Dynamic Deep Genomics Sequence Encoder for Managed File Transfer

References

• E. Pettersson, J. Lundeberg, and A. Ahmadian, “Generations of sequencing technologies,” Genomics, Vol. 93, no. 2, pp. 105–111, 2009.
   PubMed Web of Science ®Google Scholar
• S. A. Byron, K. R. Van Keuren-Jensen, D. M. Engelthaler, J. D. Carpten, and D. W. Craig, “Translating RNA sequencing into clinical diagnostics: Opportunities and challenges,” Nat. Rev. Genet., Vol. 17, no. 5, pp. 257–271, 2016.
   PubMed Web of Science ®Google Scholar
• A. Bayle, et al., “Whole exome sequencing in molecular diagnostics of cancer decreases over time: Evidence from a cost analysis in the French setting,” Eur. J. Heal. Econ., Vol. 22, no. 6, pp. 855–864, 2021.
   PubMed Web of Science ®Google Scholar
• C. Bleidorn, “Sequencing techniques,” in Phylogenomics, C. Bleidorn, Ed. Madrid: Springer, 2017, pp. 43–60.
   Google Scholar
• A. N. Kho, et al., “Practical challenges in integrating genomic data into the electronic health record,” Genet. Med., Vol. 15, no. 10, pp. 772–778, 2013.
   PubMed Web of Science ®Google Scholar
• H. Tang, et al., “Protecting genomic data analytics in the cloud: State of the art and opportunities,” BMC Med. Genom., Vol. 9, no. 1, pp. 1–9, 2016.
   PubMedGoogle Scholar
• B. Langmead, and A. Nellore, “Cloud computing for genomic data analysis and collaboration,” Nat. Rev. Genet., Vol. 19, no. 4, pp. 208–219, 2018.
   PubMed Web of Science ®Google Scholar
• H. Xu, “Big data challenges in genomics,” in Handbook of Statistics, Vol. 43, Arni S. R. Srinivasa Rao, and C. R. Rao, Ed. Amsterdam: Elsevier, 2020, pp. 337–348.
   Google Scholar
• K. Tomczak, P. Czerwińska, and M. Wiznerowicz, “The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge,” Contemp. Oncol., Vol. 19, no. 1A, pp. A68, 2015.
   Google Scholar
• Z. Wang, M. A. Jensen, and J. C. Zenklusen, “A practical guide to the cancer genome atlas (TCGA),” in Statistical Genomics, E. Mathé, and S. Davis, Ed. New York: Humana Press/Springer, 2016, pp. 111–141.
   Google Scholar
• V. Marx, “The big challenges of big data,” Nature, Vol. 498, no. 7453, pp. 255–260, 2013.
   PubMed Web of Science ®Google Scholar
• R. T. Fielding, and G. Kaiser, “The Apache HTTP server project,” IEEE Internet Comput., Vol. 1, no. 4, pp. 88–90, 1997.
   Google Scholar
• S. Praveen, and S. Sidhwa. “Introduction to Filezilla,” 2017.
   Google Scholar
• D. Wannipurage, I. Ranawaka, E. Abeysinghe, M. Christie, S. Marru, and M. Pierce. “A Multi-Protocol, Secure, and Dynamic Data Storage Integration Frameworkfor Multi-tenanted Science Gateway Middleware,” arXiv Prepr. arXiv2107.03882, 2021.
   Google Scholar
• K. Bhuvaneshwar, et al., “A case study for cloud based high throughput analysis of NGS data using the globus genomics system,” Comput. Struct. Biotechnol. J., Vol. 13, pp. 64–74, 2015.
   PubMed Web of Science ®Google Scholar
• A. Ng, P. Greenfield, and S. Chen. “A study of the impact of compression and binary encoding on SOAP performance,” in Proceedings of the Sixth Australasian Workshop on Software and System Architectures (AWSA2005), 2005, pp. 46–56.
   Google Scholar
• M. D. S. Q. Z. Nine, and T. Kosar, “A two-phase dynamic throughput optimization model for big data transfers,” IEEE Trans. Parallel Distrib. Syst., Vol. 32, no. 2, pp. 269–280, 2021.
   Web of Science ®Google Scholar
• D. Yin, E. Yildirim, S. Kulasekaran, B. Ross, and T. Kosar, “A data throughput prediction and optimization service for widely distributed many-task computing,” IEEE Trans. Parallel Distrib. Syst., Vol. 22, no. 6, pp. 899–909, 2010.
   Web of Science ®Google Scholar
• J. Kim, E. Yildirim, and T. Kosar. “A highly-accurate and low-overhead prediction model for transfer throughput optimization,” in S.C. Companion: High Performance Computing, Networking Storage and Analysis, 2012, pp. 787–795.
   Google Scholar
• E. Yildirim, D. Yin, and T. Kosar, “Prediction of optimal parallelism level in wide area data transfers,” IEEE Trans. Parallel Distrib. Syst., Vol. 22, no. 12, pp. 2033–2045, 2011.
