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Journal articles on the topic 'Dati NGS'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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1

Tatebale, Rivaldo, Orbanus Naharia, and Helen J. Lawalata. "Isolation and Identification of Lactic Acid Bacteria from Red Dragon Fruit (Hylocereus polyrhicus) as Exopolysaccharide Producers." Indonesian Biodiversity Journal 5, no. 1 (2024): 8–19. https://doi.org/10.53682/ibj.v5i1.7730.

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Cherry tomatoes are a type of Red Dragon Fruit that has various benefits, including lowering cholesterol levels, preventing colon cancer, and strengthening the working power of muscles. Isolation of LAB isolated from Dragon Fruit as a production material for EPS. This study aims to isolate and identify LAB as a producer of EPS from red dragon fruit (Hylocereus polyrhiruz), which can produce exopolysaccharides. This research uses a descriptive research method. Data from experimental research in the laboratory obtained 10 LAB isolates, namely isolates NG1, NG2, NG3, NG4, NG5, NG6, NG7, NG8, NG9

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2

Rabbone, I. "Medicina di precisione: il diabete monogenico." Journal of AMD 26, no. 2 (2023): 128. http://dx.doi.org/10.36171/jamd23.26.2.9.

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Il diabete monogenico è una forma di diabete su base genetica, determinata da mutazioni a carico di un singolo gene e/o locus cromosomico con conseguente deficit del numero o della funzione della beta cellula pancreatica. Nonostante gli innumerevoli passi avanti compiuti in ambito diagnostico-terapeutico, risulta talvolta ancora difficile giungere ad una diagnosi precoce di diabete monogenico. Per tale ragione è stata sviluppata l’idea di una medicina di precisione nel diabete, definendola come un approccio utile ad ottimizzare diagnosi, prevenzione, trattamento, prognosi e monitoraggio della

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3

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

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4

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

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5

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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6

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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7

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

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8

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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9

An, Omer, Kar-Tong Tan, Ying Li, et al. "CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing." International Journal of Molecular Sciences 21, no. 11 (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 shar

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10

Brookman-Amissah, Nicola. "Generating Robust NGS Data for Personalized Medicine." Clinical OMICs 2, no. 1 (2015): 24–26. http://dx.doi.org/10.1089/clinomi.02.01.09.

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11

Kallio, Aleksi, Taavi Hupponen, Massimiliano Gentile, et al. "Biologist-friendly analysis software for NGS data." EMBnet.journal 19, A (2013): 53. http://dx.doi.org/10.14806/ej.19.a.623.

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12

Liu, Yao-Yuan, Kevin Cheng, Rebecca Just, Sana Enke, and Jo-Anne Bright. "Sequencing-induced artefacts in NGS STR data." Forensic Science International: Genetics 72 (September 2024): 103086. http://dx.doi.org/10.1016/j.fsigen.2024.103086.

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13

Nakazato, Takeru. "A Challenge to Integrate Bioinformatics and Biodiversity Informatics Data as Museomics." Biodiversity Information Science and Standards 2 (May 22, 2018): e26102. https://doi.org/10.3897/biss.2.26102.

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Museum-preserved samples are attracting attention as a rich resource for DNA studies. Museomics aims to link DNA sequence data back to the museum collection. Molecular biologists are interested in morphological information including body size, pattern, and colors, and sequence data have also become essential for biodiversity research as evidence for species identification and phylogenetic analysis. For more than 30 years, molecular data, such as DNA and protein sequences, have been captured by the DNA Data Bank of Japan (DDBJ), the European Bioinformatics Institute (EBI, UK), and the National

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14

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

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15

Thangam, Manonanthini, and Ramesh Kumar Gopal. "CRCDA—Comprehensive resources for cancer NGS data analysis." Database 2015 (2015): bav092. http://dx.doi.org/10.1093/database/bav092.

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16

Picard, Franck, and Guy Perrière. "Bioinformatics developments for NGS data analysis at PRABI." EMBnet.journal 17, B (2012): 12. http://dx.doi.org/10.14806/ej.17.b.264.

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17

Vassilev, Dimitar, Milko Krachunov, Ivan Popov, et al. "Algorithm for error detection in metagonomics NGS data." EMBnet.journal 17, B (2012): 28. http://dx.doi.org/10.14806/ej.17.b.277.

