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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Molecular biology and evolution Ročník 39; číslo 3
Hlavní autoři: Grealey, Jason, Lannelongue, Loïc, Saw, Woei-Yuh, Marten, Jonathan, Méric, Guillaume, Ruiz-Carmona, Sergio, Inouye, Michael
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Oxford University Press 02.03.2022
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie: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.
ISSN:0737-4038
1537-1719
1537-1719
DOI:10.1093/molbev/msac034