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Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application
Open accessAbstract
There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.
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“High-throughput generated omics data (Lightbody et al., 2019) has played a pivotal role in developing precision medicine biomarkers for diseases such as Alzheimer's (Hampel et al., 2017(Hampel et al., , 2016;;Kovacs, 2016), diabetes (Capobianco, 2017;McCarthy, 2017;Mutie et al., 2017), cancer (Borad and LoRusso, 2017;Senft et al., 2017), hypertension (Barnes et al., 2016;Dominiczak et al., 2017),”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Lightbody et al., 2019
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“Such an amount of data poses significant computational challenges, in particular when considering that NGS throughput exponentially increases over the years, and the reduction of sequencing costs per genome outweighs that of the costs of computational power, as described by the Moore’s law [132].”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [132]
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Citations
Cites
Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
Conesa, A., Götz, S., Garcia-Gomez, J. M., Terol, J., Talón, M., & Robles, M. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti610 · Cited by 11,417
Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 10,133
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
MicroRNA expression profiles classify human cancers
Lü, J., Getz, G. A., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005) · Nature · doi:10.1038/nature03702 · Cited by 9,637
Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration
Thorvaldsdottir, H., Robinson, J. T., & Mesirov, J. P. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs017 · Cited by 9,049
Alberts, B., Johnson, A., Lewis, J. H., Morgan, D. O., RAFF, M. C., Roberts, K. J., & Walter, P. (2017) · W.W. Norton eBooks · doi:10.1201/9781315735368 · Cited by 7,937
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,474
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J. B., Cronin, M. T., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,321
The Immune Landscape of Cancer
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,818
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
Recurrent Fusion of TMPRSS2 and ETS Transcription Factor Genes in Prostate Cancer
Tomlins, S. A., Rhodes, D. R., Perner, S., Dhanasekaran, S. M., Mehra, R., Sun, X.-W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., et al. (2005) · Science · doi:10.1126/science.1117679 · Cited by 3,846
Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1195 · Cited by 3,261
The Molecular Taxonomy of Primary Prostate Cancer
Abeshouse, A. A., Ahn, J., Akbani, R., Ally, A., Amin, S. B., Andry, C., Annala, M., Aprikian, A. G., Armenia, J., Arora, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.10.025 · Cited by 3,126
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma
Ally, A., Balasundaram, M., Carlsen, R., Chuah, E., Clarke, A., Dhalla, N., Holt, R. A., Jones, S. J. M., Lee, D., Ma, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.046 · Cited by 2,490
Moffitt, R. A., Marayati, R., Flate, E. L., Volmar, K. E., Loeza, S. G. H., Hoadley, K. A., Rashid, N. U., Williams, L. A., Eaton, S. C., Chung, A. H., et al. (2015) · Nature Genetics · doi:10.1038/ng.3398 · Cited by 2,194
Differential oestrogen receptor binding is associated with clinical outcome in breast cancer
Ross-Innes, C. S., Stark, R., Teschendorff, A. E., Holmes, K. A., Ali, H. R., Dunning, M., Brown, G. D., Gojiš, O., Ellis, I. O., Green, A. R., et al. (2012) · Nature · doi:10.1038/nature10730 · Cited by 2,182
Quail, M., Smith, M. E., Coupland, P., Otto, T. D., Harris, S. R., Connor, T. R., Bertoni, A., Swerdlow, H. P., & Gu, Y. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-341 · Cited by 2,031
Integrated genomic characterization of pancreatic ductal adenocarcinoma
Raphael, B. J., Hruban, R. H., Aguirre, A. J., Moffitt, R. A., Yeh, J. J., Stewart, C., Robertson, A. G., Cherniack, A. D., Gupta, M., Getz, G., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.07.007 · Cited by 2,019
Moore's law: past, present and future
Schaller, R. R. (1997) · IEEE Spectrum · doi:10.1109/6.591665 · Cited by 1,931
Integrated genomic characterization of oesophageal carcinoma
Sun, Q., Kelsen, D. P., Wiznerowicz, M., Lu, Y., Sandusky, G., Wan, Y., Weinhold, N., Wong, A. M., Carvalho, A. L., Luketich, J. D., et al. (2017) · Nature · doi:10.1038/nature20805 · Cited by 1,869
Ten years of next-generation sequencing technology
van Dijk, E., Auger, H., Jaszczyszyn, Y., & Thermes, C. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.07.001 · Cited by 1,755
Diagnostic exome sequencing in persons with severe intellectual disability
de Ligt, J., Willemsen, M. H., van Bon, B. W. M., Kleefstra, T., Yntema, H. G., Kroes, T., Vulto-van Silfhout, A. T., Koolen, D. A., de Vries, P., Gilissen, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1206524 · Cited by 1,546
Hoadley, K. A., Yau, C. S. F., Wolf, D. M., Cherniack, A. D., Tamborero, D., Ng, S., Leiserson, M. D. M., Niu, B., McLellan, M. D., Uzunangelov, V., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.049 · Cited by 1,537
Genome-wide analysis of DNA copy-number changes using cDNA microarrays
Pollack, J. R., Perou, C. M., Alizadeh, A. A., Eisen, M. B., Pergamenschikov, A., Williams, C. F., Jeffrey, S. S., Botstein, D., & Brown, P. O. (1999) · Nature Genetics · doi:10.1038/12640 · Cited by 1,416
Differential Oestrogen Receptor Binding is Associated with Clinical Outcome in Breast Cancer
Stark, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29627-7_30 · Cited by 1,403
Performance comparison of benchtop high-throughput sequencing platforms
Loman, N. J., Misra, R., Dallman, T. J., Constantinidou, C., Gharbia, S. E., Wain, J. R., & Pallen, M. J. (2012) · Nature Biotechnology · doi:10.1038/nbt.2198 · Cited by 1,383
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas
Knijnenburg, T., Wang, L., Zimmermann, M. T., Chambwe, N., Gao, G. F., Cherniack, A. D., Fan, H., Shen, H., Way, G. P., Greene, C. S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.076 · Cited by 1,145
Epigenetics and gene expression
Gibney, E. R., & Nolan, C. M. (2010) · Heredity · doi:10.1038/hdy.2010.54 · Cited by 1,109 · cited 1×
“These levels are connected via genetic data transfer processes, including transcription, translation, binding and protein modification [23, 24].”