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18

Vieira, Filipe G., Anders Albrechtsen, and Rasmus Nielsen. "Estimating IBD tracts from low coverage NGS data." Bioinformatics 32, no. 14 (2016): 2096–102. http://dx.doi.org/10.1093/bioinformatics/btw212.

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19

Brouwer, R. W. W., M. C. G. N. van den Hout, F. G. Grosveld, and W. F. J. van IJcken. "NARWHAL, a primary analysis pipeline for NGS data." Bioinformatics 28, no. 2 (2011): 284–85. http://dx.doi.org/10.1093/bioinformatics/btr613.

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20

D’Agaro, Edo. "NGS genome annotation profiling using data analysis workflows." Journal of Biotechnology 256 (August 2017): S11. http://dx.doi.org/10.1016/j.jbiotec.2017.06.039.

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21

Ivanov, Maxim, Valentina Yakushina, and Mikhail Fedyanin. "Alternative allele frequency recallibration after NGS data analysis." Journal of Clinical Oncology 41, no. 16_suppl (2023): 3077. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.3077.

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3077 Background: With the spread of NGS in routine clinical practice data analysis methods actively evolve to increase and refine information yield. Not much attention is given to variant allele fraction (VAF) estimation though it impacts clinical insignificance of subclonal somatic variants and identification of variant origin. Methods: Real-world sequencing data of 2379 samples (including 781 tumor-only and 1598 blood-only sequencing datasets) obtained via amplicon-based sequencing was used for the retrospective analysis of variant allele frequency estimation of identified genetic variants e

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22

Droege, Gabriele, Jonas Zimmermann, Tim Fulcher, der Linde Sietse Van, and Walter Berendsohn. "Environmental samples, eDNA and HTS libraries – data standard proposals from the Global Genome Biodiversity Network (GGBN)." Biodiversity Information Science and Standards 1 (August 21, 2017): e20483. https://doi.org/10.3897/tdwgproceedings.1.20483.

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The GGBN Data Portal (http://www.ggbn.org, Droege et al. 2014) has established standardised data flows for genomic DNA samples, including voucher specimens, tissue samples, DNA samples as well as resulting sequences and publications. Dealing with different types of DNA (aDNA, gDNA, eDNA) is essential and closely related to user-friendly search and display functionalities. GGBN aims both at preserving voucher specimens of all kinds of DNA as well as making these important data accessible on the Internet. In addition to genomic DNA, the development and use of high-throughput-/next-generation-seq

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23

Agnik, Haldar, and Kumar Singh Ajay. "A Transcriptomic Analysis to Identify Prevalent lncRNAs in Gingivobuccal Oral Cancer." Indian Journal of Science and Technology 16, no. 14 (2023): 1082–89. https://doi.org/10.17485/IJST/v16i14.2163.

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Abstract <strong>Objective:</strong> Gingivobuccal cancer is a subtype of oral cancer, prevalent in developing countries where the use of tobacco and Areca (betel) nut is rampant. This study aims to search through the publically available NCBI data to find comparisons between the normal and cancer-affected transcripts of RNA, which are molecules that copied the genetic information from DNA and that will be “read” as instructions to produce other molecules in the body. Using bioinformatic tools, we analyzed the variations in the transcripts and examined how they were correlated

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24

Philippidis, Alex. "Big Data Duo: Edico Genome, Dell EMC Partner on NGS Data Bundle." Clinical OMICs 4, no. 1 (2017): 30. http://dx.doi.org/10.1089/clinomi.04.01.25.

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25

MIKOSHI, Taiju, Yoshihito FUKANO, Yuki MIYAZAWA, and Kenji YAMAGISHI. "Development of NGS data Analysis Program for RNA-Seq." Journal of Computer Chemistry, Japan 13, no. 6 (2014): 299–300. http://dx.doi.org/10.2477/jccj.2014-0049.

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26

Bongcam-Rudloff, Erik, Teresa K. Attwood, Ana Conesa, Andreas Gisel, and Burkhard Rost. "The Next NGS Challenge Conference: Data Processing and Integration." EMBnet.journal 19, A (2013): 3. http://dx.doi.org/10.14806/ej.19.a.686.

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27

Backes, Christina, Benjamin Meder, Martin Hart, et al. "Prioritizing and selecting likely novel miRNAs from NGS data." Nucleic Acids Research 44, no. 6 (2015): e53-e53. http://dx.doi.org/10.1093/nar/gkv1335.

