor número de núcleos ponen de manifiesto que es ésta una aproximación relevante. Esta tesis comienza con un análisis de los problemas fundamentales del proceso de datos en NGS de forma general y adaptado para su comprensión por una amplia audiencia, a través de una exhaustiva revisión del estado del arte en la corrección de datos de NGS. Esta revisión introduce gradualmente al lector en las técnicas de secuenciación masiva, presentando problemas y aplicaciones reales de las técnicas de NGS, destacando el impacto de esta tecnología en ciencia. De este estudio se concluyen dos ideas principales: La necesidad de analizar de forma adecuada las características de los datos de NGS, atendiendo a la enorme variedad intrínseca que tienen las diferentes técnicas de NGS; y la necesidad de disponer de una herramienta versátil, eficiente y precisa para la corrección de errores. En el contexto del análisis de datos, la tesis presenta MuffinInfo. La herramienta MuffinInfo es una aplicación software implementada mediante HTML5. MuffinInfo obtiene información relevante de datos crudos de NGS para favorecer el entendimiento de sus características y la aplicación de técnicas de corrección de errores, soportando además la extensión mediante funciones que implementen estadísticos definidos por el usuario. MuffinInfo almacena los resultados del proceso en ficheros JSON. Al usar HTML5, MuffinInfo puede funcionar en casi cualquier entorno hardware y software. La herramienta está implementada aprovechando múltiples hilos de ejecución por la gestión del interfaz. La segunda conclusión del análisis del estado del arte nos lleva a la oportunidad de aplicar de forma extensiva técnicas de computación de altas prestaciones en la corrección de errores para desarrollar una herramienta que soporte múltiples tecnologías (Illumina, Roche 454, Ion Torrent y experimentalmente PacBio). La herramienta propuesta (MuffinEC), soporta diferentes tipos de errores (sustituciones, indels y valores desconocidos). MuffinEC supera los resultados obtenidos por las herramientas existentes en este ámbito. Ofrece una mejor tasa de corrección, en un tiempo muy inferior y utilizando menos recursos, lo que facilita además su aplicación en muestras de mayor tamaño en computadores convencionales. MuffinEC utiliza una aproximación basada en etapas multiples. Primero agrupa todas las secuencias utilizando la métrica de los k-mers. En segundo lugar realiza un refinamiento de los grupos mediante el alineamiento con Smith-Waterman, generando contigs. Estos contigs resultan de la corrección por columnas de atendiendo a la frecuencia individual de cada base. La tesis se estructura por capítulos cuya base ha sido previamente publicada en revistas indexadas en posiciones dest
[CAT] El treball realitzat en el marc d'aquesta tesi doctoral se centra en la correcció d'errors en dades provinents de tècniques de NGS utilitzant tècniques de computació intensiva. A causa de la reducció de costos i l'increment en les prestacions dels seqüenciadors, la quantitat de dades disponibles a NGS s'ha incrementat notablement. La utilització de computadors en l'anàlisi d'aquestes mostres es fa imprescindible per poder donar resposta a l'allau d'informació generada per aquestes tècniques. L'ús de NGS transcendeix la investigació amb nombrosos exemples d'ús clínic i agronòmic, per la qual cosa apareixen noves necessitats quant al temps de procés i la fiabilitat dels resultats. Per a maximitzar la seua aplicabilitat clínica, les tècniques de procés de dades de NGS han d'accelerar-se i produir dades més precises. En este context és en el que les tècniques de comptuación intensiva juguen un paper rellevant. En l'actualitat, és comú disposar de computadors amb diversos nuclis de procés i inclús utilitzar múltiples computadors per mitjà de tècniques de computació paral·lela distribuïda. Les tendències actuals cap a arquitectures amb un nombre més gran de nuclis posen de manifest que és esta una aproximació rellevant. Aquesta tesi comença amb una anàlisi dels problemes fonamentals del procés de dades en NGS de forma general i adaptat per a la seua comprensió per una àmplia audiència, a través d'una exhaustiva revisió de l'estat de l'art en la correcció de dades de NGS. Esta revisió introduïx gradualment al lector en les tècniques de seqüenciació massiva, presentant problemes i aplicacions reals de les tècniques de NGS, destacant l'impacte d'esta tecnologia en ciència. D'este estudi es conclouen dos idees principals: La necessitat d'analitzar de forma adequada les característiques de les dades de NGS, atenent a l'enorme varietat intrínseca que tenen les diferents tècniques de NGS; i la necessitat de disposar d'una ferramenta versàtil, eficient i precisa per a la correcció d'errors. En el context de l'anàlisi de dades, la tesi presenta MuffinInfo. La ferramenta MuffinInfo és una aplicació programari implementada per mitjà de HTML5. MuffinInfo obté informació rellevant de dades crues de NGS per a afavorir l'enteniment de les seues característiques i l'aplicació de tècniques de correcció d'errors, suportant a més l'extensió per mitjà de funcions que implementen estadístics definits per l'usuari. MuffinInfo emmagatzema els resultats del procés en fitxers JSON. A l'usar HTML5, MuffinInfo pot funcionar en gairebé qualsevol entorn maquinari i programari. La ferramenta està implementada aprofitant múltiples fils d'execució per la gestió de l'interfície. La segona conclusió de l'anàlisi de l'estat de l'art ens porta a l'oportunitat d'aplicar de forma extensiva tècniques de computació d'altes prestacions en la correcció d'errors per a desenrotllar una ferramenta que suport múltiples tecnologies (Illumina, Roche 454, Ió Torrent i experimentalment PacBio). La ferramenta proposada (MuffinEC), suporta diferents tipus d'errors (substitucions, indels i valors desconeguts). MuffinEC supera els resultats obtinguts per les ferramentes existents en este àmbit. Oferix una millor taxa de correcció, en un temps molt inferior i utilitzant menys recursos, la qual cosa facilita a més la seua aplicació en mostres més gran en computadors convencionals. MuffinEC utilitza una aproximació basada en etapes multiples. Primer agrupa totes les seqüències utilitzant la mètrica dels k-mers. En segon lloc realitza un refinament dels grups per mitjà de l'alineament amb Smith-Waterman, generant contigs. Estos contigs resulten de la correcció per columnes d'atenent a la freqüència individual de cada base. La tesi s'estructura per capítols la base de la qual ha sigut prèviament publicada en revistes indexades en posicions destacades de l'índex del Journal of Citation Repor
Alic, AS. (2016). Improved Error Correction of NGS Data [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67630
TESIS
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Spáčil, Michael. "Zálohování dat a datová úložiště." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2021. http://www.nusl.cz/ntk/nusl-444686.

