Статті в журналах: "Lllumina Next Gen sequencing"

1

Bird, Christianne. "Next-Gen Sequencing Services." Genetic Engineering & Biotechnology News 32, no. 9 (May 2012): 16. http://dx.doi.org/10.1089/gen.32.9.05.

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2

Labant, MaryAnn. "What's Next for Next-Gen Sequencing?" Clinical OMICs 2, no. 2 (February 2015): 14–17. http://dx.doi.org/10.1089/clinomi.02.02.08.

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3

Labant, MaryAnn. "What's Next for Next-Gen Sequencing?" Genetic Engineering & Biotechnology News 35, no. 3 (February 2015): 1, 16–19. http://dx.doi.org/10.1089/gen.35.03.02.

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4

Bready, Barrett, and John Thompson. "Future of Next-Gen Sequencing." Genetic Engineering & Biotechnology News 34, no. 7 (April 1, 2014): 10–11. http://dx.doi.org/10.1089/gen.34.07.05.

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5

Liszewski, Kathy. "The Next Next Thing in Sequencing." Genetic Engineering & Biotechnology News 36, no. 1 (January 2016): 1, 26–27. http://dx.doi.org/10.1089/gen.36.01.02.

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6

Roberts, Josh P. "Creative Approaches to Next-Gen Sequencing." Genetic Engineering & Biotechnology News 33, no. 5 (March 2013): 23–25. http://dx.doi.org/10.1089/gen.33.5.13.

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7

Baker, Shawn C. "Next-Generation Sequencing Challenges." Genetic Engineering & Biotechnology News 37, no. 3 (February 2017): 1, 14–15, 17. http://dx.doi.org/10.1089/gen.37.03.01.

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8

Baker, Shawn C. "Advances in Next-Generation Sequencing." Genetic Engineering & Biotechnology News 36, no. 17 (October 2016): 1, 20–22. http://dx.doi.org/10.1089/gen.36.17.01.

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9

Potera, Carol. "CLC Bio Tackles Next-Gen Sequencing Data." Genetic Engineering & Biotechnology News 31, no. 1 (January 2011): 14–15. http://dx.doi.org/10.1089/gen.31.1.05.

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10

Russell, John. "Data Analysis: Today's Next-Gen Sequencing Imperative." Genetic Engineering & Biotechnology News 33, no. 15 (September 2013): 26, 27, 29. http://dx.doi.org/10.1089/gen.33.15.12.

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11

Dutton, Gail. "AATI Rides the Next-Gen Sequencing Wave." Genetic Engineering & Biotechnology News 34, no. 3 (February 2014): 10, 13. http://dx.doi.org/10.1089/gen.34.03.04.

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12

Fonseca, Rafael, and Esteban Braggio. "The MYDas touch of next-gen sequencing." Blood 121, no. 13 (March 28, 2013): 2373–74. http://dx.doi.org/10.1182/blood-2013-02-481986.

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13

Hayes, D. Neil, and William Y. Kim. "The next steps in next-gen sequencing of cancer genomes." Journal of Clinical Investigation 125, no. 2 (February 2, 2015): 462–68. http://dx.doi.org/10.1172/jci68339.

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14

Tay, Andy. "Next-Generation Sequencing Boosts Clinical Metagenomics." Genetic Engineering & Biotechnology News 42, no. 3 (March 1, 2022): 30–33. http://dx.doi.org/10.1089/gen.42.03.11.

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15

Sigaux, François. "Searching for DNA mutations : the next-gen sequencing." Hématologie 17, no. 5 (September 2011): 303–4. http://dx.doi.org/10.1684/hma.2011.0640.

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16

Guimera, Roman Valls. "bcbio-nextgen: Automated, distributed next-gen sequencing pipeline." EMBnet.journal 17, B (February 28, 2012): 30. http://dx.doi.org/10.14806/ej.17.b.286.

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17

Upton, Mark. "Unknown facet of next gen DNA sequencing history." Genomics 110, no. 4 (July 2018): 277–79. http://dx.doi.org/10.1016/j.ygeno.2017.12.012.

