Trends in Biotechnology
ForumSpecial Issue: Computation and ModelingAre Next-Generation Sequencing Tools Ready for the Cloud?
Section snippets
Why Is NGS Important?
DNA sequencing is the procedure identifying the exact order of nucleotides (guanine, cytosine, adenine, and thymine) inside a DNA molecule. Analyzing DNA sequences has become fundamental to research in biotechnology in various applied fields such as comparative genomics (e.g., metagenomics, rRNA classification, infectious disease diagnostics), genome analysis and SNP research (e.g., diagnostic approaches, disease prevention, analyzing the structure of mutant proteins), regulation of gene
How the Cloud Can Push the Evolution of NGS
Nevertheless, NGS data are complex and voluminous. Although it is possible to analyze a few nucleic acid fragments with reasonable computing resources, conducting a large number of parallel sequencing tasks means processing a huge amount of data in a short time. The enormous amount of genomics data created by NGS techniques is an example of the well-known ‘big data’ problem, which increases the demand for intensive storage and computing resources and requires significant scalable and
Current NGS Tools over the Cloud
We use the term ‘biotechnology cloud’ to indicate a specific provider supplying various cloud-based biotechnological services to researchers. Figure 1 shows the possible NGS services that a biotechnology-cloud provider can currently offer. As highlighted, comparative genomics, genome analysis, SNP research, and regulation of gene expression are currently the major areas for which researchers have adopted cloud-computing solutions to improve the big-data processing associated with NGS [1].
Toward an Ecosystem of Biotechnology Clouds for NGS
Figure 1 shows how most scientific contributions have exploited the IaaS service level, commonly installing existing bioinformatics software solutions on virtual machines to take advantage of resource scalability. In many cases Amazon EC2 was used to deploy existing applications, and Hadoop was adopted to parallelize the big-data processing associated with NGS. Unfortunately, more evolved PaaS and SaaS paradigms are used only in a few cases. Thus, it is evident that NGS cloud solutions are
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