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The diploma thesis is focused on the design of a backup system to increase the efficiency of working with stored data and increase the security of stored data. The analysis of the current state describes the company itself and also the backup system using the audit portal Zefis.cz. The following part describes the design of a new backup system that focuses on complexity using the cloud, magnetic tapes, and high server availability.
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Hriadeľ, Ondřej. "Návrh a implementace plánu zálohování dat společnosti." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2019. http://www.nusl.cz/ntk/nusl-399540.

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This diploma thesis is focused on the development of a new backup plan and its implementation. In introductory part of the thesis I explore the theorethical backround of data backup and data management. Next part is dedicated to analysis of current state and investor requierements. Last part is aimed to implementation of new backup plan with focusing on economic and quality point of view. Besides concept and realization of backup plan the concept of the backup directive is created .
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Janíček, Libor. "Zálohování dat a datová úložiště." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2020. http://www.nusl.cz/ntk/nusl-417707.

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The master´s thesis focuses on problematics associated with data backup and storages. It deals with the realistic data backup issue at a real municipal office. Part of the work is a thorough analysis of the current state and suggestions for improvement.
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Chen, Dao-Peng. "Statistical power for RNA-seq data to detect two epigenetic phenomena." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357248975.

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SAGGESE, IGOR. "NGS data analysis approaches for clinical applications." Doctoral thesis, Università del Piemonte Orientale, 2017. http://hdl.handle.net/11579/86924.

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Books on the topic "Dati NGS"

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Sentā, Kokusai Kyōryoku NGO. NGO dēta bukku, 2011: Sūji de miru Nihon no NGO = Data book on Japanese NGOs, 2011. [Tokyo]: Gaimushō Kokusai Kyōryokukyoku Minkan Enjo Renkeishitsu Gaimushō Shusai Heisei 22-nendo NGO ni Yoru Tēma Betsu Nōryoku Kōjō Puroguramu "NGO no Soshiki, Katsudō ni Kakawaru Dēta Bukku Sakusei", 2011.

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Sentā, Kokusai Kyōryoku NGO. NGO dēta bukku, 2011: Sūji de miru Nihon no NGO = Data book on Japanese NGOs, 2011. [Tokyo]: Gaimushō Kokusai Kyōryokukyoku Minkan Enjo Renkeishitsu Gaimushō Shusai Heisei 22-nendo NGO ni Yoru Tēma Betsu Nōryoku Kōjō Puroguramu "NGO no Soshiki, Katsudō ni Kakawaru Dēta Bukku Sakusei", 2011.

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Ahmad, Rofiq. Perkebunan dari NES ke PIR. Jakarta: Puspa Swara, 1998.

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McLafferty, F. W. TheW iley/NBS registry of mass spectral data. New York: Wiley, 1989.

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McLafferty, Fred W. The Wiley/NBS registry of mass spectral data. New York: Wiley, 1989.