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18

Xu, Y., C. Badea, F. Tran, M. Frick, D. Schneiderman, L. Robert, L. Harris, et al. "Next-Gen sequencing of the transcriptome of triticale." Plant Genetic Resources 9, no. 2 (March 25, 2011): 181–84. http://dx.doi.org/10.1017/s1479262111000281.

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Triticale possesses favourable agronomic attributes originating from both its wheat and rye progenitors, including high grain and biomass yields. Triticale, primarily used as animal feed in North America, is an excellent candidate for production of industrial bio-products. Little is known about the coordination of gene expression of rye and wheat genomes in this intergeneric hybrid, but significant DNA losses from the parental genomes have been reported. To clarify the regulation of gene expression in triticale, we carried out 454 sequencing of cDNAs obtained from root, leaf, stem and floral tissues in different lines of triticale and rye exhibiting different phenotypes and assembled reads into contigs. Related to the data assembly were the absence of reference genomes and the paucity of rye sequences in GenBank or other public databases. Consequently, we have sequenced cDNA libraries from roots, seedlings, leaves, floral tissues and immature seeds to facilitate the identification of triticale sequences originating from rye. To further characterize the wheat-derived cDNAs, we also developed a database close to 25,000 non-redundant full-length wheat coding sequence genes, based on existing databases and contigs that were verified against protein sequences from the grass genomes of Brachypodium distachyon, rice, sorghum and maize.

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19

McKenna, Neil. "Next-Gen Sequencing Gets a Fix on Disease." Genetic Engineering & Biotechnology News 34, no. 15 (September 2014): 36, 38–39. http://dx.doi.org/10.1089/gen.34.15.16.

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20

Macdonald, Gareth John. "Next-Generation Sequencing Drives Progress in Biopharma." Genetic Engineering & Biotechnology News 41, no. 9 (September 1, 2021): 58–60. http://dx.doi.org/10.1089/gen.41.09.22.

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21

Buguliskis, Jeffrey S. "Maximum Resolution Requires Maximum Depth for Next-Gen Sequencing." Clinical OMICs 3, no. 8 (August 2016): 8–11. http://dx.doi.org/10.1089/clinomi.03.08.05.

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22

Wilson, C. A., and V. Simonyan. "FDA's Activities Supporting Regulatory Application of "Next Gen" Sequencing Technologies." PDA Journal of Pharmaceutical Science and Technology 68, no. 6 (November 1, 2014): 626–30. http://dx.doi.org/10.5731/pdajpst.2014.01024.

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23

Salazar, Emil. "Propelled by Next-Gen Sequencing, MDx Gains on Antimicrobial Resistance." Clinical OMICs 2, no. 8 (August 2015): 26–28. http://dx.doi.org/10.1089/clinomi.02.08.08.

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24

Tae, Hongseok, Dongsung Ryu, Suhas Sureshchandra, and Jeong-Hyeon Choi. "ESTclean: a cleaning tool for next-gen transcriptome shotgun sequencing." BMC Bioinformatics 13, no. 1 (2012): 247. http://dx.doi.org/10.1186/1471-2105-13-247.

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25

Samuels, Michael, Jeff Olson, and Coren Milbery. "Next Gen Sequencing and Digital PCR Analysis of Hematological Cancers." Clinical Lymphoma Myeloma and Leukemia 15 (September 2015): S19—S20. http://dx.doi.org/10.1016/j.clml.2015.07.043.

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26

Farrell, Edward D., Jeanette E. L. Carlsson, and Jens Carlsson. "Next Gen Pop Gen: implementing a high-throughput approach to population genetics in boarfish ( Capros aper )." Royal Society Open Science 3, no. 12 (December 2016): 160651. http://dx.doi.org/10.1098/rsos.160651.

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The recently developed approach for microsatellite genotyping by sequencing (GBS) using individual combinatorial barcoding was further improved and used to assess the genetic population structure of boarfish ( Capros aper ) across the species' range. Microsatellite loci were developed de novo and genotyped by next-generation sequencing. Genetic analyses of the samples indicated that boarfish can be subdivided into at least seven biological units (populations) across the species' range. Furthermore, the recent apparent increase in abundance in the northeast Atlantic is better explained by demographic changes within this area than by influx from southern or insular populations. This study clearly shows that the microsatellite GBS approach is a generic, cost-effective, rapid and powerful method suitable for full-scale population genetic studies—a crucial element for assessment, sustainable management and conservation of valuable biological resources.

