Comparison of Distributed and Parallel NGS Data Analysis Methods based on Cloud Computing
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.
Abstract
With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.
참고문헌 (29)
- M. Choi, "Development Trends of Medical Genomics Using Next Generation Sequencing Techniques", Molecular Cell Biology Newsletter (2014)
- M. C. Schatz, "Cloud Computing and the DNA Data Race", Nature Biotechnology, 28(7), pp.691~693 (2010)[DOI]
- M. Baker, "Next-generation Sequencing: Adjusting to Data Overload", Nature Methods, 7(7), pp.495~499 (2010)[DOI]
- B. Calabrese, "Bioinformatics and Microarray Data Analysis on the Cloud", Methods in Molecular Biology, 1375, pp.25~39 (2016)
- Bioinformatics Analysis of Next-Generation Sequence Data, BRIC View Trend Report, "C. Lee", ~ (2016)
- A. Geraldine, "From FastQ Data to High Confidence Variant Calls: the Genome Analysis Toolkit Best Practices Pipeline", Current Protocols in Bioinformatics, pp.11~10 (2013)
- "BWA"[URL]
- "GATK"[URL]
- B. Langmead, "Ultrafast and Memory-efficient Alignment of Short DNA Sequences to the Human Genome", Genome biology, 10(3) (2009)
- D. Decap, "Halvade: Scalable Sequence Analysis with MapReduce", Bioinformatics, 31(15), pp.2482~2488 (2015)[DOI]
- J. Lee, "Parallel and Distributed PCR Duplication Marking Algorithm Integrated with Genome Sequence Alignment by Using Streaming Technology", Proceedings of TBC 2017 (2017)
- H. Mushtaq, "Cluster-based Apache Spark Implementation of the GATK DNA Analysis Pipeline", Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1471~1477 (2015)
- H. Mushtaq, "Sparkga: A Spark Framework for Cost Effective, Fast and Accurate DNA Analysis at Scale", Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp.148~157 (2017)