The Carbon Footprint of Bioinformatics

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...

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Published in:Molecular biology and evolution Vol. 39; no. 3
Main Authors: Grealey, Jason, Lannelongue, Loïc, Saw, Woei-Yuh, Marten, Jonathan, Méric, Guillaume, Ruiz-Carmona, Sergio, Inouye, Michael
Format: Journal Article
Language:English
Published: United States Oxford University Press 02.03.2022
Subjects:
Air pollution
Algorithms
Australia
Bioinformatics
Carbon
Carbon dioxide
Carbon Footprint
Central processing units
Computational Biology
Computer software industry
CPUs
Discoveries
Ecological footprint
Emissions
Energy conservation
Footprint analysis
Gene sequencing
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Geography
Graphics processing units
Greenhouse gases
Metagenomics
Microprocessors
Parallel processing
Phylogeny
Power consumption
RNA
RNA sequencing
Software
United Kingdom
United States
Upgrading
Australia
United States
United Kingdom
genomics
green algorithms
carbon footprint
bioinformatics
ISSN:0737-4038, 1537-1719, 1537-1719
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Abstract 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.
AbstractList 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.
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.
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.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.
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 C[O.sub.2] equivalent units, kgC[O.sub.2]e) 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 kgC[O.sub.2]e 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 kgC[O.sub.2]e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research. Key words: carbon footprint, bioinformatics, genomics, green algorithms.
Audience Academic
Author Saw, Woei-Yuh
Lannelongue, Loïc
Méric, Guillaume
Inouye, Michael
Marten, Jonathan
Ruiz-Carmona, Sergio
Grealey, Jason
AuthorAffiliation 3 Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge , Cambridge, United Kingdom
4 British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge , Cambridge, United Kingdom
7 British Heart Foundation Centre of Research Excellence, University of Cambridge , Cambridge, United Kingdom
5 Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge , Cambridge, United Kingdom
1 Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute , Melbourne, VIC, Australia
6 Department of Infectious Diseases, Central Clinical School, Monash University , Melbourne, VIC, Australia
2 Department of Mathematics and Statistics, La Trobe University , Melbourne, VIC, Australia
8 The Alan Turing Institute , London, United Kingdom
AuthorAffiliation_xml – name: 8 The Alan Turing Institute , London, United Kingdom
– name: 4 British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge , Cambridge, United Kingdom
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– name: 5 Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge , Cambridge, United Kingdom
– name: 3 Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge , Cambridge, United Kingdom
– name: 1 Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute , Melbourne, VIC, Australia
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– name: 6 Department of Infectious Diseases, Central Clinical School, Monash University , Melbourne, VIC, Australia
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  surname: Inouye
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  organization: Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35143670$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. 2022
The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
COPYRIGHT 2022 Oxford University Press
The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. 2022
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ISSN 0737-4038
1537-1719
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IsDoiOpenAccess true
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Issue 3
Keywords genomics
green algorithms
carbon footprint
bioinformatics
Language English
License This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by/4.0
The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
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Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Present address: Genomics PLC, King Charles House, Park End Street, Oxford, United Kingdom
Jason Grealey, Loïc Lannelongue contributed equally to this work as first authors.
ORCID 0000-0002-9135-1345
OpenAccessLink https://dx.doi.org/10.1093/molbev/msac034
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PublicationTitle Molecular biology and evolution
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Snippet Abstract Bioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified...
Bioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified the...
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SubjectTerms Air pollution
Algorithms
Australia
Bioinformatics
Carbon
Carbon dioxide
Carbon Footprint
Central processing units
Computational Biology
Computer software industry
CPUs
Discoveries
Ecological footprint
Emissions
Energy conservation
Footprint analysis
Gene sequencing
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Geography
Graphics processing units
Greenhouse gases
Metagenomics
Microprocessors
Parallel processing
Phylogeny
Power consumption
RNA
RNA sequencing
Software
United Kingdom
United States
Upgrading
Title The Carbon Footprint of Bioinformatics
URI https://www.ncbi.nlm.nih.gov/pubmed/35143670
https://www.proquest.com/docview/3170889753
https://www.proquest.com/docview/2628301751
https://pubmed.ncbi.nlm.nih.gov/PMC8892942
Volume 39
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