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Статті в журналах з теми "Very high throughput sequencing":
Bray, Paul F., Paolo M. Fortina, Srikanth Nagalla, Kathleen Delgrosso, Adam Ertel, Isidore Rigoutsos, and Steven E. McKenzie. "High-Throughput Sequencing of the Human Platelet Transcriptome." Blood 116, no. 21 (November 19, 2010): 481. http://dx.doi.org/10.1182/blood.v116.21.481.481.
Hoffman, Joseph I., Fraser Simpson, Patrice David, Jolianne M. Rijks, Thijs Kuiken, Michael A. S. Thorne, Robert C. Lacy, and Kanchon K. Dasmahapatra. "High-throughput sequencing reveals inbreeding depression in a natural population." Proceedings of the National Academy of Sciences 111, no. 10 (February 28, 2014): 3775–80. http://dx.doi.org/10.1073/pnas.1318945111.
Chen, Yanwu, Bin Wu, Cheng Zhang, Zhiqi Fan, Ying Chen, Bingmu Xin, and Qiong Xie. "Current Progression: Application of High-Throughput Sequencing Technique in Space Microbiology." BioMed Research International 2020 (June 22, 2020): 1–13. http://dx.doi.org/10.1155/2020/4094191.
Drees, Alissa, and Markus Fischer. "High-Throughput Selection and Characterisation of Aptamers on Optical Next-Generation Sequencers." International Journal of Molecular Sciences 22, no. 17 (August 25, 2021): 9202. http://dx.doi.org/10.3390/ijms22179202.
Zheng, Huiquan, Dehuo Hu, Ruping Wei, Shu Yan, and Runhui Wang. "Chinese Fir Breeding in the High-Throughput Sequencing Era: Insights from SNPs." Forests 10, no. 8 (August 12, 2019): 681. http://dx.doi.org/10.3390/f10080681.
Bao, Ergude, Fei Xie, Changjin Song, and Dandan Song. "FLAS: fast and high-throughput algorithm for PacBio long-read self-correction." Bioinformatics 35, no. 20 (March 21, 2019): 3953–60. http://dx.doi.org/10.1093/bioinformatics/btz206.
Bokulich, Nicholas A., and David A. Mills. "Improved Selection of Internal Transcribed Spacer-Specific Primers Enables Quantitative, Ultra-High-Throughput Profiling of Fungal Communities." Applied and Environmental Microbiology 79, no. 8 (February 1, 2013): 2519–26. http://dx.doi.org/10.1128/aem.03870-12.
Christoff, Ana P., Aline FR Sereia, Camila Hernandes, and Luiz FV de Oliveira. "Uncovering the hidden microbiota in hospital and built environments: New approaches and solutions." Experimental Biology and Medicine 244, no. 6 (January 7, 2019): 534–42. http://dx.doi.org/10.1177/1535370218821857.
Schorderet, Daniel F., Alexandra Iouranova, Tatiana Favez, Leila Tiab, and Pascal Escher. "IROme, a New High-Throughput Molecular Tool for the Diagnosis of Inherited Retinal Dystrophies." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/198089.
Zhao, Lili, Hongbo Li, Zhenbin Liu, Liangbin Hu, Dan Xu, Xiaolin Zhu, and Haizhen Mo. "Quality Changes and Fungal Microbiota Dynamics in Stored Jujube Fruits: Insights from High-Throughput Sequencing for Food Preservation." Foods 13, no. 10 (May 10, 2024): 1473. http://dx.doi.org/10.3390/foods13101473.
Дисертації з теми "Very high throughput sequencing":
Koreki, Axelle. "Recherche de déterminants génétiques de la résistance aux herbicides auxiniques chez le Coquelicot (Papaver rhoeas L.) dans un but de diagnostic." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCK005.