   Web of Science ®Google Scholar
• S. Deorowicz, and S. Grabowski, “Compression of DNA sequence reads in FASTQ format,” Bioinformatics., Vol. 27, no. 6, pp. 860–862, 2011.
   PubMed Web of Science ®Google Scholar
• S. W. Hodson, S. W. Poole, T. M. Ruwart, and B. W. Settlemyer. “Moving large data sets over high-performance long distance networks,” Oak Ridge Natl. Lab, 2011. Available: http://info.ornl.gov/sites/publications/files/Pub28508.pdf
   Google Scholar
• J. L. Watson. “Changing destinies: An overview of the Human Genome Project,” 1999.
   Google Scholar
• R. A. Power, J. Parkhill, and T. de Oliveira, “Microbial genome-wide association studies: Lessons from human GWAS,” Nat. Rev. Genet., Vol. 18, no. 1, pp. 41–50, 2017.
   PubMed Web of Science ®Google Scholar
• T. A. Manolio, and F. S. Collins, “The HapMap and genome-wide association studies in diagnosis and therapy,” Annu. Rev. Med., Vol. 60, pp. 443–456, 2009.
   PubMed Web of Science ®Google Scholar
• I. Lappalainen, et al., “The European genome-phenome archive of human data consented for biomedical research,” Nat. Genet., Vol. 47, no. 7, pp. 692–695, 2015.
   PubMed Web of Science ®Google Scholar
• A. K. Parekh, and R. G. Gallager, “A generalized processor sharing approach to flow control in integrated services networks: The single-node case,” IEEE/ACM Trans. Netw., Vol. 1, no. 3, pp. 344–357, 1993.
   Web of Science ®Google Scholar
• M. Alizadeh, et al. “Data center TCP (DCTCP),” in Proceedings of the ACM SIGCOMM 2010 Conference, 2010, pp. 63–74.
   Google Scholar
• S. Floyd, “TCP and explicit congestion notification,” ACM SIGCOMM Comput. Commun. Rev., Vol. 24, no. 5, pp. 8–23, 1994.
   Google Scholar
• B. Vamanan, J. Hasan, and T. N. Vijaykumar, “Deadline-aware datacenter tcp (d2tcp),” ACM SIGCOMM Comput. Commun. Rev., Vol. 42, no. 4, pp. 115–126, 2012.
   Web of Science ®Google Scholar
• Y. Ren, J. Li, S. Shi, L. Li, G. Wang, and B. Zhang, “Congestion control in named data networking – a survey,” Comput. Commun., Vol. 86, pp. 1–11, 2016.
   Web of Science ®Google Scholar
• B. Teitelbaum, S. Hares, L. Dunn, R. Neilson, V. Narayan, and F. Reichmeyer, “Internet2 QBone: building a testbed for differentiated services,” IEEE Netw., Vol. 13, no. 5, pp. 8–16, 1999.
   Web of Science ®Google Scholar
• Y. Rathore, M. K. Ahirwar, and R. Pandey, “A brief study of data compression algorithms,” Int. J. Comput. Sci. Inf. Secur., Vol. 11, no. 10, pp. 86, 2013.
   Google Scholar
• A. J. Pinho, and D. Pratas, “MFCompress: a compression tool for FASTA and multi-FASTA data,” Bioinformatics., Vol. 30, no. 1, pp. 117–118, 2014.
   PubMed Web of Science ®Google Scholar
• J. Seward. “Bzip2 and libbzip2: a program and library for data compression,” 1998. htpp://sources.redhat.com/bzip2.
   Google Scholar
• S. Grumbach, and F. Tahi, “A new challenge for compression algorithms: genetic sequences,” Inf. Process. Manag., Vol. 30, no. 6, pp. 875–886, 1994.
   Web of Science ®Google Scholar
• A. Legout, N. Liogkas, E. Kohler, and L. Zhang, “Clustering and sharing incentives in bittorrent systems,” ACM SIGMETRICS Perform. Eval. Rev., Vol. 35, no. 1, pp. 301–312, 2007.
   Google Scholar
• C. Wilks, D. Maltbie, M. Diekhans, and D. Haussler, “Cghub: Kick-starting the worldwide genome web,” Proc. Asia Pac. Adv. Netw., Vol. 35, pp. 1–13, 2013.
   Google Scholar
• R. T. Fielding, and R. N. Taylor, “Principled design of the modern web architecture,” ACM Trans. Internet Technol., Vol. 2, no. 2, pp. 115–150, 2002.
   Google Scholar
• J. C. Mogul, F. Douglis, A. Feldmann, and B. Krishnamurthy. “Potential benefits of delta encoding and data compression for HTTP,” in Proceedings of the ACM SIGCOMM’97 conference on Applications, technologies, architectures, and protocols for computer communication, 1997, pp. 181–194.
   Google Scholar
• L. Mamatas, I. Matta, P. Papadimitriou, and Y. Koucheryavy. Wired/Wireless Internet Communications: 14th IFIP WG 6.2 International Conference, WWIC 2016, Thessaloniki, Greece, May 25–27, 2016, Proceedings, vol. 9674. Springer, 2016.
