The Carbon Footprint of Bioinformatics.
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Authors
Grealey, Jason
Saw, Woei-Yuh
Marten, Jonathan
Méric, Guillaume
Ruiz-Carmona, Sergio
Inouye, Michael
Publication Date
2022-03-02Journal Title
Mol Biol Evol
ISSN
0737-4038
Publisher
Oxford University Press (OUP)
Volume
39
Issue
3
Language
eng
Type
Article
This Version
VoR
Metadata
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Grealey, J., Lannelongue, L., Saw, W., Marten, J., Méric, G., Ruiz-Carmona, S., & Inouye, M. (2022). The Carbon Footprint of Bioinformatics.. Mol Biol Evol, 39 (3) https://doi.org/10.1093/molbev/msac034
Description
Funder: Wellcome Trust
Abstract
Bioinformatic 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.
Keywords
Bioinformatics, Genomics, Carbon Footprint, Green Algorithms
Sponsorship
British Heart Foundation (RG/13/13/30194, RG/18/13/33946)
Medical Research Council (2120045)
Identifiers
PMC8892942, 35143670
External DOI: https://doi.org/10.1093/molbev/msac034
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334932
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