BackgroundMethods of integrating data to uncover genotype-phenotype interactions
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085
Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells
Zhao, S., Fung‐Leung, W., Bittner, A., Ngo, K., & Liu, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0078644 · Cited by 1,000
Health Insurance Portability and Accountability Act
(2007) · Sage eBooks · doi:10.4135/9781412952644.n199 · Cited by 971
Shen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849
MOLECULAR BIOLOGY OF THE CELL, 5TH EDITION
Fenderson, B. A. (2008) · Shock · doi:10.1097/01.shk.0000286288.33338.f6 · Cited by 829
The Wellcome Trust Case Control Consortium (2010) · Nature · doi:10.1038/nature08979 · Cited by 807
Massard, C., Michiels, S., Ferté, C., Deley, M. L., Lacroix, L., Hollebecque, A., Verlingue, L., Ileana, E. E., Rosellini, S., Ammari, S., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-1396 · Cited by 802
A case for NOW (Networks of Workstations)
Anderson, T. E., Culler, D., & Patterson, D. A. (1995) · IEEE Micro · doi:10.1109/40.342018 · Cited by 799
Cellular microRNAs contribute to HIV-1 latency in resting primary CD4+ T lymphocytes
Huang, J., Wang, F., Argyris, E. G., Chen, K., Liang, Z., Tian, H., Huang, W., Squires, K., Verlinghieri, G., & Zhang, H. (2007) · Nature Medicine · doi:10.1038/nm1639 · Cited by 766
The International Nucleotide Sequence Database Collaboration
Karsch‐Mizrachi, I., Nakamura, Y., & Cochrane, G. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1006 · Cited by 717
Genome Sequencing Identifies a Basis for Everolimus Sensitivity
Iyer, G., Hanrahan, A. J., Milowsky, M. I., Al‐Ahmadie, H. A., Scott, S. N., Janakiraman, M., Pirun, M., Sander, C., Socci, N. D., Ostrovnaya, I., et al. (2012) · Science · doi:10.1126/science.1226344 · Cited by 697
The European nucleotide archive
Leinonen, R., Akhtar, R., Birney, E., Bower, L., Cerdeno-Tarraga, A., Cheng, Y., Cleland, I., Faruque, N., Goodgame, N., Gibson, R., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq967 · Cited by 690
Harada, H., Inoue, M., Itasaka, S., Hirota, K., Morinibu, A., Shinomiya, K., Zeng, L., Ou, G., Zhu, Y., Yoshimura, M., et al. (2013) · Nature Communications · doi:10.1038/ncomms3314 · Cited by 676
Dupuy, A., & Simon, R. (2007) · Journal of the National Cancer Institute · doi:10.1093/jnci/djk018 · Cited by 658
Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study
Roychowdhury, S., Iyer, M. K., Robinson, D. R., Lonigro, R. J., Wu, Y.-M., Cao, X., Sundaram, S. K., Sam, L. T., Balbin, O. A., Quist, M. J., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3003161 · Cited by 638
Sosnay, P. R., Siklosi, K. R., Van Goor, F., Kaniecki, K., Yu, H., Sharma, N., Ramalho, A. S., Amaral, M. D., Dorfman, R., Zielenski, J., et al. (2013) · Nature Genetics · doi:10.1038/ng.2745 · Cited by 630
CloudBurst: highly sensitive read mapping with MapReduce
Schatz, M. C. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp236 · Cited by 630
Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles
Farshidfar, F., Zheng, S., Gingras, M., Newton, Y., Shih, J., Robertson, A. G., Hinoue, T., Hoadley, K. A., Gibb, E. A., Roszik, J., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.02.033 · Cited by 624
Metabolomics enables precision medicine: “A White Paper, Community Perspective”
for “Precision Medicine and Pharmacometabolomics Task Group”-Metabolomics Society Initiative, Beger, R. D., Dunn, W., Schmidt, M. A., Gross, S. S., Kirwan, J. A., Cascante, M., Brennan, L., Wishart, D. S., Oresic, M., et al. (2016) · Metabolomics · doi:10.1007/s11306-016-1094-6 · Cited by 592
Shen, R., Olshen, A. B., & Ladanyi, M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp659 · Cited by 564
Development and verification of the PAM50-based Prosigna breast cancer gene signature assay
Wallden, B., Storhoff, J. J., Nielsen, T. O., Dowidar, N., Schaper, C., Ferree, S., Liu, S., Leung, S. C. Y., Geiss, G. K., Snider, J., et al. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0129-6 · Cited by 563
Lionel, A. C., Costain, G., Monfared, N., Walker, S., Reuter, M. S., Hosseini, S. M., Thiruvahindrapuram, B., Merico, D., Jobling, R., Nalpathamkalam, T., et al. (2018) · Genetics in Medicine · doi:10.1038/gim.2017.119 · Cited by 557
Transcriptome analysis using next-generation sequencing
Mutz, K.-O., Heilkenbrinker, A., Lönne, M., Walter, J., & Stahl, F. (2012) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2012.09.004 · Cited by 547
Health Insurance Portability and Accountability Act
(2017) · Sage eBooks · doi:10.4135/9781483365817.n660 · Cited by 530