   Google Scholar
• R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., Vol. 14, no. 4, pp. 337–339, 2020.
   PubMed Web of Science ®Google Scholar
• J. S. Marron, “Big data in context and robustness against heterogeneity,” Econ. Stat., Vol. 2, pp. 73–80, 2017.
   Web of Science ®Google Scholar
• E. N. Gilbert, and E. F. Moore, “Variable-length binary encodings,” Bell Syst. Tech. J., Vol. 38, no. 4, pp. 933–967, 1959.
   Google Scholar
• S. D. Wickramaratne, and M. D. S. Mahmud. “Bi-directional gated recurrent unit based ensemble model for the early detection of sepsis,” in 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, pp. 70–73.
   Google Scholar
• A. Bhuvaneswari, J. T. J. Thomas, and P. Kesavan, “Embedded bi-directional GRU and LSTM learning models to predict disasterson Twitter data,” Proc. Comput. Sci., Vol. 165, pp. 511–516, 2019.
   Google Scholar
• A. Shewalkar, D. Nyavanandi, and S. A. Ludwig, “Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU,” J. Artif. Intell. Soft Comput. Res., Vol. 9, no. 4, pp. 235–245, 2019.
   Web of Science ®Google Scholar
• S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon. “Cbam: convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3–19.
   Google Scholar
• A. Kaur, A. P. S. Chauhan, and A. K. Aggarwal, “An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network,” Expert Syst. Appl., Vol. 186, p. 115686, 2021.
   Web of Science ®Google Scholar
• K. Cho, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv Prepr. arXiv1406.1078, 2014.
   Google Scholar
• P. Zhou, et al. “Attention-based bidirectional long short-term memory networks for relation classification,” in Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), 2016, pp. 207–212.
   Google Scholar
• Y. Zheng, B. K. Iwana, and S. Uchida, “Mining the displacement of max-pooling for text recognition,” Pattern Recognit., Vol. 93, pp. 558–569, 2019.
   Web of Science ®Google Scholar
• D. Karolchik, et al., “The UCSC genome browser database,” Nucleic Acids Res., Vol. 31, no. 1, pp. 51–54, 2003.
   PubMed Web of Science ®Google Scholar
• E. W. Sayers, et al., “Database resources of the national center for biotechnology information,” Nucleic Acids Res., Vol. 39, no. Suppl. 1, pp. D38–D51, 2010.
   PubMedGoogle Scholar
• B. Chapman, and J. Chang, “Biopython: python tools for computational biology,” ACM Sigbio Newsl., Vol. 20, no. 2, pp. 15–19, 2000.
   Google Scholar
• B. Rekepalli, and A. Vose. “Petascale genomic sequence search,” 2011.
   Google Scholar
• D. Karolchik, et al., “The UCSC table browser data retrieval tool,” Nucleic Acids Res., Vol. 32, no. suppl_1, pp. D493–D496, 2004.
   PubMedGoogle Scholar
• J. Zhou, and K. E. Rudd, “Ecogene 3.0,” Nucleic Acids Res., Vol. 41, no. D1, pp. D613–D624, 2012.
   PubMed Web of Science ®Google Scholar
• K. R. Rosenbloom, et al., “The UCSC genome browser database: 2015 update,” Nucleic Acids Res., Vol. 43, no. D1, pp. D670–D681, 2015.
   PubMed Web of Science ®Google Scholar
• C. M. Hudson, and K. P. Williams, “The tmRNA website,” Nucleic Acids Res., Vol. 43, no. D1, pp. D138–D140, 2015.
   PubMed Web of Science ®Google Scholar
• T. D. Lee, H. Yang, J. Whang, and S. C. Lu, “Cloning and characterization of the human glutathione synthetase 5’-flanking region,” Biochem. J., Vol. 390, no. 2, pp. 521–528, 2005.
   PubMedGoogle Scholar
• D. L. Wheeler, et al., “Database resources of the National Center for Biotechnology,” Nucleic Acids Res., Vol. 31, no. 1, pp. 28–33, 2003.
   PubMed Web of Science ®Google Scholar
• T. Barrett, et al., “NCBI GEO: Archive for functional genomics data sets – update,” Nucleic Acids Res., Vol. 41, no. D1, pp. D991–D995, 2012.
   PubMed Web of Science ®Google Scholar
• M. Aledhari, M. Di Pierro, M. Hefeida, and F. Saeed, “A deep learning-based data minimization algorithm for fast and secure transfer of big genomic datasets,” IEEE Trans. Big Data, Vol. 7, no. 2, pp. 271–284, 2021.
   Web of Science ®Google Scholar
• M. Kappelmann-Fenzl, “Reference genome,” in Next Generation Sequencing and Data Analysis, M. Kappelmann-Fenzl, Ed. Deggendorf, Germany: Springer, 2021, pp. 105–109.
   Google Scholar