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dc.contributor.authorGrealey, Jason
dc.contributor.authorLannelongue, Loïc
dc.contributor.authorSaw, Woei-Yuh
dc.contributor.authorMarten, Jonathan
dc.contributor.authorMéric, Guillaume
dc.contributor.authorRuiz-Carmona, Sergio
dc.contributor.authorInouye, Michael
dc.date.accessioned2022-03-14T02:03:40Z
dc.date.available2022-03-14T02:03:40Z
dc.date.issued2022-03-02
dc.identifier.issn0737-4038
dc.identifier.otherPMC8892942
dc.identifier.other35143670
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334932
dc.descriptionFunder: Wellcome Trust
dc.description.abstractBioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this work, we estimate the carbon footprint of bioinformatics (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org, last accessed 2022). We assessed 1) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics, and molecular simulations, as well as 2) computation strategies, such as parallelization, CPU (central processing unit) versus GPU (graphics processing unit), cloud versus local computing infrastructure, and geography. In particular, we found that biobank-scale GWAS emitted substantial kgCO2e and simple software upgrades could make it greener, for example, upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Moreover, switching from the average data center to a more efficient one can reduce carbon footprint by approximately 34%. Memory over-allocation can also be a substantial contributor to an algorithm's greenhouse gas emissions. The use of faster processors or greater parallelization reduces running time but can lead to greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimize kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 8501455
dc.sourceessn: 1537-1719
dc.subjectBioinformatics
dc.subjectGenomics
dc.subjectCarbon Footprint
dc.subjectGreen Algorithms
dc.titleThe Carbon Footprint of Bioinformatics.
dc.typeArticle
dc.date.updated2022-03-14T02:03:40Z
prism.issueIdentifier3
prism.publicationNameMol Biol Evol
prism.volume39
dc.identifier.doi10.17863/CAM.82370
rioxxterms.versionofrecord10.1093/molbev/msac034
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidLannelongue, Loïc [0000-0002-9135-1345]
dc.identifier.eissn1537-1719
pubs.funder-project-idBritish Heart Foundation (RG/13/13/30194, RG/18/13/33946)
pubs.funder-project-idMedical Research Council (2120045)
cam.issuedOnline2022-02-10


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International