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27

Scherer, Andreas. "Clinical DNA Analysis Using Next-Generation Sequencing: Past, Present, and Future." Genetic Engineering & Biotechnology News 38, no. 18 (October 15, 2018): 29–30. http://dx.doi.org/10.1089/gen.38.18.10.

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28

Gustafson, Heather, Lorraine Stewart, Noah Theiss, and James Hnatyszyn. "Use of next-gen sequencing in an integrated companion diagnostics workflow." Journal of Clinical Oncology 34, no. 15_suppl (May 20, 2016): e23273-e23273. http://dx.doi.org/10.1200/jco.2016.34.15_suppl.e23273.

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29

Quinones-Mateu, M. E., and V. Endris. "The Sequencing Continuum for Clinical Research: From Sanger to Next Gen." Science 343, no. 6175 (March 6, 2014): 1159. http://dx.doi.org/10.1126/science.343.6175.1159-c.

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30

Taylor, Graham R. "Next Gen Exome Sequencing Reveals Mutations in a Rare Recessive Disorder." Human Mutation 31, no. 8 (June 1, 2010): v. http://dx.doi.org/10.1002/humu.21319.

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31

Gunawan, Asep, Mutasem Ali M. Abuzahra, Kasita Listyarini, Jakaria Jakaria, and Cece Sumantri. "SNP Discovery of Chicken Liver with Divergent Unsaturated Fatty Acid using Next Generation RNA Sequencing." Jurnal Ilmu dan Teknologi Peternakan Tropis 6, no. 1 (January 11, 2019): 100. http://dx.doi.org/10.33772/jitro.v6i1.5807.

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ABSTRAKRNA sequencing memberikan peluang baru untuk mendeteksi variasi SNP (Single Nucleotide Polymorphism) pada perbedaan jaringan dengan perbedaan fenotipe. Tujuan dari penelitian ini adalah untuk mengkarakterisisasi penemuan SNP terbaru terkait perbedaan asam lemak tak jenuh pada ayam dengan menggunakan RNA sequencing. Sebanyak 6 sampel dipilih dari 62 sampel masing-masing 3 sampel tinggi dan 3 sampel rendah yang merepresentasikan perbedaan fenotip yang kontras terkait asam lemak tak jenuh dianalisis dengan menggunakan RNA Sequensing. Hasil identifikasi SNP memperlihatkan sebanyak 1208 SNP pada sampel tinggi dan rendah setelah disejajarkan dengan genom ayam Gallus gallus (GGA) v4.0. Sekitar 91% dari total SNP yang ditemukan memiliki tingkat polimorfisme yang tinggi pada 5 gen yang ditemukan terkait asam lemak yaitu gen SCD, COL6A2, CYP2J2L4, HSD17B4, dan SLC23A3. Gen SCD, HSD17B4, dan SLC23A3 memiliki jumlah titik mutasi dengan jumlah yang paling tinggi masing-masing berturut-turut 18, 13, dan 12 SNP. Tingkat level signifikan yang tinggi dan peranan dari ketiga gen tersebut yang sangat penting terkait komposisi asam lemak mengindikasikan bahwa gen SCD, HSD17B4, dan SLC23A3 merupakan tiga gen baru dan potensial untuk digunakan sebagai penanda seleksi kandungan asam lemak tak jenuh tinggi. Namun, hasil penelitian ini perlu divalidasi dan dikonfirmasi sebagai potensial kandidat gen dalam jumlah ayam yang lebih besar dan breed yang berbeda.Kata kunci: asam lemak, ayam, RNA-Seq, variasi transkriptomikABSTRACTRNA sequencing (RNA-Seq) reveals new opportunity for identification SNP discovery in different tissues with divergent phenotype. The objective of this study was to characterize SNP profile from divergent unsaturated fatty acids using RNA-Seq. Six liver samples were selected from 62 chicken which classified 3 high and 3 low unsaturated fatty acids were analyzed using RNA-Seq. The SNP identification showed 1208 SNPs in chicken samples and a large number of those corresponded to differences between high and low chicken genome assembly Gallus gallus (GGA) v4.0. Among them, about 91% of genes had multiple polymorphisms within 5 genes (SCD, COL6A2, CYP2J2L4, HSD17B4, and SLC23A3). The SCD, HSD17B4, and SLC23A3 contained the largest number of mutations with 18, 13, and 12 SNPs respectively. Combining the significant level of SNPs and gene function related with fatty acid composition allow us to suggest SCD, SLC23A3, HSD17B4 as the three novel and promising candidate genes for selecting unsaturated fatty acids. However, further validation is required to confirm the effect of these candidate genes in larger chicken populations.Keywords: chicken, fatty acids, RNA-Seq, transcriptome variants

