Loading…

Practical estimation of cloud storage costs for clinical genomic data

Laboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solu...

Full description

Saved in:
Bibliographic Details
Published in:Practical laboratory medicine 2020-08, Vol.21, p.e00168-e00168, Article e00168
Main Authors: Krumm, Niklas, Hoffman, Noah
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3
cites cdi_FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3
container_end_page e00168
container_issue
container_start_page e00168
container_title Practical laboratory medicine
container_volume 21
creator Krumm, Niklas
Hoffman, Noah
description Laboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solutions tout ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers, and provides complex cost and usage forecasts over 1–20 year timeframes. Parameters include current test volumes, growth rate, data compression, data retention policies, and case re-access rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Two factors were found to markedly decrease the average cost per test: 1) reducing total file size, including through the use of compression, 2) rapid transfer to “cold” or archival storage. In contrast, re-access of data from archival storage tiers was not found to dramatically increase the cost of storage per test. Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.
doi_str_mv 10.1016/j.plabm.2020.e00168
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_7ab2a6c1019c4dc3bc59f16c5de20abc</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S2352551719301052</els_id><doaj_id>oai_doaj_org_article_7ab2a6c1019c4dc3bc59f16c5de20abc</doaj_id><sourcerecordid>2412983537</sourcerecordid><originalsourceid>FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3</originalsourceid><addsrcrecordid>eNp9UU1v1DAQjRCIVqW_AAnlyGW3_ojt-AASqgqtVAkOcLYm4_HiVTZe7Gyl_nu8TanaCydbb968mXmvad5ztuaM64vtej_CsFsLJtiaWIX6V82pkEqslOLm9bP_SXNeypZVTm-MZeJtcyKFEpZxc9pc_ciAc0QYWypz3MEc09Sm0OKYDr4tc8qwoRZTmUsbUq54nB7oG5rSLmLrYYZ3zZsAY6Hzx_es-fX16ufl9er2-7ebyy-3K1RCzatBBM-gt73mPePWM9tx6VF3xA3o3psgBcm-QyZQD0FprY1VvYTQDRB4kGfNzaLrE2zdPtd9871LEN0DkPLGQa7XjOQMDAI0VrMsdh7lgMoGrlF5EgwGrFqfF639YdiRR5rmDOML0ZeVKf52m3TnjDC6s7wKfHwUyOnPobrndrEgjSNMlA7FiY4L20slTaXKhYo5lZIpPI3hzB3zdPWaY57umKdb8qxdH55v-NTzL71K-LQQqHp-Fym7gpEmJB8z4VxNif8d8BdPz7MW</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2412983537</pqid></control><display><type>article</type><title>Practical estimation of cloud storage costs for clinical genomic data</title><source>ScienceDirect®</source><source>PubMed Central</source><creator>Krumm, Niklas ; Hoffman, Noah</creator><creatorcontrib>Krumm, Niklas ; Hoffman, Noah</creatorcontrib><description>Laboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solutions tout ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers, and provides complex cost and usage forecasts over 1–20 year timeframes. Parameters include current test volumes, growth rate, data compression, data retention policies, and case re-access rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Two factors were found to markedly decrease the average cost per test: 1) reducing total file size, including through the use of compression, 2) rapid transfer to “cold” or archival storage. In contrast, re-access of data from archival storage tiers was not found to dramatically increase the cost of storage per test. Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.</description><identifier>ISSN: 2352-5517</identifier><identifier>EISSN: 2352-5517</identifier><identifier>DOI: 10.1016/j.plabm.2020.e00168</identifier><identifier>PMID: 32529017</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><ispartof>Practical laboratory medicine, 2020-08, Vol.21, p.e00168-e00168, Article e00168</ispartof><rights>2020 The Authors</rights><rights>2020 The Authors.