Journal articles: 'Dati NGS'

1

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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4

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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6

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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7

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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10

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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11

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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12

Picard, Franck, and Guy Perrière. "Bioinformatics developments for NGS data analysis at PRABI." EMBnet.journal 17, B (February 28, 2012): 12. http://dx.doi.org/10.14806/ej.17.b.264.

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13

Vassilev, Dimitar, Milko Krachunov, Ivan Popov, Elena Todorovska, Valeria Simeonova, Pawel Szczesny, Pawel Siedlecki, and Urszula Zelenkiewicz. "Algorithm for error detection in metagonomics NGS data." EMBnet.journal 17, B (February 28, 2012): 28. http://dx.doi.org/10.14806/ej.17.b.277.

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14

Vieira, Filipe G., Anders Albrechtsen, and Rasmus Nielsen. "Estimating IBD tracts from low coverage NGS data." Bioinformatics 32, no. 14 (April 22, 2016): 2096–102. http://dx.doi.org/10.1093/bioinformatics/btw212.

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15

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 (November 8, 2011): 284–85. http://dx.doi.org/10.1093/bioinformatics/btr613.

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16

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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17

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, no. 331 (December 2022): 9–23. http://dx.doi.org/10.53106/1025593133101.

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18

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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19

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

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20

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 (May 6, 2013): 3. http://dx.doi.org/10.14806/ej.19.a.686.

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21

Backes, Christina, Benjamin Meder, Martin Hart, Nicole Ludwig, Petra Leidinger, Britta Vogel, Valentina Galata, et al. "Prioritizing and selecting likely novel miRNAs from NGS data." Nucleic Acids Research 44, no. 6 (December 3, 2015): e53-e53. http://dx.doi.org/10.1093/nar/gkv1335.

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22

Groux, Romain, and Philipp Bucher. "SPar-K: a method to partition NGS signal data." Bioinformatics 35, no. 21 (May 22, 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. Supplementary information Supplementary data are available at Bioinformatics online.

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23

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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24

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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25

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 (July 2019): 461–64. http://dx.doi.org/10.1016/j.humimm.2019.03.007.

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26

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

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27

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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28

Wang, Xuning, Charles Tilford, Isaac Neuhaus, Gabe Mintier, Qi Guo, John N. Feder, and Stefan Kirov. "CRISPR-DAV: CRISPR NGS data analysis and visualization pipeline." Bioinformatics 33, no. 23 (August 14, 2017): 3811–12. http://dx.doi.org/10.1093/bioinformatics/btx518.

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29

Ogasawara, Takeshi, Yinhe Cheng, and Tzy-Hwa Kathy Tzeng. "Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools." PLOS ONE 11, no. 11 (November 18, 2016): e0167100. http://dx.doi.org/10.1371/journal.pone.0167100.

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30

Johansson, Lennart F., Freerk van Dijk, Eddy N. de Boer, Krista K. van Dijk-Bos, Jan D. H. Jongbloed, Annemieke H. van der Hout, Helga Westers, et al. "CoNVaDING: Single Exon Variation Detection in Targeted NGS Data." Human Mutation 37, no. 5 (February 24, 2016): 457–64. http://dx.doi.org/10.1002/humu.22969.

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31

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

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32

Ruark, Elise, Anthony Renwick, Matthew Clarke, Katie Snape, Emma Ramsay, Anna Elliott, Sandra Hanks, Ann Strydom, Sheila Seal, and Nazneen Rahman. "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 files and Sanger sequence results can be accessed in the European Genome-phenome Archive under the accession number EGAS00001001332.

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33

Ruark, Elise, Anthony Renwick, Matthew Clarke, Katie Snape, Emma Ramsay, Anna Elliott, Sandra Hanks, Ann Strydom, Sheila Seal, and Nazneen Rahman. "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 files and Sanger sequence results can be accessed in the European Genome-phenome Archive under the accession number EGAS00001001332.