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32

Marsic, Damien, and Sergei Zolotukhin. "304. Deriving Useful Data from Next-Gen Sequencing of AAV Capsid Libraries." Molecular Therapy 23 (May 2015): S123. http://dx.doi.org/10.1016/s1525-0016(16)33913-2.

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33

Colman, Rebecca E., Julia Anderson, Darrin Lemmer, Erik Lehmkuhl, Sophia B. Georghiou, Hannah Heaton, Kristin Wiggins, et al. "Rapid Drug Susceptibility Testing of Drug-Resistant Mycobacterium tuberculosis Isolates Directly from Clinical Samples by Use of Amplicon Sequencing: a Proof-of-Concept Study." Journal of Clinical Microbiology 54, no. 8 (May 25, 2016): 2058–67. http://dx.doi.org/10.1128/jcm.00535-16.

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Increasingly complex drug-resistant tuberculosis (DR-TB) is a major global health concern and one of the primary reasons why TB is now the leading infectious cause of death worldwide. Rapid characterization of a DR-TB patient's complete drug resistance profile would facilitate individualized treatment in place of empirical treatment, improve treatment outcomes, prevent amplification of resistance, and reduce the transmission of DR-TB. The use of targeted next-generation sequencing (NGS) to obtain drug resistance profiles directly from patient sputum samples has the potential to enable comprehensive evidence-based treatment plans to be implemented quickly, rather than in weeks to months, which is currently needed for phenotypic drug susceptibility testing (DST) results. In this pilot study, we evaluated the performance of amplicon sequencing ofMycobacterium tuberculosisDNA from patient sputum samples using a tabletop NGS technology and automated data analysis to provide a rapid DST solution (the Next Gen-RDST assay). One hundred sixty-six out of 176 (94.3%) sputum samples from the Republic of Moldova yielded complete Next Gen-RDST assay profiles for 7 drugs of interest. We found a high level of concordance of our Next Gen-RDST assay results with phenotypic DST (97.0%) and pyrosequencing (97.8%) results from the same clinical samples. Our Next Gen-RDST assay was also able to estimate the proportion of resistant-to-wild-type alleles down to mixtures of ≤1%, which demonstrates the ability to detect very low levels of resistant variants not detected by pyrosequencing and possibly below the threshold for phenotypic growth methods. The assay as described here could be used as a clinical or surveillance tool.

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34

Millholland, John M., Maria F. Campo, Cecilia A. Fernandez, and Anthony P. Shuber. "Combining next-gen sequencing of TP53 and FGFR3 for detecting bladder cancer-related mutations." Journal of Clinical Oncology 31, no. 6_suppl (February 20, 2013): 304. http://dx.doi.org/10.1200/jco.2013.31.6_suppl.304.