Corn poppy (Papaver rhoeas) is a very widespread cosmopolitan weed in winter crops cereal in Europe which has a high potential for invasion and spread in crops. It is mainly controlled by ALS inhibitor herbicides and auxin herbicides. The intensive use of these two modes of action has led to the evolution of resistance in many poppy populations across Europe. Herbicide resistance involves two categories of mechanisms: target-site-based resistance (TSR) and non-target-site-based resistance (NTSR). In poppy, only NTSR mechanisms have been identified, but the specific genes remain unknown. This work therefore has several goals: (i) identify and potentially validate the genetic determinants of resistance to auxin herbicides in corn poppy and (ii) evaluate resistance status to auxin herbicides in French poppy populations.In a first part, we phenotypically characterized the plant material available using herbicides sensitivity bioassays (Chapter 1) to assess the resistance status of poppies to auxin herbicides in France. We have shown that resistance to 2,4-D in France was widespread, even very well established in certain areas. We also identified two areas in Italy and Greece where resistant plants to halauxifen-methyl were detected, suggesting the beginning of the evolution of resistance to this new synthetic herbicide. Populations with a balanced ratio of resistant and sensitive individuals were used for plant material production for the molecular biology approaches of the second part.In a second part, we studied constitutive resistance to 2,4-D and halauxifen-methyl among 14 populations via RNA sequencing (RNAseq) (Chapter 2). We showed that the expression profiles of sensitive and resistant plants were specific to each population. Among the genes differentially expressed in resistant plants, some gene families potentially involved in the metabolism of herbicides (CYP450, GST, ABC transporters, etc.) or regulatory cascades (transcription factors, protein kinases) have been identified. Based on these results, the expression level of these genes was validated via an RT-qPCR approach using a larger sample of plants. All the results indicate that there is potentially a wide variety of inter- and intra-population resistance mechanisms.The second RNAseq (Chapter 3) aimed to study the transcriptomic response of resistant and sensitive plants between 4h and 48h after the application of 2,4-D in two populations. We identified a large diversity of genes and gene families specifically induced in resistant plants from both populations, but their role in resistance could not be verified. As in constitutive resistance, these can potentially be detoxification enzymes, transporters, or even potential auxin target genes or genes associated with the general stress response. In addition, 2,4-D induces a rapid response which is detectable within 4 hours following treatment regardless of the phenotype and population.Finally, the comparison of constitutively differentially expressed genes between the two RNAseq approaches demonstrates that the absence of common genes is potentially due to a high diversity of intra- and -inter population resistance mechanisms, or to the fact that the mechanisms that contribute the most to resistance are due to structural mutations
Roguski, Łukasz 1987. "High-throughput sequencing data compression." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/565775.
Gràcies als avenços en el camp de les tecnologies de seqüenciació, en els darrers anys la recerca biomèdica ha viscut una revolució, que ha tingut com un dels resultats l'explosió del volum de dades genòmiques generades arreu del món. La mida típica de les dades de seqüenciació generades en experiments d'escala mitjana acostuma a situar-se en un rang entre deu i cent gigabytes, que s'emmagatzemen en diversos arxius en diferents formats produïts en cada experiment. Els formats estàndards actuals de facto de representació de dades genòmiques són en format textual. Per raons pràctiques, les dades necessiten ser emmagatzemades en format comprimit. En la majoria dels casos, aquests mètodes de compressió es basen en compressors de text de caràcter general, com ara gzip. Amb tot, no permeten explotar els models d'informació especifícs de dades de seqüenciació. És per això que proporcionen funcionalitats limitades i estalvi insuficient d'espai d'emmagatzematge. Això explica per què operacions relativament bàsiques, com ara el processament, l'emmagatzematge i la transferència de dades genòmiques, s'han convertit en un dels principals obstacles de processos actuals d'anàlisi. Per tot això, aquesta tesi se centra en mètodes d'emmagatzematge i compressió eficients de dades generades en experiments de sequenciació. En primer lloc, proposem un compressor innovador d'arxius FASTQ de propòsit general. A diferència de gzip, aquest compressor permet reduir de manera significativa la mida de l'arxiu resultant del procés de compressió. A més a més, aquesta eina permet processar les dades a una velocitat alta. A continuació, presentem mètodes de compressió que fan ús de l'alta redundància de seqüències present en les dades de seqüenciació. Aquests mètodes obtenen la millor ratio de compressió d'entre els compressors FASTQ del marc teòric actual, sense fer ús de cap referència externa. També mostrem aproximacions de compressió amb pèrdua per emmagatzemar dades de seqüenciació auxiliars, que permeten reduir encara més la mida de les dades. En últim lloc, aportem un sistema flexible de compressió i un format de dades. Aquest sistema fa possible generar de manera semi-automàtica solucions de compressió que no estan lligades a cap mena de format específic d'arxius de dades genòmiques. Per tal de facilitar la gestió complexa de dades, diversos conjunts de dades amb formats heterogenis poden ser emmagatzemats en contenidors configurables amb l'opció de dur a terme consultes personalitzades sobre les dades emmagatzemades. A més a més, exposem que les solucions simples basades en el nostre sistema poden obtenir resultats comparables als compressors de format específic de l'estat de l'art. En resum, les solucions desenvolupades i descrites en aquesta tesi poden ser incorporades amb facilitat en processos d'anàlisi de dades genòmiques. Si prenem aquestes solucions conjuntament, aporten una base sòlida per al desenvolupament d'aproximacions completes encaminades a l'emmagatzematge i gestió eficient de dades genòmiques.
