The Carbon Footprint of Bioinformatics

TLDR

Bioinformatics relies on large computational infrastructures that emit carbon, yet no study has quantified these environmental costs. The study estimates bioinformatics carbon footprints using the Green Algorithms calculator and offers guidance to reduce power consumption. Using Green Algorithms, the authors estimated carbon footprints for diverse bioinformatics methods (GWAS, RNA‑seq, assembly, metagenomics, phylogenetics, simulations) and computational strategies (parallelization, CPU vs GPU, cloud vs local, geography). They found that large GWAS generate high CO₂e, but software upgrades can cut emissions by 73%, efficient data centers by 34%, while memory over‑allocation and increased parallelization can raise footprints; overall, the work provides actionable strategies to lower bioinformatics carbon emissions.

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.