</rights><rights>2020 The Authors 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3</citedby><cites>FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276491/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S2352551719301052$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,3536,27901,27902,45756,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32529017$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Krumm, Niklas</creatorcontrib><creatorcontrib>Hoffman, Noah</creatorcontrib><title>Practical estimation of cloud storage costs for clinical genomic data</title><title>Practical laboratory medicine</title><addtitle>Pract Lab Med</addtitle><description>Laboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solutions tout ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers, and provides complex cost and usage forecasts over 1–20 year timeframes. Parameters include current test volumes, growth rate, data compression, data retention policies, and case re-access rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Two factors were found to markedly decrease the average cost per test: 1) reducing total file size, including through the use of compression, 2) rapid transfer to “cold” or archival storage. In contrast, re-access of data from archival storage tiers was not found to dramatically increase the cost of storage per test. Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.</description><issn>2352-5517</issn><issn>2352-5517</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9UU1v1DAQjRCIVqW_AAnlyGW3_ojt-AASqgqtVAkOcLYm4_HiVTZe7Gyl_nu8TanaCydbb968mXmvad5ztuaM64vtej_CsFsLJtiaWIX6V82pkEqslOLm9bP_SXNeypZVTm-MZeJtcyKFEpZxc9pc_ciAc0QYWypz3MEc09Sm0OKYDr4tc8qwoRZTmUsbUq54nB7oG5rSLmLrYYZ3zZsAY6Hzx_es-fX16ufl9er2-7ebyy-3K1RCzatBBM-gt73mPePWM9tx6VF3xA3o3psgBcm-QyZQD0FprY1VvYTQDRB4kGfNzaLrE2zdPtd9871LEN0DkPLGQa7XjOQMDAI0VrMsdh7lgMoGrlF5EgwGrFqfF639YdiRR5rmDOML0ZeVKf52m3TnjDC6s7wKfHwUyOnPobrndrEgjSNMlA7FiY4L20slTaXKhYo5lZIpPI3hzB3zdPWaY57umKdb8qxdH55v-NTzL71K-LQQqHp-Fym7gpEmJB8z4VxNif8d8BdPz7MW</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Krumm, Niklas</creator><creator>Hoffman, Noah</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20200801</creationdate><title>Practical estimation of cloud storage costs for clinical genomic data</title><author>Krumm, Niklas ; Hoffman, Noah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Krumm, Niklas</creatorcontrib><creatorcontrib>Hoffman, Noah</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Practical laboratory medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Krumm, Niklas</au><au>Hoffman, Noah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Practical estimation of cloud storage costs for clinical genomic data</atitle><jtitle>Practical laboratory medicine</jtitle><addtitle>Pract Lab Med</addtitle><date>2020-08-01</date><risdate>2020</risdate><volume>21</volume><spage>e00168</spage><epage>e00168</epage><pages>e00168-e00168</pages><artnum>e00168</artnum><issn>2352-5517</issn><eissn>2352-5517</eissn><abstract>Laboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solutions tout ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers, and provides complex cost and usage forecasts over 1–20 year timeframes. Parameters include current test volumes, growth rate, data compression, data retention policies, and case re-access rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Two factors were found to markedly decrease the average cost per test: 1) reducing total file size, including through the use of compression, 2) rapid transfer to “cold” or archival storage. In contrast, re-access of data from archival storage tiers was not found to dramatically increase the cost of storage per test. Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>32529017</pmid><doi>10.1016/j.plabm.2020.e00168</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2352-5517
ispartof Practical laboratory medicine, 2020-08, Vol.21, p.e00168-e00168, Article e00168
issn 2352-5517
2352-5517
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_7ab2a6c1019c4dc3bc59f16c5de20abc
source ScienceDirect®; PubMed Central
title Practical estimation of cloud storage costs for clinical genomic data
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T22%3A19%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Practical%20estimation%20of%20cloud%20storage%20costs%20for%20clinical%20genomic%20data&rft.jtitle=Practical%20laboratory%20medicine&rft.au=Krumm,%20Niklas&rft.date=2020-08-01&rft.volume=21&rft.spage=e00168&rft.epage=e00168&rft.pages=e00168-e00168&rft.artnum=e00168&rft.issn=2352-5517&rft.eissn=2352-5517&rft_id=info:doi/10.1016/j.plabm.2020.e00168&rft_dat=%3Cproquest_doaj_%3E2412983537%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c525t-b2fd0a898618019d09413dc64e17a68d7f32e384c02c6bf566679583af4baf1f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2412983537&rft_id=info:pmid/32529017&rfr_iscdi=true