Mozere, M. "High-throughput sequencing analysis pipeline." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1528797/.
Durif, Ghislain. "Multivariate analysis of high-throughput sequencing data." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1334/document.
The statistical analysis of Next-Generation Sequencing data raises many computational challenges regarding modeling and inference, especially because of the high dimensionality of genomic data. The research work in this manuscript concerns hybrid dimension reduction methods that rely on both compression (representation of the data into a lower dimensional space) and variable selection. Developments are made concerning: the sparse Partial Least Squares (PLS) regression framework for supervised classification, and the sparse matrix factorization framework for unsupervised exploration. In both situations, our main purpose will be to focus on the reconstruction and visualization of the data. First, we will present a new sparse PLS approach, based on an adaptive sparsity-inducing penalty, that is suitable for logistic regression to predict the label of a discrete outcome. For instance, such a method will be used for prediction (fate of patients or specific type of unidentified single cells) based on gene expression profiles. The main issue in such framework is to account for the response to discard irrelevant variables. We will highlight the direct link between the derivation of the algorithms and the reliability of the results. Then, motivated by questions regarding single-cell data analysis, we propose a flexible model-based approach for the factorization of count matrices, that accounts for over-dispersion as well as zero-inflation (both characteristic of single-cell data), for which we derive an estimation procedure based on variational inference. In this scheme, we consider probabilistic variable selection based on a spike-and-slab model suitable for count data. The interest of our procedure for data reconstruction, visualization and clustering will be illustrated by simulation experiments and by preliminary results on single-cell data analysis. All proposed methods were implemented into two R-packages "plsgenomics" and "CMF" based on high performance computing
Langenberger, David. "High-throughput sequencing and small non-coding RNAs." Doctoral thesis, Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-112876.
Zhang, Xuekui. "Mixture models for analysing high throughput sequencing data." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/35982.
Roberts, Adam. "Ambiguous fragment assignment for high-throughput sequencing experiments." Thesis, University of California, Berkeley, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3616509.
As the cost of short-read, high-throughput DNA sequencing continues to fall rapidly, new uses for the technology have been developed aside from its original purpose in determining the genome of various species. Many of these new experiments use the sequencer as a digital counter for measuring biological activities such as gene expression (RNA-Seq) or protein binding (ChIP-Seq).
A common problem faced in the analysis of these data is that of sequenced fragments that are "ambiguous", meaning they resemble multiple loci in a reference genome or other sequence. In early analyses, such ambiguous fragments were ignored or were assigned to loci using simple heuristics. However, statistical approaches using maximum likelihood estimation have been shown to greatly improve the accuracy of downstream analyses and have become widely adopted Optimization based on the expectation-maximization (EM) algorithm are often employed by these methods to find the optimal sets of alignments, with frequent enhancements to the model. Nevertheless, these improvements increase complexity, which, along with an exponential growth in the size of sequencing datasets, has led to new computational challenges.
Herein, we present our model for ambiguous fragment assignment for RNA-Seq, which includes the most comprehensive set of parameters of any model introduced to date, as well as various methods we have explored for scaling our optimization procedure. These methods include the use of an online EM algorithm and a distributed EM solution implemented on the Spark cluster computing system. Our advances have resulted in the first efficient solution to the problem of fragment assignment in sequencing.