Wagle, N., Berger, M. F., Davis, M. J., Blumenstiel, B. S., DeFelice, M. T., Pochanard, P., Ducar, M. D., Hummelen, P. V., MacConaill, L. E., Hahn, W. C., et al. (2011) · Cancer Discovery · doi:10.1158/2159-8290.cd-11-0184 · Cited by 529
Duffy, M. J., Harbeck, N., Nap, M., Molina, R., Nicolini, A., Senkus, E., & Cardoso, F. (2017) · European Journal of Cancer · doi:10.1016/j.ejca.2017.01.017 · Cited by 523
Searching for SNPs with cloud computing
Langmead, B., Schatz, M. C., Lin, J., Pop, M., & Salzberg, S. L. (2009) · Genome biology · doi:10.1186/gb-2009-10-11-r134 · Cited by 490
Cell type–specific gene expression differences in complex tissues
Shen-Orr, S. S., Tibshirani, R. J., Khatri, P., Bodian, D. L., Staedtler, F., Perry, N. M., Hastie, T. J., Sarwal, M., Davis, M. M., & Butte, A. J. (2010) · Nature Methods · doi:10.1038/nmeth.1439 · Cited by 489
Routes for breaching and protecting genetic privacy
Erlich, Y., & Narayanan, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3723 · Cited by 487
Quehenberger, O., & Dennis, E. A. (2011) · New England Journal of Medicine · doi:10.1056/nejmra1104901 · Cited by 470
Naccache, S. N., Federman, S., Veeraraghavan, N., Zaharia, M., Lee, D., Samayoa, E., Bouquet, J., Greninger, A. L., Luk, K.-C., Enge, B., et al. (2014) · Genome Research · doi:10.1101/gr.171934.113 · Cited by 460
Design and computational analysis of single-cell RNA-sequencing experiments
Bacher, R. L., & Kendziorski, C. M. (2016) · Genome biology · doi:10.1186/s13059-016-0927-y · Cited by 460
Data Management in the Cloud: Limitations and Opportunities.
Abadi, D. J. (2009) · IEEE Data(base) Engineering Bulletin · Cited by 457
The Genomic and Transcriptomic Landscape of a HeLa Cell Line
Landry, J. J. M., Pyl, P. T., Rausch, T., Zichner, T., Tekkedil, M. M., Stütz, A. M., Jauch, A. S., Aiyar, R. S., Pau, G., Delhomme, N., et al. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.005777 · Cited by 455
Proteomics: new perspectives, new biomedical opportunities
Banks, R. E., Dunn, M. J., Hochstrasser, D. F., Sanchez, J., Blackstock, W. P., Pappin, D., & Selby, P. J. (2000) · The Lancet · doi:10.1016/s0140-6736(00)03214-1 · Cited by 451
Integrated Molecular Characterization of Uterine Carcinosarcoma
Cherniack, A. D., Shen, H., Walter, V., Stewart, C., Murray, B. A., Bowlby, R., Hu, X., Ling, S., Soslow, R. A., Broaddus, R. R., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.02.010 · Cited by 440
Library preparation methods for next-generation sequencing: Tone down the bias
van Dijk, E., Jaszczyszyn, Y., & Thermes, C. (2014) · Experimental Cell Research · doi:10.1016/j.yexcr.2014.01.008 · Cited by 436
GPU Cluster for High Performance Computing
Fan, Z. Y., Qiu, F., Kaufman, A. E., et al. (2004) · Proceedings - Supercomputing · doi:10.1109/sc.2004.26 · Cited by 436
DNA sequencing technologies: 2006–2016
Mardis, E. R. (2017) · Nature Protocols · doi:10.1038/nprot.2016.182 · Cited by 421
The real cost of sequencing: higher than you think!
Sboner, A., Mu, X. J., Greenbaum, D. S., Auerbach, R. K., & Gerstein, M. (2011) · Genome biology · doi:10.1186/gb-2011-12-8-125 · Cited by 393
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
Zhang, W., Yu, Y., Hertwig, F., Thierry-Mieg, J., Zhang, W., Thierry-Mieg, D., Wang, J., Furlanello, C., Devanarayan, V., Cheng, J., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0694-1 · Cited by 393
The real cost of sequencing: scaling computation to keep pace with data generation
Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D. J., Salichos, L., Zhang, J., Weinstock, G. M., Isaacs, F. J., Rozowsky, J. S., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0917-0 · Cited by 390
Efficient storage of high throughput DNA sequencing data using reference-based compressionPreprint
Fritz, M. H.-Y., Leinonen, R., Cochrane, G., & Birney, E. (2011) · Genome Research · doi:10.1101/gr.114819.110 · Cited by 388
Roadmap for Developing and Validating Therapeutically Relevant Genomic Classifiers
Simon, R. M. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.02.8712 · Cited by 387
Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.14 · Cited by 378 · cited 1×
“In comparison with other omics levels, it is relatively poorly characterized [54].”
BackgroundNext-Generation Sequencing of Circulating Tumor DNA for Early Cancer Detection
Aravanis, A. M., Lee, M., & Klausner, R. D. (2017) · Cell · doi:10.1016/j.cell.2017.01.030 · Cited by 375
Keller, A., Leidinger, P., Lange, J., Borries, A., Schrörs, H., Scheffler, M., Lenhof, H., Ruprecht, K., & Meese, E. U. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007440 · Cited by 365
Clinical sequencing: is WGS the better WES?