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28

Groux, Romain, and Philipp Bucher. "SPar-K: a method to partition NGS signal data." Bioinformatics 35, no. 21 (2019): 4440–41. http://dx.doi.org/10.1093/bioinformatics/btz416.

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Abstract Summary We present SPar-K (Signal Partitioning with K-means), a method to search for archetypical chromatin architectures by partitioning a set of genomic regions characterized by chromatin signal profiles around ChIP-seq peaks and other kinds of functional sites. This method efficiently deals with problems of data heterogeneity, limited misalignment of anchor points and unknown orientation of asymmetric patterns. Availability and implementation SPar-K is a C++ program available on GitHub https://github.com/romaingroux/SPar-K and Docker Hub https://hub.docker.com/r/rgroux/spar-k. Supp

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29

Krachunov, Milko, Dimitar Vassilev, Maria Nisheva, Ognyan Kulev, Valeriya Simeonova, and Vladimir Dimitrov. "Fuzzy Indication of Reliability in Metagenomics NGS Data Analysis." Procedia Computer Science 51 (2015): 2859–63. http://dx.doi.org/10.1016/j.procs.2015.05.448.

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30

Lilje, Liisa, Triin Lillsaar, Ranno Rätsep, Jaak Simm, and Anu Aaspõllu. "Soil sample metagenome NGS data management for forensic investigation." Forensic Science International: Genetics Supplement Series 4, no. 1 (2013): e35-e36. http://dx.doi.org/10.1016/j.fsigss.2013.10.017.

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31

van Deutekom, Hanneke W. M., Wietse Mulder, and Erik H. Rozemuller. "Accuracy of NGS HLA typing data influenced by STR." Human Immunology 80, no. 7 (2019): 461–64. http://dx.doi.org/10.1016/j.humimm.2019.03.007.

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32

Ivanova, Milena, Lisa E. Creary, Bushra Al Hadra, et al. "17th IHIW component “Immunogenetics of Ageing” – New NGS data." Human Immunology 80, no. 9 (2019): 703–13. http://dx.doi.org/10.1016/j.humimm.2019.07.287.

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33

Sobenin, I., A. Zhelankin, Z. Khasanova, V. Orekhova, A. Orekhov, and A. Postnov. "Mitochondrial DNA mutations associated with carotid atherosclerosis: NGS data." Atherosclerosis 252 (September 2016): e80. http://dx.doi.org/10.1016/j.atherosclerosis.2016.07.499.

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34

Wang, Xuning, Charles Tilford, Isaac Neuhaus, et al. "CRISPR-DAV: CRISPR NGS data analysis and visualization pipeline." Bioinformatics 33, no. 23 (2017): 3811–12. http://dx.doi.org/10.1093/bioinformatics/btx518.

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35

Ogasawara, Takeshi, Yinhe Cheng, and Tzy-Hwa Kathy Tzeng. "Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools." PLOS ONE 11, no. 11 (2016): e0167100. http://dx.doi.org/10.1371/journal.pone.0167100.

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36

Lei, Rex, Kaixiong Ye, Zhenglong Gu, and Xuepeng Sun. "Diminishing returns in next-generation sequencing (NGS) transcriptome data." Gene 557, no. 1 (2015): 82–87. http://dx.doi.org/10.1016/j.gene.2014.12.013.

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37

Johansson, Lennart F., Freerk van Dijk, Eddy N. de Boer, et al. "CoNVaDING: Single Exon Variation Detection in Targeted NGS Data." Human Mutation 37, no. 5 (2016): 457–64. http://dx.doi.org/10.1002/humu.22969.

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38

Menon, Sudheer. "Computational Prediction of SARS-CoV-2 Genomic, Proteomic Mutation, and Variants by (NGS) Next-Generation Sequencing Data." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 565–73. https://doi.org/10.21275/sr241206125122.

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39

何之行, 何之行. "英國生醫健康資料之整合應用與資料治理規範". 月旦法學雜誌 331, № 331 (2022): 9–23. http://dx.doi.org/10.53106/1025593133101.

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40

Ruark, Elise, Anthony Renwick, Matthew Clarke, et al. "The ICR142 NGS validation series: a resource for orthogonal assessment of NGS analysis." F1000Research 5 (March 22, 2016): 386. http://dx.doi.org/10.12688/f1000research.8219.1.