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K, Eaton C., Young Bruce, and Research Institute for Advanced Computer Science (U.S.), eds. Data communication requirements for the advanced NAS network. Moffett Field, Calif: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1987.

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R. O. van Everdingen Research Specialties Limited. Halifax International Airport, N.S.: Hydrological and geochemical data. Calgary, Alta: R.O. van Everdingen Research Specialities, 1988.

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United States. National Aeronautics and Space Administration., ed. Materials engineering data base: [final report], NAS 8-37780. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Rockett, John A. The NBS/Harvard Mark VI multi-room fire simulation. Gaithersburg, MD: U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Buckey, Jay C. "Life sciences data archive scientific development": Contract NAS 9-19190 : final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Book chapters on the topic "Dati NGS"

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Kappelmann-Fenzl, Melanie. "NGS Data." In Next Generation Sequencing and Data Analysis, 79–104. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62490-3_7.

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Eisele, Marius, and Melanie Kappelmann-Fenzl. "NGS Technologies." In Next Generation Sequencing and Data Analysis, 47–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62490-3_4.

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Kappelmann-Fenzl, Melanie. "Library Construction for NGS." In Next Generation Sequencing and Data Analysis, 39–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62490-3_3.

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Benoit, Gaetan, Claire Lemaitre, Guillaume Rizk, Erwan Drezen, and Dominique Lavenier. "De Novo NGS Data Compression." In Algorithms for Next-Generation Sequencing Data, 91–115. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59826-0_4.

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Comin, Matteo, and Michele Schimd. "Assembly-Free Techniques for NGS Data." In Algorithms for Next-Generation Sequencing Data, 327–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59826-0_14.

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Theodoridis, Evangelos. "Cloud Storage-Management Techniques for NGS Data." In Algorithms for Next-Generation Sequencing Data, 117–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59826-0_5.

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Prieto, T., J. M. Alves, and D. Posada. "NGS Analysis of Somatic Mutations in Cancer Genomes." In Big Data Analytics in Genomics, 357–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41279-5_11.

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Bosserhoff, Anja, and Melanie Kappelmann-Fenzl. "Next Generation Sequencing (NGS): What Can Be Sequenced?" In Next Generation Sequencing and Data Analysis, 1–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62490-3_1.

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Peace, R. J., and James R. Green. "Computational Sequence- and NGS-Based MicroRNA Prediction." In Signal Processing and Machine Learning for Biomedical Big Data, 381–410. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351061223-19.

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Garg, Vanika, and Rajeev K. Varshney. "Analysis of Small RNA Sequencing Data in Plants." In Plant Bioinformatics, 497–509. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_26.

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AbstractOver the past decades, next-generation sequencing (NGS) has been employed extensively for investigating the regulatory mechanisms of small RNAs. Several bioinformatics tools are available for aiding biologists to extract meaningful information from enormous amounts of data generated by NGS platforms. This chapter describes a detailed methodology for analyzing small RNA sequencing data using different open source tools. We elaborate on various steps involved in analysis, from processing the raw sequencing reads to identifying miRNAs, their targets, and differential expression studies.
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Conference papers on the topic "Dati NGS"

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Kchouk, Mehdi, Jean-Francois Gibrat, and Mourad Elloumi. "An Error Correction Algorithm for NGS Data." In 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2017. http://dx.doi.org/10.1109/dexa.2017.33.

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Biji C.L., Achuthsankar S. Nair, Arun P.R, and Jojo George. "NGS read data compression using parallel computing algorithm." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359890.

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Колмыков, С. К., И. С. Евшин, Ф. А. Колпаков, and М. А. Куляшов. "ANALYSIS OF NGS DATA ON THE TRANSCRIPTIONAL REGULATION." In XVII Российская конференция “Распределенные информационно-вычислительные ресурсы: Цифровые двойники и большие данные”. Crossref, 2019. http://dx.doi.org/10.25743/ict.2019.80.67.016.

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База данных GTRD (http://gtrd.biouml.org) содержит более 30000 единообразно обработанных NGS экспериментов по регуляции транскрипции (СhIP-seq, ChIP-exo, DNaseseq, ATAC-seq, MNase-seq и FAIRE-seq). Для обработки этих типов данных были разработаны сценарии для системы управления распределенными вычислениями eGrid и платформы BioUML. The GTRD database (http://gtrd.biouml.org) contains over 30,000 uniformly processed NGS experiments on transcriptional regulation (ChIP-seq, ChIP-exo, DNase-seq, ATAC-seq, MNase-seq and FAIRE-seq). To process these types of data, pipelines have been developed for the eGrid distributed computing management system and the BioUML platform.
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Braga, D., D. Calvanese, A. Campi, S. Ceri, F. Daniel, D. Martinenghi, P. Merialdo, and R. Torlone. "NGS: a framework for multi-domain query answering." In 2008 IEEE 24th International Conference on Data Engineeing workshop (ICDE Workshop 2008). IEEE, 2008. http://dx.doi.org/10.1109/icdew.2008.4498328.