Meienberg, J., Bruggmann, R., Oexle, K., & Mátyás, G. (2016) · Human Genetics · doi:10.1007/s00439-015-1631-9 · Cited by 358
Sense from sequence reads: methods for alignment and assembly
Flicek, P., & Birney, E. (2009) · Nature Methods · doi:10.1038/nmeth.1376 · Cited by 357
Challenges and emerging directions in single-cell analysis
Yuan, G., Cai, L., Elowitz, M. B., Enver, T., Fan, G., Guo, G., Irizarry, R. A., Kharchenko, P. V., Kim, J., Orkin, S. H., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1218-y · Cited by 349
Votintseva, A. A., Bradley, P., Pankhurst, L. J., Elías, C. D. O., Loose, M., Nilgiriwala, K. S., Chatterjee, A., Smith, E. G., Sanderson, N. D., Walker, T. M., et al. (2017) · Journal of Clinical Microbiology · doi:10.1128/jcm.02483-16 · Cited by 344
2018 REFORM OF EU DATA PROTECTION RULES
Viorescu, R. (2017) · European Journal of Law and Public Administration · doi:10.18662/eljpa/11 · Cited by 344
Erratum: Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.52 · Cited by 303
Miller, N., Farrow, E., Gibson, M., Willig, L. K., Twist, G. P., Yoo, B., Marrs, T., Corder, S., Krivohlavek, L. A., Walter, A., et al. (2015) · Genome Medicine · doi:10.1186/s13073-015-0221-8 · Cited by 301
Liao, H., Wu, J., Kuhn, E., Chin, W., Chang, B., Jones, M. D., O'Neil, S., Clauser, K., Karl, J., Hasler, F., et al. (2004) · Arthritis & Rheumatism · doi:10.1002/art.20720 · Cited by 297
Perspective: The precision-oncology illusion
Prasad, V. K. (2016) · Nature · doi:10.1038/537s63a · Cited by 293
Cloud computing and the DNA data race
Schatz, M. C., Langmead, B., & Salzberg, S. L. (2010) · Nature Biotechnology · doi:10.1038/nbt0710-691 · Cited by 284
Angiuoli, S. V., Matalka, M. N., Gussman, A., Galens, K., Vangala, M., Riley, D. R. J., Arze, C., White, J. R., White, O. R., & Fricke, W. F. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-356 · Cited by 282
The MOSIX multicomputer operating system for high performance cluster computing
Barak, A., & Laadan, O. (1998) · Future Generation Computer Systems · doi:10.1016/s0167-739x(97)00037-x · Cited by 273
Kim, K., Lee, H. W., Lee, H., Kim, S. C., Seo, Y. J., Chung, W., Eum, H. H., Nam, D., Kim, J., Joo, K. M., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0692-3 · Cited by 269
A review of bioinformatic pipeline frameworks
Leipzig, J. N. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw020 · Cited by 252
SOAP3: ultra-fast GPU-based parallel alignment tool for short reads
Liu, C.-M., Wong, T. K. F., Wu, E., Luo, R., Yiu, S., Li, Y., Wang, B., Yu, C., Chu, X., Zhao, K., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts061 · Cited by 248
Liu, Y., Wirawan, A., & Schmidt, B. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-117 · Cited by 228
Reference standards for next-generation sequencing
Hardwick, S. A., Deveson, I. W., & Mercer, T. R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.44 · Cited by 227
GPU-BLAST: using graphics processors to accelerate protein sequence alignment
Vouzis, P. D., & Sahinidis, N. V. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq644 · Cited by 220
Standardization and quality management in next-generation sequencing
Endrullat, C., Glökler, J., Franke, P., & Frohme, M. (2016) · Applied & Translational Genomics · doi:10.1016/j.atg.2016.06.001 · Cited by 219
Whole blood and leukocyte RNA isolation for gene expression analyses
Feezor, R. J., Baker, H. V., Mindrinos, M. N., Hayden, D. L., Tannahill, C. L., Brownstein, B. H., Fay, A., MacMillan, S., Laramie, J. M., Xiao, W., et al. (2004) · Physiological Genomics · doi:10.1152/physiolgenomics.00020.2004 · Cited by 214
Health Insurance Portability and Accountability Act of 1996. Public Law 104-191.