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304 Background: We have recently described an ultra-deep amplicon sequencing method for FGFR3 in urine that closely replicates the sensitivity found in tissue. While FGFR3 mutations are present in a large fraction of non-invasive tumors, these mutations are rarely detected in invasive bladder tumors. To complement our existing FGFR3 deep sequencing assay, we have developed a sequencing assay for TP53. Mutations in TP53 are commonly found in advanced bladder cancer, and show little overlap with FGFR3 mutations. This proof of concept study demonstrates detection of FGFR3 and TP53 in bladder cancer tissue. Methods: The FGFR3 sequencing assay was performed as described previously, permitting the detection of 9 mutations associated with bladder cancer. Similarly, amplicons were designed against TP53 exons 5-8 using, permitting the detection of 133 unique TP53 mutations previously detected in bladder cancer. Primary amplification was performed on DNA isolated from 3 10µ tissue sections. The resulting PCR products were used as template for emulsion PCR and sequenced using the Ion Torrent PGM. Samples were analyzed for total DNA reads and number of mutant sequencing reads to determine percent mutation. Results: Previously we analyzed 151 non-invasive bladder tumor samples for the presence of FGFR3 mutations. Of these samples, 93 out of 151 (61.5%) were positive for FGFR3 mutations. An additional set of 10 high stage bladder tumor samples were analyzed for mutations in TP53. Of these samples, 5 out of 10 (50.0%) were positive for TP53 mutations, while a separate sample was found to be positive for FGFR3 only. Importantly, a TP53 mutation was detected in a Tis carcinoma in situ sample. These studies will be expanded to a larger set of bladder cancer tissues and urine samples to more accurately assess clinical performance of this FGFR3/TP53 deep sequencing assay. Conclusions: We have developed a multiplexed FGFR3 and TP53 sequencing assay that can detect mutations in a broad range of bladder cancer stages. The complementarity of mutations found in these two genes may allow for the detection of a larger fraction of patients with undiagnosed bladder cancer.

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35

LeMieux, Julianna. "Element of Surprise: San Diego Biotech Seeks to Disrupt Next-Gen Sequencing Space." GEN Biotechnology 1, no. 2 (April 1, 2022): 124–26. http://dx.doi.org/10.1089/genbio.2022.29025.jlm.

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36

Bybee, Seth M., Heather Bracken-Grissom, Benjamin D. Haynes, Russell A. Hermansen, Robert L. Byers, Mark J. Clement, Joshua A. Udall, Edward R. Wilcox, and Keith A. Crandall. "Targeted Amplicon Sequencing (TAS): A Scalable Next-Gen Approach to Multilocus, Multitaxa Phylogenetics." Genome Biology and Evolution 3 (January 1, 2011): 1312–23. http://dx.doi.org/10.1093/gbe/evr106.

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37

Churbanov, Alexander, Rachael Ryan, Nabeeh Hasan, Donovan Bailey, Haofeng Chen, Brook Milligan, and Peter Houde. "HighSSR: high-throughput SSR characterization and locus development from next-gen sequencing data." Bioinformatics 28, no. 21 (September 6, 2012): 2797–803. http://dx.doi.org/10.1093/bioinformatics/bts524.

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38

Corless, Christopher L., Tanaya Neff, Michael C. Heinrich, and Carol Beadling. "Combining semiconductor-based sequencing with amplicon-based cancer gene panels: A rapid next-gen approach to clinical cancer genotyping." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 10591. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.10591.

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10591 Background: Bringing next-gen sequencing into clinical (CLIA-licensed) laboratories is an important step in the advancement of personalized cancer care. We have validated a new sequencing approach on the Ion Torrent (IT) PGM using the AmpliSeq Cancer Panel, which covers hotspot regions across 46 commonly mutated cancer genes. Methods: The AmpliSeq panel is comprised of 190 primer pairs that are co-amplified in a single tube to generate amplicons for sequencing. In our testing only 10ng of input DNA was used. Initial PCR was for 20 cycles, after which the amplicons were ligated with sequencing/barcode adapters, amplified for an additional 7 cycles, and then subjected to emulsion PCR. The resulting nanospheres were sequenced on an IT 316 chip. Results: We sequenced 44 samples of FFPE-derived tumor DNA that were previously genotyped on a mass spectroscopy (MS)-based panel. Samples were barcoded and sequenced in batches of 4, yielding an average of 2034 reads per amplicon (range: 95-5162) and an average read length of 76bp. Overall, 95.4% of reads were on target, and 79% of reads were AQ20 or better; 95% of the 190 amplicons had over 500 reads. All 42 known point mutations were accurately identified by the variant caller software. Seventeen in/dels from 4 to 63 bp in length were at least partially visible upon manual inspection of the read alignments, but some were not accurately called by the software. In addition, 22 new mutations were identified in gene regions not covered on our MS-based panel. We demonstrated sensitivity to the level of 5% mutant allele, and the correlation between allelic ratios measured by MS and IT sequencing was excellent (r2=0.81). Two DNA samples from laser-captured tumor worked well with the AmpliSeq Panel. Conclusions: Combining solid-state sequencing with a highly multiplexed PCR method for library construction is a rapid (48 hr) approach for next-gen sequencing of clinical cancer samples. The process is highly scalable and larger, cancer-specific amplicon panels are in development. Automated identification of in/dels remains a challenge in next-gen sequencing output.