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Pan, Ren-Hao, Lin-Yu Tseng, I.-En Liao, Chien-Lung Chan, K. Robert Lai, and Kai-Biao Lin. "Design of an NGS MicroRNA predictor using multilayer hierarchical MapReduce framework." In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2015. http://dx.doi.org/10.1109/dsaa.2015.7344862.

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Saha, Subrata, and Sanguthevar Rajasekaran. "Efficient algorithms for error correction and compression of NGS data." In 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2014. http://dx.doi.org/10.1109/iccabs.2014.6863941.

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Lawrence, Aamna, Rahul Shukla, Utkarsh Raj, and Pritish Kumar Varadwaj. "Estimating percentage epigenetic modifications in human genome using NGS data." In 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552141.

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SENGUPTA, SUBHAJIT, JIN WANG, JUHEE LEE, PETER MÜLLER, KAMALAKAR GULUKOTA, ARUNAVA BANERJEE, and YUAN JI. "BAYCLONE: BAYESIAN NONPARAMETRIC INFERENCE OF TUMOR SUBCLONES USING NGS DATA." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814644730_0044.

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Collet, C., T. Coupaye, L. Fayolle, and C. Roncancio. "NAGS prototype-version 2.2." In Proceedings 13th International Conference on Data Engineering. IEEE, 1997. http://dx.doi.org/10.1109/icde.1997.582035.

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Xu, Jin, Xu Tan, Renqian Luo, Kaitao Song, Jian Li, Tao Qin, and Tie-Yan Liu. "NAS-BERT." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467262.

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Reports on the topic "Dati NGS"

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Chamberlain, C. A., and K. Lochhead. Data modeling as applied to surveying and mapping data. Natural Resources Canada/CMSS/Information Management, 1988. http://dx.doi.org/10.4095/331263.

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The Geodetic Survey Division of the Canada Centre for Surveying is replacing the National Geodetic Data Base (NGDB) with the National Geodetic Information System (NGIS). For the NGIS to be successful, it was recognized that a sound, well engineered data mode was essential. The methodology chosen to design the data mode! was Nijssen's Information Analysis Methodology (NIAM), a binary modeling technique that is supported by a Computer Aided Software Engineering (CASE) tool, PC-IAST. An NGIS prototype has also been developed using Digital Equipment of Canada's Relational Database (Rdb) management system and COGNOS Corporations POWERHOUSE 4th generation language. This paper addresses the need for, and the advantages of using a strong engineering approach to data modeling and describes the use of the NIAM methodology in NGIS development. The paper identifies the relationship between the data mode!, data structures, the design and development of a database and the use of automated tools for systems development. In conclusion, critical success factors for the continuation of the N.G.I.S. developments are identified and the benefits that will accrue are enumerated.
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Friske, P. W. B., A. G. Pronk, M. W. McCurdy, S. J. A. Day, R J McNeil, S. Allard, and R. Boldon. National Geochemical Reconnaissance (NGR): Regional stream sediment and water geochemical data, northeastern New Brunswick (NTS 21O/9). Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/215455.

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Friske, P. W. B., W. Jackaman, R. E. W. Lett, and M. W. McCurdy. National Geochemical Reconnaissance (NGR): Regional stream sediment and water geochemical data, northwestern British Columbia (NTS 104A and 104H). Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/220856.

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Talpey, T., and B. Callaghan. Network File System (NFS) Direct Data Placement. RFC Editor, January 2010. http://dx.doi.org/10.17487/rfc5667.

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Lyles, B., and T. Mihevc. NTS groundwater recharge study, FY 1992. Data report. Office of Scientific and Technical Information (OSTI), October 1992. http://dx.doi.org/10.2172/10123053.

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Miles, W. F., M. Pilkington, and M. D. Thomas. Aeromagnetic data interpretation, NTS 56P, Committee Bay, Nunavut, Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213861.

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Read, P. B., J. F. Psutka, and J. Fillipone. Abbreviated metamorphic data for the Canadian Cordillera: arranged by NTS sheet. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/131848.

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Friske, P. W. B., E. H. W. Hornbrook, J. J. Lynch, M. W. McCurdy, H. Gross, A. C. Galletta, and C C Durham. Regional Lake Sediment and Water Geochemical Data, northwestern Ontario [NTS 52C]. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/131990.

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McCurdy, M. W., R. J. McNeil, J. A. Percival, and R. G. Garrett. Regional lake sediment geochemical data, Nonacho Basin - East Arm of Great Slave Lake region, Northwest Territories (NTS 75-C, NTS 75-F and NTS 75-K). Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297797.

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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner, and Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, February 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Abstract:
Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.
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