Cloud Computing and the DNA Data Race

By Michael C. Schatz, Ben Langmead, Steven L. Salzberg. 2010

301 citations recorded by the source corpus.

Given the accumulation of DNA sequence data sets at ever-faster rates, what are the key factors you should consider when using distributed and multicore computing systems for analysis?

Subfields: Computer Networks and Communications

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In Computer Networks and Communications

Citations in the corpus

Big Data: Astronomical or Genomical? (2015, background): “(3) Distribution The most practical, and perhaps only, solution for distributing genome sequences at a population scale is to use cloud-computing systems that minimize data movement and maximize code federation [47].”
Opportunities and Challenges of Cloud Computing to Improve Health Care Services (2011, background): “Numerous of articles and resources also reported the successful application of cloud computing in bioinformatics research [15-21,40,41].”
Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells (2014, background): “volumes of data at lower cost, presenting increasing challenges for data management, storage, and analysis [39,40].”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “One promising technology to close the gap between generation and handling of omics data is cloud computing [46, 47].”
An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics (2010, background): “Another computer architecture/service model now being explored is cloud computing [3-5].”
Scalable metagenomic taxonomy classification using a reference genome database (2013, methodology): “This work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.”
Cloud-scale RNA-sequencing differential expression analysis with Myrna (2010, background): “Users considering the appropriateness of the cloud for their work can also consult recent reviews and commentaries on this topic [13,14,16].”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “For example, as illustrated in [134], the bursting of DNA sequence data even wins over “Moore’s Law”, which requires distributed system or cloud computing to bridge the gap between the computational capability and the tremendous data size.”
Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data (2012, background): “…important contribution to bioinformatics, allowing users to customize and extend core to their needs without significant manual intervention, matching the success of extensible platforms in parallel spaces such as network analysis and workflow management (Goecks et al., 2010; Smoot et al., 2010).”