%0 Journal Article %1 Langmead:2018:Nat-Rev-Genet:29379135 %A Langmead, B %A Nellore, A %D 2018 %J Nat Rev Genet %K bioinformatics cancer-research cloud-computing %N 4 %P 208-219 %R 10.1038/nrg.2017.113 %T Cloud computing for genomic data analysis and collaboration %U https://www.ncbi.nlm.nih.gov/pubmed/29379135 %V 19 %X Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

@article{Langmead:2018:Nat-Rev-Genet:29379135, abstract = {Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.}, added-at = {2019-07-10T08:07:57.000+0200}, author = {Langmead, B and Nellore, A}, biburl = {https://www.bibsonomy.org/bibtex/226d65dad6dc5901a4520a2cf944cc109/marcsaric}, description = {Cloud computing for genomic data analysis and collaboration. - PubMed - NCBI}, doi = {10.1038/nrg.2017.113}, interhash = {a5650a5a84ad3d616d961399be70bb69}, intrahash = {26d65dad6dc5901a4520a2cf944cc109}, journal = {Nat Rev Genet}, keywords = {bioinformatics cancer-research cloud-computing}, month = apr, number = 4, pages = {208-219}, pmid = {29379135}, timestamp = {2019-07-10T08:07:57.000+0200}, title = {Cloud computing for genomic data analysis and collaboration}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29379135}, volume = 19, year = 2018 }