Furthermore, we are the first to create a fully generalized model for ambiguous fragment assignment and present details on how our method can provide solutions for additional high-throughput sequencing assays including ChIP-Seq, Allele-Specific Expression (ASE), and the detection of RNA-DNA Differences (RDDs) in RNA-Seq.
Hoffmann, Steve. "Genome Informatics for High-Throughput Sequencing Data Analysis." Doctoral thesis, Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-152643.
Diese Arbeit stellt drei verschiedene algorithmische und statistische Strategien für die Analyse von Hochdurchsatz-Sequenzierungsdaten vor. Zuerst führen wir eine auf enhanced Suffixarrays basierende heuristische Methode ein, die kurze Sequenzen mit grossen Genomen aligniert. Die Methode basiert auf der Idee einer fehlertoleranten Traversierung eines Suffixarrays für Referenzgenome in Verbindung mit dem Konzept der Matching-Statistik von Chang und einem auf Bitvektoren basierenden Alignmentalgorithmus von Myers. Die vorgestellte Methode unterstützt Paired-End und Mate-Pair Alignments, bietet Methoden zur Erkennung von Primersequenzen und zum trimmen von Poly-A-Signalen an. Auch in unabhängigen Benchmarks zeichnet sich das Verfahren durch hohe Sensitivität und Spezifität in simulierten und realen Datensätzen aus. Für eine große Anzahl von Sequenzierungsprotokollen erzielt es bessere Ergebnisse als andere bekannte Short-Read Alignmentprogramme. Zweitens stellen wir einen auf dynamischer Programmierung basierenden Algorithmus für das spliced alignment problem vor. Der Vorteil dieses Algorithmus ist seine Fähigkeit, nicht nur kollineare Spleiß- Ereignisse, d.h. Spleiß-Ereignisse auf dem gleichen genomischen Strang, sondern auch zirkuläre und andere nicht-kollineare Spleiß-Ereignisse zu identifizieren. Das Verfahren zeichnet sich durch eine hohe Genauigkeit aus: während es bei der Erkennung kollinearer Spleiß-Varianten vergleichbare Ergebnisse mit anderen Methoden erzielt, schlägt es die Wettbewerber mit Blick auf Sensitivität und Spezifität bei der Vorhersage nicht-kollinearer Spleißvarianten. Die Anwendung dieses Algorithmus führte zur Identifikation neuer Isoformen. In unserer Publikation berichten wir über eine neue Isoform des Tumorsuppressorgens p53. Da dieses Gen eines der am besten untersuchten Gene des menschlichen Genoms ist, könnte die Anwendung unseres Algorithmus helfen, eine Vielzahl weiterer Isoformen bei weniger prominenten Genen zu identifizieren. Drittens stellen wir ein datenadaptives Modell zur Identifikation von Single Nucleotide Variations (SNVs) vor. In unserer Arbeit zeigen wir, dass sich unser auf empirischen log-likelihoods basierendes Modell automatisch an die Qualität der Sequenzierungsexperimente anpasst und eine \"Entscheidung\" darüber trifft, welche potentiellen Variationen als SNVs zu klassifizieren sind. In unseren Simulationen ist diese Methode auf Augenhöhe mit aktuell eingesetzten Verfahren. Schließlich stellen wir eine Auswahl biologischer Ergebnisse vor, die mit den Besonderheiten der präsentierten Alignmentverfahren in Zusammenhang stehen
Duggett, Nicholas A. "High-throughput sequencing of the chicken gut microbiome." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/6678/.
Chiang, HyoJin Rosaria. "Examination of mammalian microRNAs by high-throughput sequencing." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65289.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Small non-coding RNAs play an important role in a wide range of cellular events. MicroRNAs (miRNAs) are an abundant class of small RNAs that post-transcriptionally repress expression of their target genes. Since miRNA targeting is based on its sequence, accurate and comprehensive annotation of miRNA genes is fundamental to understanding miRNA gene regulation. Advances in high-throughput sequencing technology have led to discoveries of novel small RNA genes and identifications of their properties. We describe a method for construction of small-RNA library for Illumina sequencing platform that improves upon previous efforts. Sequencing data from small-RNA libraries constructed using this protocol can be used to profile small RNAs from a broad range of samples. In particular, we sequenced 60 million small RNAs from mouse brain, ovary, testes, embryonic stem cells, three embryonic stages, and whole newborns. The analysis of the data provide a substantially revised list of confidently identified murine miRNAs, thereby providing a more accurate picture of the general features of mammalian miRNAs and their abundance in the genome. In addition, our results revealed new aspects of miRNA biogenesis and modification, including tissue-specific strand preferences, sequential Dicer cleavage of a metazoan pre-miRNA, cases of consequential 5' heterogeneity, newly identified instances of miRNA editing, and widespread pre-miRNA uridylation reminiscent of Lin28-like miRNA regulation.
by HyoJin Rosaria Chiang.