(1996) · PubMed · Cited by 213
Genomics: The amazing complexity of the human transcriptome
Frith, M. C., Pheasant, M., & Mattick, J. S. (2005) · European Journal of Human Genetics · doi:10.1038/sj.ejhg.5201459 · Cited by 204
Open software for biologists: from famine to feast
Field, D., Tiwari, B., Booth, T., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt0706-801 · Cited by 199
First FDA Authorization for Next-Generation Sequencer
Collins, F. S., & Hamburg, M. A. (2013) · New England Journal of Medicine · doi:10.1056/nejmp1314561 · Cited by 199
Kennedy, R. D., Bylesjö, M., Kerr, P., Davison, T. S., Black, J. M., Kay, E. W., Holt, R. J., Proutski, V., Ahdesmaki, M. J., Farztdinov, V. M., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2011.35.4498 · Cited by 192
The international nucleotide sequence database collaboration
Karsch-Mizrachi, I., Takagi, T., Cochrane, G., & on behalf of the International Nucleotide Sequence Database Collaboration (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1097 · Cited by 190
The impact of the EU general data protection regulation on scientific research
Chassang, G. (2017) · ecancermedicalscience · doi:10.3332/ecancer.2017.709 · Cited by 182
Integration of Regulatory Networks by NKX3-1 Promotes Androgen-Dependent Prostate Cancer Survival
Tan, P. Y., Chang, C. W., Chng, K. R., Wansa, K. D. S. A., Sung, W.-K. K., & Cheung, E. C. W. (2011) · Molecular and Cellular Biology · doi:10.1128/mcb.05958-11 · Cited by 180
Epigenetic Inheritance of Disease and Disease Risk
Bohacek, J., & Mansuy, I. M. (2012) · Neuropsychopharmacology · doi:10.1038/npp.2012.110 · Cited by 180
Impacts of Variation in the Human Genome on Gene Regulation
Haraksingh, R., & Snyder, M. P. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.015 · Cited by 169
Pharmacologically Active Drug Metabolites: Impact on Drug Discovery and Pharmacotherapy
Obach, R. S. (2013) · Pharmacological Reviews · doi:10.1124/pr.111.005439 · Cited by 165
Updates to the RMAP short-read mapping software
Smith, A. D., Chung, W., Hodges, E. C., Kendall, J., Hannon, G. J., Hicks, J. B., Xuan, Z., & Zhang, M. Q. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp533 · Cited by 164
CUSHAW: a CUDA compatible short read aligner to large genomes based on the Burrows–Wheeler transform
Liu, Y., Schmidt, B., & Maskell, D. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts276 · Cited by 162
Abrams, J. S., Conley, B. A., Mooney, M., Zwiebel, J. A., Chen, A. P., Welch, J. J., Takebe, N., Malik, S. M., McShane, L. M., Korn, E. L., et al. (2014) · American Society of Clinical Oncology Educational Book · doi:10.14694/edbook_am.2014.34.71 · Cited by 159
Gong, T., Hartmann, N., Kohane, I. S., Brinkmann, V., Staedtler, F., Letzkus, M., Bongiovanni, S., & Szustakowski, J. D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027156 · Cited by 158
Next-generation sequencing: big data meets high performance computing
Schmidt, B., & Hildebrandt, A. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.01.014 · Cited by 152
NCBI GEO standards and services for microarray data
Edgar, R., & Barrett, T. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1471 · Cited by 148
BarraCUDA - a fast short read sequence aligner using graphics processing units
Klus, P., Lam, S., Lyberg, D., Cheung, M. S., Pullan, G., McFarlane, I., Yeo, G. S. H., & Lam, B. (2012) · BMC Research Notes · doi:10.1186/1756-0500-5-27 · Cited by 147
Next-Generation Sequencing and Large Genome Assemblies
Henson, J., Tischler, G., & Ning, Z. (2012) · Pharmacogenomics · doi:10.2217/pgs.12.72 · Cited by 146
Network-based machine learning and graph theory algorithms for precision oncology
Zhang, W., CHIEN, J. R., Yong, J., & Kuang, R. (2017) · npj Precision Oncology · doi:10.1038/s41698-017-0029-7 · Cited by 137
McPherson, J. D. (2009) · Nature Methods · doi:10.1038/nmeth.f.268 · Cited by 134
Next-generation sequencing: adjusting to data overload
Baker, M. (2010) · Nature Methods · doi:10.1038/nmeth0710-495 · Cited by 132
Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy
Kühnemund, M., Wei, Q., Darai, E., Wang, Y., Hernández-Neuta, I., Yang, Z., Tseng, D. K., Ahlford, A., Mathot, L., Sjöblom, T., et al. (2017) · Nature Communications · doi:10.1038/ncomms13913 · Cited by 131
MFCompress: a compression tool for FASTA and multi-FASTA data
Pinho, A. J., & Pratas, D. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt594 · Cited by 111
A frame-shift mutation in the cystic fibrosis gene
White, M. B., Amos, J. A., Hsu, J. M. C., Gerrard, B., Finn, P., & Dean, M. C. (1990) · Nature · doi:10.1038/344665a0 · Cited by 109
Redefining Genomic Privacy: Trust and Empowerment
Narayanan, A., Yocum, K., Glazer, D., Farahany, N. A., Olson, M., Stein, L. D., Williams, J. B., Witkowski, J., Kain, R. C., & Erlich, Y. (2014) · bioRxiv · doi:10.1371/journal.pbio.1001983 · Cited by 109
Wiewiórka, M. S., Messina, A., Pacholewska, A., Maffioletti, S., Gawrysiak, P., & Okoniewski, M. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu343 · Cited by 108
Gender, Contraceptives and Individual Metabolic Predisposition Shape a Healthy Plasma Lipidome
Sales, S., Graessler, J., Ciucci, S., Al-Atrib, R., Vihervaara, T., Schuhmann, K., Kauhanen, D., Sysi‐Aho, M., Bornstein, S. R., Bickle, M., et al. (2016) · Scientific Reports · doi:10.1038/srep27710 · Cited by 104
Peplow, M. (2016) · BMJ · doi:10.1136/bmj.i1757 · Cited by 102