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39

Dagogo-Jack, Ibiayi, Marguerite Rooney, Rebecca Nagy, Subba Digumarthy, Emily Chin, Jennifer Ackil, Justin F. Gainor, Jessica Jiyeong Lin, Richard B. Lanman, and Alice Tsang Shaw. "Longitudinal analysis of plasma ALK mutations during treatment with next-generation ALK inhibitors." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 9068. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.9068.

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9068 Background: Next-generation ALK tyrosine kinase inhibitors (TKIs) are the cornerstone of management of ALK-positive (ALK+) lung cancer. Each ALK TKI has a unique spectrum of activity against distinct ALK kinase domain mutations (muts). Plasma genotyping is a promising strategy for identifying ALK muts at relapse on ALK TKIs. Methods: To detect ALK muts, we performed next-generation sequencing (Guardant360) of circulating tumor DNA from patients (pts) with ALK+ lung cancer relapsing on a second-generation (2nd-gen) ALK TKI (n = 65) or the third-generation (3rd-gen) TKI lorlatinib (n = 26). Results: Among 65 pts progressing on a 2nd-gen TKI, 49 (75%) had only received one 2nd-gen ALK TKI prior to analysis: n = 42 alectinib, n = 3 each brigatinib/ceritinib, and n = 1 ensartinib. We detected an ALK mut in 42/65 (65%) specimens at relapse, among which ALK G1202R (32%) and I1171X (23%) were the most common. Sixteen (25%) pts had ≥2 ALK muts at progression on a 2nd-gen TKI. Among 26 pts progressing on lorlatinib (all of whom had previously relapsed on a 2nd-gen ALK TKI), we identified ALK muts in 20 (76%), including 14 (54%) with ≥2 ALK muts. Detection of ≥2 ALK muts was more common at relapse on lorlatinib compared to a 2nd-gen TKI (p = 0.013). To assess the evolution of ALK muts during treatment with different TKIs, we analyzed serial plasma specimens from 20 pts treated with sequential 2nd-gen/2nd-gen or 2nd-gen/3rd-gen TKIs. Among six pts who received alectinib followed by brigatinib, repeat plasma analysis at brigatinib progression revealed persistence of pre-brigatinib ALK muts in two pts (one L1196M and one G1202R), expansion of G1202R in one pt, and acquisition of new ALK muts in three pts. Among 14 pts who received a 2nd-gen TKI followed by lorlatinib, 11 had persistence of pre-lorlatinib ALK muts and 8 acquired ≥1 additional ALK muts at lorlatinib progression. The most frequently acquired ALK mut was D1203N in four of eight cases. Conclusions: ALK resistance muts are prevalent at relapse on next-generation ALK TKIs and increase with each successive generation of ALK TKIs. These findings suggest that sequential therapy with increasingly potent ALK TKIs may select for compound ALK muts and/or fuel tumor heterogeneity.

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40

Devanna, P., X. S. Chen, J. Ho, D. Gajewski, S. D. Smith, A. Gialluisi, C. Francks, S. E. Fisher, D. F. Newbury, and S. C. Vernes. "Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders." Molecular Psychiatry 23, no. 5 (March 14, 2017): 1375–84. http://dx.doi.org/10.1038/mp.2017.30.

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41

Blum, Andrew, Vinay Varadan, Yan Guo, Ann Marie Kieber-Emmons, Lakshmeswari Ravi, Apoorva K. Chandar, Marcia I. Canto, et al. "377 Discovery of Novel Gene-Fusions in Esophageal Adenocarcinoma Using Next-Gen RNA Sequencing." Gastroenterology 148, no. 4 (April 2015): S—78. http://dx.doi.org/10.1016/s0016-5085(15)30271-7.