Ph.D.
Книги з теми "Very high throughput sequencing":
Kwon, Young Min, and Steven C. Ricke, eds. High-Throughput Next Generation Sequencing. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-089-8.
Rodríguez-Ezpeleta, Naiara, Michael Hackenberg, and Ana M. Aransay, eds. Bioinformatics for High Throughput Sequencing. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-0782-9.
Rodríguez-Ezpeleta, Naiara, Michael Hackenberg, and Ana M. Aransay. Bioinformatics for high throughput sequencing. New York, NY: Springer, 2012.
Ricke, Steven C., and Young Min Kwon. High-throughput next generation sequencing: Methods and applications. New York: Springer, 2011.
Aransay, Ana M., and José Luis Lavín Trueba, eds. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31350-4.
Mäkinen, Veli. Genome-scale algorithm design: Biological sequence analysis in the era of high-throughput sequencing. Cambridge, United Kingdom: University Printing House, 2015.
Cunha, Monica V., and João Inácio. Veterinary infection biology: Molecular diagnostics and high-throughput strategies. New York: Humana Press, 2015.
Rodríguez-Ezpeleta, Naiara, Ana M. Aransay, and Michael Hackenberg. Bioinformatics for High Throughput Sequencing. Springer, 2014.
Rodríguez-Ezpeleta, Naiara, Ana M. Aransay, and Michael Hackenberg. Bioinformatics for High Throughput Sequencing. Springer, 2011.
Lee, Eric, and T. W. Tan. Beginners Guide to Bioinformatics for High Throughput Sequencing. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/10720.
Частини книг з теми "Very high throughput sequencing":
Chen, Yuan-Jyue, and Georg Seelig. "Scaling Up DNA Computing with Array-Based Synthesis and High-Throughput Sequencing." In Natural Computing Series, 281–93. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9891-1_16.
Deng, Xiangyu, Lee S. Katz, Patricia I. Fields, and Wei Zhang. "High-Throughput Sequencing." In DNA Methods in Food Safety, 65–83. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118278666.ch4.
Woods, Douglas W., Matthew R. Capriotti, Madison Pilato, Carolyn A. Doyle, Christopher J. McDougle, Beth Springate, Deborah Fein, et al. "High-Throughput Sequencing." In Encyclopedia of Autism Spectrum Disorders, 1508. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_100671.
Elumalai, Elakkiya, and Krishna Kant Gupta. "High-Throughput Sequencing Technologies." In Bioinformatics in Rice Research, 283–304. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3993-7_13.
Goya, Rodrigo, Irmtraud M. Meyer, and Marco A. Marra. "Applications of High-Throughput Sequencing." In Bioinformatics for High Throughput Sequencing, 27–53. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0782-9_3.
Myllykangas, Samuel, Jason Buenrostro, and Hanlee P. Ji. "Overview of Sequencing Technology Platforms." In Bioinformatics for High Throughput Sequencing, 11–25. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0782-9_2.
Kaya, Kamer, Ayat Hatem, Hatice Gülçin Özer, Kun Huang, and Ümit V. Çatalyürek. "High-Performance Computing In High-Throughput Sequencing." In Biological Knowledge Discovery Handbook, 981–1002. Hoboken, New Jersey: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch43.
Rodríguez-Ezpeleta, Naiara, and Ana M. Aransay. "Introduction." In Bioinformatics for High Throughput Sequencing, 1–9. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0782-9_1.
Young, Matthew D., Davis J. McCarthy, Matthew J. Wakefield, Gordon K. Smyth, Alicia Oshlack, and Mark D. Robinson. "Differential Expression for RNA Sequencing (RNA-Seq) Data: Mapping, Summarization, Statistical Analysis, and Experimental Design." In Bioinformatics for High Throughput Sequencing, 169–90. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0782-9_10.