Mashima, J., Kodama, Y., Fujisawa, T., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1001 · Cited by 101
Data structures and compression algorithms for genomic sequence data
Brandon, M. C., Wallace, D. C., & Baldi, P. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp319 · Cited by 97
The European Nucleotide Archive in 2017
Silvester, N. C., Alako, B., Amid, C., Cerdeño-Tárraga, A. M., Clarke, L., Cleland, I., Harrison, P. W., Jayathilaka, S., Kay, S., Keane, T., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1125 · Cited by 93
Treatment Algorithms Based on Tumor Molecular Profiling: The Essence of Precision Medicine Trials
Tourneau, C. L., Kamal, M. T., Tsimberidou, A. M., Bedard, P. L., Pierron, G., Callens, C., Rouleau, É., Vincent‐Salomon, A., Servant, N., Alt, M., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv362 · Cited by 91
Arreaza, G., Qiu, P., Pang, L., et al. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17091579 · Cited by 91
Genome sequence data: management, storage, and visualization
Batley, J., & Edwards, D. (2009) · BioTechniques · doi:10.2144/000113134 · Cited by 88
The Utilization of Formalin Fixed-Paraffin-Embedded Specimens in High Throughput Genomic Studies
Zhang, P., Lehmann, B. D., Shyr, Y., & Guo, Y. P. (2017) · International Journal of Genomics · doi:10.1155/2017/1926304 · Cited by 85
Legendre, C., Gooden, G. C., Johnson, K. N., Martinez, R. A., Liang, W. S., & Salhia, B. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0135-8 · Cited by 81
Schatz, M. C., & Langmead, B. (2013) · IEEE Spectrum · doi:10.1109/mspec.2013.6545119 · Cited by 79
Graw, S., Meier, R. L., Minn, K. T., Bloomer, C., Godwin, A. K., Fridley, B. L., Vlad, A., Beyerlein, P., & CHIEN, J. R. (2015) · Scientific Reports · doi:10.1038/srep12335 · Cited by 78
McGuire, A. L., Basford, M., Dressler, L. G., Fullerton, S. M., Koenig, B. A., Li, R., McCarty, C. A., Ramos, E., Smith, M. E., Somkin, C. P., et al. (2011) · Genome Research · doi:10.1101/gr.120329.111 · Cited by 76
Data Transfer Matters for GPU Computing
Fujii, Y., Azumi, T., Nishio, N., et al. (2013) · International Conference on Parallel and Distributed Systems · doi:10.1109/icpads.2013.47 · Cited by 73
Shoenbill, K. A., Fost, N., Tachinardi, U., & Mendonça, E. A. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001694 · Cited by 71
Shi, H., Schmidt, B., Liu, W., & Müller‐Wittig, W. (2010) · Journal of Computational Biology · doi:10.1089/cmb.2009.0062 · Cited by 66
Velez, G., Roybal, C. N., Colgan, D., Tsang, S. H., Bassuk, A. G., & Mahajan, V. B. (2016) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2015.5934 · Cited by 66
A General Reconfigurable Architecture for the BLAST Algorithm
Sotiriades, E., & Dollas, A. (2007) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1007/s11265-007-0069-2 · Cited by 60
Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing
Zhao, S., Prenger, K., Smith, L., Messina, T., Fan, H., Jaeger, E., & Stephens, S. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-425 · Cited by 57
Ciucci, S., Ge, Y., Durán, C., Palladini, A., Jiménez-Jiménez, V., Martínez-Sánchez, L. M., Wang, Y., Sales, S., Shevchenko, A., Poser, S., et al. (2017) · Scientific Reports · doi:10.1038/srep43946 · Cited by 55
A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues
Li, Y., & Xie, X. S. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s5-s11 · Cited by 51
LIPEA: Lipid Pathway Enrichment AnalysisPreprint
Acevedo, A., Durán, C., Ciucci, S., Gerl, M. J., & Cannistraci, C. V. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/274969 · Cited by 50
Saghatchian, M., Mook, S., Pruneri, G., et al. (2013) · The Breast · doi:10.1016/j.breast.2012.12.002 · Cited by 49
Rao, P. N., Uplekar, S., Kayal, S., Mallick, P. K., Bandyopadhyay, N., Kale, S., Singh, O. P., Mohanty, A., Mohanty, S., Wassmer, S. C., et al. (2016) · Journal of Clinical Microbiology · doi:10.1128/jcm.00235-16 · Cited by 49
Metabolic phenotyping for monitoring ovarian cancer patients
Ke, C., Li, A., Hou, Y., Sun, M., Yang, K. G., Cheng, J., Wang, J., Ge, T., Zhang, F., Li, Q., et al. (2016) · Scientific Reports · doi:10.1038/srep23334 · Cited by 49
Large Scale Protein Sequence Alignment Using FPGA Reprogrammable Logic Devices
Dydel, S., & Bała, P. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30117-2_5 · Cited by 47
A case for NOW (networks of workstation)
Patterson, D. A., Culler, D., & Anderson, T. E. (1995) · doi:10.1145/224964.224966 · Cited by 47
Goyal, A., Kwon, H., Lee, K., Garg, R., Yun, S. Y., Kim, Y. H., Lee, S., & Lee, M. S. (2017) · Open Journal of Genetics · doi:10.4236/ojgen.2017.71002 · Cited by 46
Clinical Sensitivity of Cystic Fibrosis Mutation Panels in a Diverse Population
Hughes, E. E., Stevens, C. F., Saavedra‐Matiz, C. A., et al. (2015) · Human Mutation · doi:10.1002/humu.22927 · Cited by 43
Kawalia, A., Motameny, S., Wonczak, S., Thiele, H., Nieroda, L., Jabbari, K., Borowski, S., Sinha, V., Gunia, W., Lang, U., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126321 · Cited by 41
Paoli‐Iseppi, R. D., Johansson, P. A., Menzies, A. M., Dias, K., Pupo, G. M., Kakavand, H., Wilmott, J. S., Mann, G. J., Hayward, N. K., Dinger, M. E., et al. (2016) · Pathology · doi:10.1016/j.pathol.2016.01.001 · Cited by 40
Parallelizing BLAST and SOM Algorithms with MapReduce-MPI Library