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34

Alexiou, Athanasios, Dimitrios Zisis, Ioannis Kavakiotis, Marios Miliotis, Antonis Koussounadis, Dimitra Karagkouni, and Artemis G. Hatzigeorgiou. "DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification." Genes 12, no. 1 (December 30, 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-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

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35

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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36

Conesa, Ana, and Erik Bongcam-Rudloff. "‘Next NGS Challenge – Data Processing and Integration’ Conference – Conference report." EMBnet.journal 19, no. 1 (July 24, 2013): 14. http://dx.doi.org/10.14806/ej.19.1.703.

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37

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 (March 25, 2015): 827. http://dx.doi.org/10.14806/ej.21.a.827.

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38

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 (June 15, 2017): 3140–41. http://dx.doi.org/10.1093/bioinformatics/btx377.

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39

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 (September 2016): e227. http://dx.doi.org/10.1016/j.fertnstert.2016.07.657.

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40

Reisinger, Eva, Lena Genthner, Jules Kerssemakers, Philip Kensche, Stefan Borufka, Alke Jugold, Andreas Kling, 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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41

Bertelli, M., G. Marceddu, T. Dallavilla, G. Guerri, P. E. Maltese, E. Manara, and S. Paolacci. "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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42

Voigt, Benjamin, Oliver Fischer, Christian Krumnow, Christian Herta, and Piotr Wojciech Dabrowski. "NGS read classification using AI." PLOS ONE 16, no. 12 (December 22, 2021): e0261548. http://dx.doi.org/10.1371/journal.pone.0261548.

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Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient’s sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out. Typically, this is done by comparison to reference databases. While this approach has been optimized over the past years and works well to detect pathogens that are represented in the used databases, a common challenge in analysing a metagenomic patient sample arises when no pathogen sequences are found: How to determine whether truly no evidence of a pathogen is present in the data or whether the pathogen’s genome is simply absent from the database and the sequences in the dataset could thus not be classified? Here, we present a novel approach to this problem of detecting novel pathogens in metagenomic datasets by classifying the (segments of) proteins encoded by the sequences in the datasets. We train a neural network on the sequences of coding sequences, labeled by taxonomic domain, and use this neural network to predict the taxonomic classification of sequences that can not be classified by comparison to a reference database, thus facilitating the detection of potential novel pathogens.

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43

Wong, William Bruce, Daniel Sheinson, Sarika Ogale, Carlos Flores, and Cary Philip Gross. "The association between Medicare’s next generation sequencing (NGS), national coverage decision (NCD), and NGS utilization." Journal of Clinical Oncology 38, no. 29_suppl (October 10, 2020): 98. http://dx.doi.org/10.1200/jco.2020.38.29_suppl.98.

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98 Background: In 2018, Medicare released a NGS NCD memo which would facilitate reimbursement for NGS tests for patients (pts) with advanced or metastatic cancer who had not been previously tested using NGS for the same cancer and genetic content. We examined the association between the NCD and: a) NGS utilization trends in commercially-insured and Medicare pts and b) repeat NGS testing. Methods: We conducted a retrospective study of pts with advanced non-small cell lung cancer (aNSCLC), metastatic colorectal cancer (mCRC), metastatic breast cancer (mBC) or advanced melanoma (aM), diagnosed 2011 (2013 for mCRC) through Dec. 2019 using the Flatiron Health EHR-derived de-identified database, comprising data from over 280 (largely community based) cancer clinics (~800 sites of care). Pts were classified as Medicare or Commercially insured based on age and insurance type, and grouped into quarters based on their advanced or metastatic diagnosis date. NGS testing rates per quarter were based on evidence of first NGS test within 60 days from diagnosis. We used an interrupted time series analysis to assess NGS utilization trends pre- and post-NCD policy effective date (March 2018). The frequency of repeat NGS testing was assessed among those pts with only 1 primary cancer. Results: The utilization analysis included 70,290 pts while the repeat NGS testing analysis included 51,385 pts. Across the 4 tumors combined, the use of NGS was < 1% in 2011 (both insurance types) and increased to 41% in commercially-insured pts and 37% in Medicare in 2019. In each tumor, NGS utilization was < 6% in Q1 2014; however, the rate of increase varied by tumor, with aNSCLC increasing to 58% in commercial and 48% in Medicare, while mBC and aM remained < 20% in Q4 2019. Among pts with aNSCLC, mCRC, or mBC, the quarterly rate of increase in NGS testing was higher post-NCD compared to pre-NCD (p < 0.05 for pre-post difference in rate of NGS increase within each cancer type). The difference in trends pre- and post-NCD was not significantly different between commercial and Medicare in any of the tumors (p > 0.05 within each cancer type). Repeat NGS testing increased over time from 17.8% (in Q3 2014 to Q2 2016) to 29.6% (in Q2 2018 to Q4 2019). Conclusions: NGS utilization trends significantly changed post-NCD, however the rate of change was not significantly different by insurance, indicating private insurers may also be following the guidance of the NCD. We observed an increase in repeat NGS testing, despite the NCD not covering repeat testing with the same NGS test.