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42

Nguyen, Thien Khanh. "Genetic alterations in head and neck squamous cell carcinoma: The next-gen sequencing era." World Journal of Medical Genetics 3, no. 4 (2013): 22. http://dx.doi.org/10.5496/wjmg.v3.i4.22.

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43

Cani, Andi K., Daniel H. Hovelson, Andrew S. McDaniel, Seth Sadis, Michaela J. Haller, Venkata Yadati, Anmol M. Amin, et al. "Next-Gen Sequencing Exposes Frequent MED12 Mutations and Actionable Therapeutic Targets in Phyllodes Tumors." Molecular Cancer Research 13, no. 4 (January 15, 2015): 613–19. http://dx.doi.org/10.1158/1541-7786.mcr-14-0578.

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44

Zhou, Wen, Bin Li, Lin Li, Wen Ma, Yuanchu Liu, Shuchao Feng, and Zhezhi Wang. "Genome survey sequencing of Dioscorea zingiberensis." Genome 61, no. 8 (August 2018): 567–74. http://dx.doi.org/10.1139/gen-2018-0011.

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Dioscorea zingiberensis (Dioscoreceae) is the main plant source of diosgenin (steroidal sapogenins), the precursor for the production of steroid hormones in the pharmaceutical industry. Despite its large economic value, genomic information of the genus Dioscorea is currently unavailable. Here, we present an initial survey of the D. zingiberensis genome performed by next-generation sequencing technology together with a genome size investigation inferred by flow cytometry. The whole genome survey of D. zingiberensis generated 31.48 Gb of sequence data with approximately 78.70× coverage. The estimated genome size is 800 Mb, with a high level of heterozygosity based on K-mer analysis. These reads were assembled into 334 288 contigs with a N50 length of 1079 bp, which were further assembled into 92 163 scaffolds with a total length of 173.46 Mb. A total of 4935 genes, 81 tRNAs, 69 rRNAs, and 661 miRNAs were predicted by the genome analysis, and 263 484 repeated sequences were obtained with 419 372 simple sequence repeats (SSRs). Among these SSRs, the mononucleotide repeat type was the most abundant (up to 54.60% of the total SSRs), followed by the dinucleotide (29.60%), trinucleotide (11.37%), tetranucleotide (3.53%), pentanucleotide (0.65%), and hexanucleotide (0.25%) repeat types. The 1C-value of D. zingiberensis was calibrated against Salvia miltiorrhiza and calculated as 0.87 pg (851 Mb) by flow cytometry, which was very close to the result of the genome survey. This is the first report of genome-wide characterization within this taxon.

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Zinetti, Lee-Anne, Damian M. Goodridge, Steve Hodges, Tim Gilchrist, and David C. Sayer. "282-P: Assign-ATF: Sequence analysis software for HLA and non-HLA applications and Now-Gen and Next-Gen sequencing." Human Immunology 70 (November 2009): S154. http://dx.doi.org/10.1016/j.humimm.2009.09.315.

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Emerson, Ryan, Anna Sherwood, William DeWitt, Bryan Howie, Mark Rieder, and Harlan Robins. "A next-gen pipeline for generation, error correction and annotation of high-throughput immunosequencing data (TECH1P.842)." Journal of Immunology 192, no. 1_Supplement (May 1, 2014): 69.10. http://dx.doi.org/10.4049/jimmunol.192.supp.69.10.

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Abstract Immunosequencing is emerging as a new technology with substantial scientific and clinical relevance in immunology, hematology and oncology. Thanks to the highly-randomized somatic rearrangement of antigen receptor genes in lymphocytes, each clonal population of lymphocytes bears a receptor rearrangement that is essentially unique. Thus, sequencing of rearranged antigen receptors in B and T cells can provide a thorough characterization of the diversity and clonal structure of the adaptive immune system. Despite the significant promise of immunosequencing, accurate and consistent methods for data generation and cleaning have proven elusive in practice. Outstanding issues include biased amplification of rearranged antigen receptor genes; computational correction of PCR and sequencing errors in raw sequencing data; alignment of receptor sequences to germline gene receptor loci for accurate reconstruction of rearrangement events; and methods for extrapolation from sequencing reads attributed to each unique rearrangement to absolute input cell numbers. Using a set of robust experiments in which carefully-controlled conditions and synthetic molecules are utilized to precisely determine correct output, we have developed an experimental paradigm and data analysis pipeline that address these concerns and set a new standard for accuracy and data quality in the field of immunosequencing.