Sul, S.-J., & Tovchigrechko, A. (2011) · doi:10.1109/ipdps.2011.180 · Cited by 39
The NCI-MATCH trial and precision medicine in gynecologic cancers
Barroilhet, L. M., & Matulonis, U. A. (2018) · Gynecologic Oncology · doi:10.1016/j.ygyno.2018.01.008 · Cited by 39
Kuperstein, I., Grieco, L., Cohen, D., Thieffry, D., Zinovyev, A. Y., & Barillot, E. (2015) · Mutagenesis · doi:10.1093/mutage/geu078 · Cited by 38
Burkard, M. E., Deming, D. A., Parsons, B. M., Kenny, P. A., Schuh, M. R., Leal, T. A. B., Uboha, N. V., Lang, J. M., Thompson, M. A., Warren, R. M. L., et al. (2017) · JCO Precision Oncology · doi:10.1200/po.16.00022 · Cited by 38
SEVERE SICKLE CELL ANEMIA IS ASSOCIATED WITH INCREASED PLASMA LEVELS OF TNF-R1 AND VCAM-1
Dworkis, D., Klings, E., Solovieff, N., Li, G.-H., Milton, J., Hartley, S., Melista, E., Parente, J., Sebastiani, P., Steinberg, M. H., et al. (2011) · American journal of hematology/oncology · doi:10.1002/ajh.21928 · Cited by 37
Small RNA sequencing reveals metastasis-related microRNAs in lung adenocarcinoma
Daugaard, I., Venø, M. T., Yan, Y., Kjeldsen, T. E., Lamy, P., Hager, H., Kjems, J., & Hansen, L. L. (2017) · Oncotarget · doi:10.18632/oncotarget.15968 · Cited by 37
Methods for DNA methylation analysis and applications in colon cancer
Jordà, M., & Peinado, M. A. (2010) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/j.mrfmmm.2010.06.010 · Cited by 33
Menon, R., Deng, M., Boehm, D., Braun, M., Fend, F., Böhm, D., Biskup, S., & Perner, S. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms13078933 · Cited by 32
Next-generation sequencing data analysis on cloud computing
Kwon, T., Yoo, W. G., Lee, W., Kim, W. B., & Kim, D.-W. (2015) · Genes & Genomics · doi:10.1007/s13258-015-0280-7 · Cited by 32
Secure cloud computing for genomic data
Datta, S., Bettinger, K., & Snyder, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3496 · Cited by 30
Cluster-based Apache Spark implementation of the GATK DNA analysis pipeline
Mushtaq, H., & Al-Ars, Z. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359893 · Cited by 29
Public–Private Partnerships in Cloud-Computing Services in the Context of Genomic Research
Moreno, P. G., Joly, Y., & Knoppers, B. M. (2017) · Frontiers in Medicine · doi:10.3389/fmed.2017.00003 · Cited by 28
A comparative study of ChIP-seq sequencing library preparation methods
Sundaram, A. Y. M., Hughes, T., Biondi, S., Bolduc, N., Bowman, S., Camilli, A., Chew, Y. C., Couture, C., Farmer, A. A., Jerome, J. P., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-3135-y · Cited by 26
GPU-RMAP: Accelerating Short-Read Mapping on Graphics Processors
Aji, A. M., Zhang, L., & Feng, W.-C. (2010) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2010.29 · Cited by 23
Ramdas, T., & Egan, G. K. (2005) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2005.300963 · Cited by 22
A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm
Guo, X., Wang, H., & Devabhaktuni, V. (2012) · ISRN Bioinformatics · doi:10.5402/2012/195658 · Cited by 20
Review of GPU implementation to process of RNA sequence on cancer
Rahman, M. A., & Muniyandi, R. C. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.10.008 · Cited by 20
Rowińska, Z., & Gocławski, J. (2012) · Image Processing & Communications · doi:10.2478/v10248-012-0046-7 · Cited by 19
Prasad, V. K. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx786 · Cited by 19
Parallelization of BLAST with MapReduce for Long Sequence Alignment
Yang, X., Liu, Y.-L., Yuan, C., et al. (2011) · International Symposium on Parallel Architectures, Algorithms and Programming · doi:10.1109/paap.2011.36 · Cited by 18
Qiao, D., Yip, W., & Lange, C. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-100 · Cited by 17
Rackham, O. J. L., Langley, S. R., Oates, T. M., Vradi, E., Harmston, N., Srivastava, P. K., Behmoaras, J. V., Δελλαπόρτας, Π., Bottolo, L., & Petretto, E. G. (2017) · Genetics · doi:10.1534/genetics.116.195008 · Cited by 17
MicroRNA and Putative Target Discoveries in Chrysanthemum Polyploidy Breeding
Zhang, F., Zhao, J., Xu, S., et al. (2017) · International Journal of Genomics · doi:10.1155/2017/6790478 · Cited by 16
Minireview: Progress and Challenges in Proteomics Data Management, Sharing, and Integration
Becnel, L. B., & McKenna, N. J. (2012) · Molecular Endocrinology · doi:10.1210/me.2012-1180 · Cited by 15
Li, G., Bankhead, P., Dunne, P. D., O’Reilly, P. G., James, J. A., Salto‐Tellez, M., Hamilton, P. W., & McArt, D. G. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw044 · Cited by 15
FPGA-based hardware acceleration for local complexity analysis of massive genomic data
Papadopoulos, A., Kirmitzoglou, I., Promponas, V. J., et al. (2012) · Integration · doi:10.1016/j.vlsi.2012.10.003 · Cited by 14
Chau, N. G., & Lorch, J. H. (2015) · The Oncologist · doi:10.1634/theoncologist.2014-0476 · Cited by 14
Opportunities from the use of FPGAs as platforms for bioinformatics algorithms
Chrysos, G., Sotiriades, E., Rousopoulos, C., et al. (2012) · doi:10.1109/bibe.2012.6399733 · Cited by 13
Benchmark Dataset for Whole Genome Sequence Compression
Biji, C. L., & Nair, A. S. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2568186 · Cited by 11
Ma, T., & Zhang, A. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2535251 · Cited by 10
Walcott, F. L., & Dunn, B. K. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.18 · Cited by 9
Is gene discovery research or diagnosis?