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44

Stelet, Vinicus N., Rafael F. Cita, Matilde Romero, Maristela F. Mendes, and Renata Binato. "P054 Using NGSEngine® data analysis software to analyze third party NGS HLA data." Human Immunology 80 (September 2019): 94. http://dx.doi.org/10.1016/j.humimm.2019.07.106.

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45

Meisner, Jonas, and Anders Albrechtsen. "Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data." Genetics 210, no. 2 (August 21, 2018): 719–31. http://dx.doi.org/10.1534/genetics.118.301336.

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46

Song, Hae Jung, JunMo Lee, Louis Graf, Mina Rho, Huan Qiu, Debashish Bhattacharya, and Hwan Su Yoon. "A novice’s guide to analyzing NGS-derived organelle and metagenome data." ALGAE 31, no. 2 (June 30, 2016): 137–54. http://dx.doi.org/10.4490/algae.2016.31.6.5.

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47

Brouwer, R. W. W., M. C. G. N. van den Hout, C. E. M. Kockx, E. Brosens, B. Eussen, A. de Klein, F. Sleutels, and W. F. J. van IJcken. "Nimbus: a design-driven analyses suite for amplicon-based NGS data." Bioinformatics 34, no. 16 (March 10, 2018): 2732–39. http://dx.doi.org/10.1093/bioinformatics/bty145.

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48

Koelling, Nils, Marie Bernkopf, Eduardo Calpena, Geoffrey J. Maher, Kerry A. Miller, Hannah K. Ralph, Anne Goriely, and Andrew O. M. Wilkie. "amplimap: a versatile tool to process and analyze targeted NGS data." Bioinformatics 35, no. 24 (July 26, 2019): 5349–50. http://dx.doi.org/10.1093/bioinformatics/btz582.

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Abstract Summary amplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing experiments with PCR-based amplicons or capture-based enrichment systems. From raw sequencing reads, amplimap generates output such as read alignments, annotated variant calls, target coverage statistics and variant allele counts and frequencies for each target base pair. In addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as consensus base calling for read families based on unique molecular identifiers and filtering false positive variant calls caused by amplification of off-target loci. Availability and implementation amplimap is available as a free Python package under the open-source Apache 2.0 License. Documentation, source code and installation instructions are available at https://github.com/koelling/amplimap.

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49

Koelling, Nils, Marie Bernkopf, Eduardo Calpena, Geoffrey J. Maher, Kerry A. Miller, Hannah K. Ralph, Anne Goriely, and Andrew O. M. Wilkie. "amplimap: a versatile tool to process and analyze targeted NGS data." Bioinformatics 36, no. 8 (February 26, 2020): 2643. http://dx.doi.org/10.1093/bioinformatics/btz905.

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50

Djedatin, Gustave, Cécile Monat, Stefan Engelen, and Francois Sabot. "DuplicationDetector , a light weight tool for duplication detection using NGS data." Current Plant Biology 9-10 (June 2017): 23–28. http://dx.doi.org/10.1016/j.cpb.2017.07.001.

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Journal articles on the topic 'Dati NGS'

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