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Bolz, H. "Next-Generation Sequencing: Quantensprung für Forschung und Diagnostik in der Ophthalmologie." Klinische Monatsblätter für Augenheilkunde 234, no. 03 (March 2017): 280–88. http://dx.doi.org/10.1055/s-0043-103962.

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ZusammenfassungViele Augenerkrankungen können erblich bedingt sein und weisen oft eine ausgeprägte genetische Heterogenität auf, d. h. sie können durch Mutationen in vielen verschiedenen Genen verursacht werden. Dies trifft besonders auf die Netzhautdystrophien zu: Mehr als 200 Gene sind für die isolierten Formen (z. B. Lebersche kongenitale Amaurose, Retinitis pigmentosa, Zapfen-Stäbchen-Dystrophie, kongenitale stationäre Nachtblindheit) und für Syndrome, bei denen zusätzlich Dysfunktionen oder Fehlbildungen anderer Organsysteme vorliegen, bekannt. Die gezielte Auswahl einzelner Gene für die Diagnostik war bislang schwierig und ihre Analyse mit der DNA-Sequenzierung nach der Sanger-Methode extrem aufwendig: Das Krankheitsbild lässt selten einen Rückschluss auf das betroffene Gen zu, und die Häufigkeiten von Mutationen der einzelnen Gene etwa bei der LCA waren nur unzureichend bekannt. Umfassende molekulargenetisch-diagnostische Abklärungen waren daher in den meisten Fällen nicht möglich. Die unter dem Sammelbegriff Next-Generation Sequencing (NGS) zusammengefassten Verfahren der Hochdurchsatzsequenzierung haben innerhalb weniger Jahre zunächst die humangenetische Forschung und dann die molekulargenetische Diagnostik revolutioniert. Dies hat weitreichende Implikationen für die individuelle Betreuung und Beratung von Patienten mit erblichen Augenerkrankungen und deren Familien.

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Hoffecker, Ian T., Yunshi Yang, Giulio Bernardinelli, Pekka Orponen, and Björn Högberg. "A computational framework for DNA sequencing microscopy." Proceedings of the National Academy of Sciences 116, no. 39 (September 4, 2019): 19282–87. http://dx.doi.org/10.1073/pnas.1821178116.

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We describe a method whereby microscale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without conventional optics. Barcoded DNA “polymerase colony” (polony) amplification techniques enable one to distinguish specific locations of a surface by their sequence. Image formation is based on pairwise fusion of uniquely tagged and spatially adjacent polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from pairs of barcodes fused during a polony cross-linking phase, the sequences of which are determined by recovery from the surface and next-generation (next-gen) sequencing. We developed a mathematical and computational framework for this principle called polony adjacency reconstruction for spatial inference and topology and show that Euclidean spatial data may be stored and transmitted in the form of graph topology. Images are formed by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical molecular markers. The theory developed here could serve as a basis for an automated, multiplexable, and potentially superresolution imaging method based purely on molecular information.

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Dent, Leon, Nahed Ismail, Steven Robinson, Gary Rogers, Siddharth Pratap, and Dana Marshall. "Next-gen sequencing of multi-drug resistant Acinetobacter baumanii at Nashville General Hospital at Meharry." BMC Bioinformatics 12, Suppl 7 (2011): A14. http://dx.doi.org/10.1186/1471-2105-12-s7-a14.

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van der Merwe, M., H. McPherson, J. Siow, and M. Rossetto. "Next-Gen phylogeography of rainforest trees: exploring landscape-level cpDNA variation from whole-genome sequencing." Molecular Ecology Resources 14, no. 1 (October 25, 2013): 199–208. http://dx.doi.org/10.1111/1755-0998.12176.

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