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To provide a useful community resource for orthogonal assessment of NGS analysis software, we present the ICR142 NGS validation series. The dataset includes high-quality exome sequence data from 142 samples together with Sanger sequence data at 730 sites; 409 sites with variants and 321 sites at which variants were called by an NGS analysis tool, but no variant is present in the corresponding Sanger sequence. The dataset includes 286 indel variants and 275 negative indel sites, and thus the ICR142 validation dataset is of particular utility in evaluating indel calling performance. The FASTQ fi

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41

Ruark, Elise, Anthony Renwick, Matthew Clarke, et al. "The ICR142 NGS validation series: a resource for orthogonal assessment of NGS analysis." F1000Research 5 (September 5, 2018): 386. http://dx.doi.org/10.12688/f1000research.8219.2.

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To provide a useful community resource for orthogonal assessment of NGS analysis software, we present the ICR142 NGS validation series. The dataset includes high-quality exome sequence data from 142 samples together with Sanger sequence data at 704 sites; 416 sites with variants and 288 sites at which variants were called by an NGS analysis tool, but no variant is present in the corresponding Sanger sequence. The dataset includes 293 indel variants and 247 negative indel sites, and thus the ICR142 validation dataset is of particular utility in evaluating indel calling performance. The FASTQ fi

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42

Alexiou, Athanasios, Dimitrios Zisis, Ioannis Kavakiotis, et al. "DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification." Genes 12, no. 1 (2020): 46. http://dx.doi.org/10.3390/genes12010046.

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microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-

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43

Allen, Julie M., Raphael LaFrance, Ryan A. Folk, Kevin P. Johnson, and Robert P. Guralnick. "aTRAM 2.0: An Improved, Flexible Locus Assembler for NGS Data." Evolutionary Bioinformatics 14 (January 2018): 117693431877454. http://dx.doi.org/10.1177/1176934318774546.

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44

Conesa, Ana, and Erik Bongcam-Rudloff. "‘Next NGS Challenge – Data Processing and Integration’ Conference – Conference report." EMBnet.journal 19, no. 1 (2013): 14. http://dx.doi.org/10.14806/ej.19.1.703.

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45

Krachunov, Milko, Ognyan Kulev, Maria Nisheva, Valeria Simeonova, Deyan Peychev, and Dimitar Vassilev. "Using neural networks to filter predicted errors in NGS data." EMBnet.journal 21, A (2015): 827. http://dx.doi.org/10.14806/ej.21.a.827.

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46

Tappeiner, Elias, Francesca Finotello, Pornpimol Charoentong, Clemens Mayer, Dietmar Rieder, and Zlatko Trajanoski. "TIminer: NGS data mining pipeline for cancer immunology and immunotherapy." Bioinformatics 33, no. 19 (2017): 3140–41. http://dx.doi.org/10.1093/bioinformatics/btx377.

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47

Shraga, R., M. C. Akana, S. L. Bristow, A. Manoharan, and O. Puig. "Detecting Y-chromosome microdeletions using next generation sequencing (NGS) data." Fertility and Sterility 106, no. 3 (2016): e227. http://dx.doi.org/10.1016/j.fertnstert.2016.07.657.

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48

Reisinger, Eva, Lena Genthner, Jules Kerssemakers, et al. "OTP: An automatized system for managing and processing NGS data." Journal of Biotechnology 261 (November 2017): 53–62. http://dx.doi.org/10.1016/j.jbiotec.2017.08.006.

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49

Bertelli, M., G. Marceddu, T. Dallavilla, et al. "PIPE-MAGI, Bioinformatic system for the analysis of NGS data." Journal of Biotechnology 305 (November 2019): S6. http://dx.doi.org/10.1016/j.jbiotec.2019.05.037.

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50

Nakazato, Takeru. "Current situation of DNA Barcoding data in biodiversity and genomics databases and data integration for museomics." Biodiversity Information Science and Standards 3 (June 18, 2019): e35165. https://doi.org/10.3897/biss.3.35165.

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The museomics activity regards museum-preserved specimens as rich resources for DNA studies by extracting and analyzing DNA from these specimens in conjunction with their biodiversity information. Also in biodiversity field, DNA sequence data such as DNA barcoding has become essential as evidence for species identification and phylogenetic analysis as well as occurrence and morphological information. To accelerate biodiversity informatics, it is important to utilize both biodiversity occurrence and morphology data, and bioinformatics sequencing data. There are many databases for biodiversity d

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