Hackenberg, Michael. "MicroRNA Expression Profiling and Discovery." In Bioinformatics for High Throughput Sequencing, 191–208. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0782-9_11.
Тези доповідей конференцій з теми "Very high throughput sequencing":
Taher, Ahmed, Ben Jones, Peter Peumans, and Liesbet Lagae. "A Simplified Model for Species Transport in Very Large Scale Microfluidic Networks." In ASME 2018 16th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icnmm2018-7663.
Chaabane, Mohamed, Eric C. Rouchka, and Juw Won Park. "Circular RNA Detection from High-throughput Sequencing." In RACS '17: International Conference on Research in Adaptive and Convergent Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3129676.3129734.
Mangul, Serghei, and Alex Zelikovsky. "Poster: Haplotype discovery from high-throughput sequencing data." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729908.
Holt, James, Shunping Huang, Leonard McMillan, and Wei Wang. "Read Annotation Pipeline for High-Throughput Sequencing Data." In BCB'13: ACM-BCB2013. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2506583.2506645.
Kuroshu, Reginaldo M. "Non-overlapping clone pooling for high-throughput sequencing." In the ACM Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2382936.2382947.
Chanyshev, M. D., N. V. Vlasenko, I. A. Kotov, K. F. Khafizov, and V. G. Akimkin. "HIGH THROUGHPUT DNA SEQUENCING OF HEPATITIS B VIRUS." In X Международная конференция молодых ученых: биоинформатиков, биотехнологов, биофизиков, вирусологов и молекулярных биологов — 2023. Novosibirsk State University, 2023. http://dx.doi.org/10.25205/978-5-4437-1526-1-266.
White, Brian S., Abdullah Ozer, John T. Lis, and David Shalloway. "Abstract LB-97: Optimizing SELEX with high-throughput sequencing." 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-lb-97.
Hoobin, Christopher, Trey Kind, Christina Boucher, and Simon J. Puglisi. "Fast and efficient compression of high-throughput sequencing reads." In BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808719.2808753.
Rakova, Irina, Maria Kapetanaki, Kusum Pandit, Lara Chensny, Kevin Gibson, Elodie Ghedin, and Naftali Kaminski. "High-Throughput Sequencing Of MicroRNA In Idiopathic Pulmonary Fibrosis." In American Thoracic Society 2011 International Conference, May 13-18, 2011 • Denver Colorado. American Thoracic Society, 2011. http://dx.doi.org/10.1164/ajrccm-conference.2011.183.1_meetingabstracts.a5538.
Tsui, Stephen Kwok-Wing. "High-throughput DNA sequencing and bioinformatics: Bottlenecks and opportunities." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255117.
Звіти організацій з теми "Very high throughput sequencing":
Lu, X. An integrated multiple capillary array electrophoresis system for high-throughput DNA sequencing. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/348901.
Cooney, Kathleen A. High Throughput Sequencing of Germline and Tumor from Men With Early-Onset Metastatic Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada611828.
Cooney, Kathleen A. High-Throughput Sequencing of Germline and Tumor From Men with Early-Onset Metastatic Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2015. http://dx.doi.org/10.21236/ada624260.
Dhere, N. G. High Throughput, Low Toxic Processing of Very Thin, High Efficiency CIGSS Solar Cells: Final Report, December 2008. Office of Scientific and Technical Information (OSTI), April 2009. http://dx.doi.org/10.2172/951811.
Novikova, Irina, James Evans, Lye Meng Markillie, and Hugh Mitchell. Validation and functional characterization of transcription factors in wheat using cell-free protein expression and high-throughput sequencing technologies. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1976176.
Zhang, Yonghua. High Throughput Sample Preparation and Analysis for DNA Sequencing, PCR and Combinatorial Screening of Catalysis Based on Capillary Array Technique. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/804158.
Xue, Gang. High-Throughput Analysis With 96-Capillary Array Electrophoresis and Integrated Sample Preparation for DNA Sequencing Based on Laser Induced Fluorescence Detection. Office of Scientific and Technical Information (OSTI), January 2001. http://dx.doi.org/10.2172/803101.
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