Samuels, M. E., Orr, A. C., Guernsey, D. L., Dooley, K., Riddell, C., Hodgkinson, K. A., Ludman, M. D., & Pullman, D. (2008) · Genetics in Medicine · doi:10.1097/gim.0b013e3181770172 · Cited by 9
Erratum: Sense from sequence reads: methods for alignment and assembly
Flicek, P., & Birney, E. (2010) · Nature Methods · doi:10.1038/nmeth0610-479b · Cited by 9
Flagged first, then by citations
Research works printed but not linked: 39
- Fikes, 2017, New machines can sequence human genome in one hour, Illumina announces, The San Diego Union-Tribune
- Edico Genome, 2018, DRAGEN Bio-IT platform
- Wetterstrand, 2017, DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP)
- NCI, 2017, Definition of personalized medicine—National Cancer Institute Dictionary of Cancer Terms
- NCCN, 2018, National Comprehensive Cancer Network—NCCB clinical practice guidelines in oncology
- Qiagen, 2018, GeneRead Sequencing (NGS)
- Genomics England, 2014, UK to become world number one in DNA testing with plan to revolutionise fight against cancer and rare diseases
- ScienceCloud, 2017, A secure cloud solution
- DNAnexus, 2017, DNAnexus
- The Apache Software Foundation, 2014, Welcome to ApacheTM Hadoop®!
- Illumina, 2017, BaseSpace Sequence Hub
- SevenBridges, 2018, Actionable informatics for biomedical research
- Dydel, 2004, Field Programmable Logic and Application. FPL 2004, Lecture Notes in Computer Science, 23
- Melanakos, 2008, Parallel computing on a personal computer
- Carr, 2011, SeqNFind®: a GPU accelerated sequence analysis toolset facilitates bioinformatics, 1
- NVIDIA, 2017, CUDA GPUs
- AWS, 2017, Amazon elastic compute cloud (EC2)
- Microsoft Azure, 2017, Microsoft Azure: cloud computing platform and services
- Xilinx, 2018, Xilinx: Vivado design suite
- Intel, 2018, Intel FPGA SDK for OpenCL—overview
- Intel Altera, 2018, Intel® FPGA Acceleration Hub—acceleration stack for Intel INTEL® FPGA Acceleration Hub—Xeon CPU with FPGAs
- Fieldman, 2016, Microsoft goes all in for FPGAs to build out AI cloud | TOP500 supercomputer sites
- AWS, 2018, Amazon EC2 F1 instances—run customizable FPGAs in the AWS cloud
- NHGRI, 2017, Electronic Medical Records and Genomics (eMERGE) Network
- US Congress, 2008, H.R.493—110th Congress (2007-2008): genetic information nondiscrimination act of 2008
- AWS, 2018, Cloud compliance—Amazon Web Services (AWS) compliance
- Microsoft Azure, 2017, Big compute: HPC and batch large-scale cloud computing power on demand
- BC Platforms, 2017, BC platforms—software platforms for next-generation sequencing
- NCBI, 2017, GenBank home
- ENA, 2018, European nucleotide archive
- Benson, 2013, GenBank, Nucleic Acids Res, 41, D36
- EMBL-EBI, 2017, ArrayExpress—functional genomics data
- US Congress, 2017, Text—H.R.1313—115th Congress (2017-2018): preserving employee wellness programs act
- Sun, 2017, Employees who decline genetic testing could face penalties under proposed bill, The Washington Post
- Herper, 2017, Illumina promises to sequence human genome for $100—but not quite yet
- Heger, 2017, Illumina unveils new high-throughput sequencing instrument at JP Morgan
- AWS, 2015, Architecting for HIPAA security and compliance on Amazon Web Services
- AstraZeneca, 2017, Research-based BioPharmaceutical Company
- Human Proteome Organisation, 2018, The HUPO proteomics standards initiative
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Metadata
- Authors
- Lightbody, G., Haberland, V., Browne, F., Taggart, L., Zheng, H., Parkes, E., & Blayney, J. K.
- Affiliations
- University of Ulster (GB)
- University of Bristol (GB)
- Almac (United Kingdom) (GB)
- Queen's University Belfast (GB)
- Corresponding author
- Lightbody, Gaye
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-06-14
- Volume
- 20
- Issue
- 5
- Pages
- 1795-1811
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC6917217
- OpenAlex
- W2885591122
- Cited by
- 173
- References
- 208
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 26 · 2023: 27 · 2022: 34 · 2021: 25 · 2020: 21 · 2019: 8
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- Ulster University Research Portal (Ulster University) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Academy of Medical Sciences (SGL017\1064)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- next-generation sequencing, personalized medicine, clinical translation, data governance, computational genomics, genomic data interpretation, bioinformatics infrastructure
- MeSH terms
- Cloud Computing, Ethics, Computer Security, Computational Biology, Biomedical Research, Precision Medicine, High-Throughput Nucleotide Sequencing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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