The case for cloud computing in genome informatics

By Lincoln D. Stein. 2010

588 citations recorded by the source corpus.

With DNA sequencing now getting cheaper more quickly than data storage or computation, the time may have come for genome informatics to migrate to the cloud.

Subfields: Information Systems, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Cloud computing, which has already seen wide adoption in genomics [403], could facilitate easier sharing of the large datasets common to biology [404,405], and may be key to scaling deep learning.”
Genome Medicine: past, present and future (2011, unknown): “Fortunately, the software, databases and computing power required for these community efforts are now becoming available through computer grids and cloud computing infrastructures, offering an affordable alternative for genome and translational bioinformatics [6,7].”
Cistrome: an integrative platform for transcriptional regulation studies (2011, methodology): “With more researchers adopting ChIP-chip/seq methods and NGS technologies that are improving at rates beyond Moore ’ s law [32], the production of cistrome data is increasing exponentially.”
Integrative omics for health and disease (2018, background): “Fortunately, cloud-computing-based options have begun to alleviate these concerns 81 , providing elastic computation and storage facilities based on specific requirements from each hospital or healthcare provider system while simultaneously promoting reproducibility in computational processes 82 .”
Detection of Chromosomal Alterations in the Circulation of Cancer Patients with Whole-Genome Sequencing (2012, background): “If sequencing technologies continue to improve at their current pace (28), the amount of sequencing needed for detection of wholegenome structural alterations will soon become affordable.”
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines (2018, unknown): “The cost of sequencing is dropping rapidly while the costs of computing and data storage are dropping more slowly in comparison (Stein, 2010), making it difﬁcult to deploy core analysis on raw data in genomics cohorts.”
Pybedtools: a flexible Python library for manipulating genomic datasets and annotations (2011, background): “[15:43 15/11/2011 Bioinformatics-btr539.tex] Page: 3423 3423–3424 Summary: pybedtools is a flexible Python software library for manipulating and exploring genomic datasets in many common formats.”
The sequence of sequencers: The history of sequencing DNA (2016, background): “…grown at a rate even faster than that seen in the computing revolution described by Moore's law: the complexity of microchips (measured by number of transistors per unit cost) doubles approximately every two years, while sequencing capabilities between 2004 and 2010 doubled every ﬁ ve months [71].”
Methods of integrating data to uncover genotype–phenotype interactions (2015, background): “Additionally, rapid advances in data generation require substantial increases in computational power and storage capabilities of computing systems 19 .”
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].”
DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “New developments such as cloud computing provide cost efficient solutions for decentralized storage and analysis of Big Data (Stein, 2010) while facilitating research collaborations.”
Cloud-scale RNA-sequencing differential expression analysis with Myrna (2010, background): “While sequencing throughput grows at a rate of about 5× per year [10-12], computer speeds are thought to double approximately every 18 or 24 months [13].”
The UCSC Genome Browser database: update 2011 (2010, methodology): “Finally, we plan to offer cloud support for mirrors, providing a Genome Browser image that will enable labs to instantiate a browser for private use without the overhead of a local server (42) and supporting the simple construction of new Genome Browsers on novel genome sequences.”
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “Beyond the technologies used to run, collect and digest large-scale phenotypic evaluations, the field of phenomics faces similar bottlenecks that genomics has been grappling with as the drop in cost of DNA sequencing outpaces the cost of hard drive data storage (Stein 2010).”
Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder (2017, unknown): “Its capacity for bulk data storage, with efficient processing and built-in security, is ushering in a new model for data sharing, enabling access and collaboration across continents 31,32 .”