ArticleMolecular biology and evolution2022
The Carbon Footprint of Bioinformatics.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed.
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- From grassroots to strategy: advancing laboratory sustainability at Utrecht university.RSC advances · 2026Article
- Sustainable AI in medicine: balancing benefits and environmental costs.Nature reviews. Nephrology · 2026Article
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- Greening public health: embedding environmental sustainability in research and practice.Frontiers in public health · 2026Article
- MTBseq-nf: Enabling Scalable Tuberculosis Genomics "Big Data" Analysis Through a User-Friendly Nextflow Wrapper for MTBseq Pipeline.Microorganisms · 2025Article
- Calculation of minimum energy pathways in transport proteins.Communications chemistry · 2025Article
- Leveraging GWAS-Identified Markers in Combination with Bayesian and Machine Learning Models to Improve Genomic Selection in Soybean.International journal of molecular sciences · 2025Article
- Transcriptomics in the era of long-read sequencing.Nature reviews. Genetics · 2025Review
- The Earth BioGenome Project Phase II: illuminating the eukaryotic tree of life.Frontiers in science · 2025Article
- Starship giant transposable elements cluster by host taxonomy using k-mer-based phylogenetics.G3 (Bethesda, Md.) · 2025Article
- Feature Architecture-Aware Ortholog Search With fDOG Reveals the Distribution of Plant Cell Wall-Degrading Enzymes Across Life.Molecular biology and evolution · 2025Article
- Mitigating the environmental effects of healthcare: the role of the endocrinologist.Nature reviews. Endocrinology · 2025Review
- Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning.Molecular biology and evolution · 2025Article
- Imperatives and co-benefits of research into climate change and neurological disease.Nature reviews. Neurology · 2025Review
- Efficient detection and characterization of targets of natural selection using transfer learning.bioRxiv : the preprint server for biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.bioRxiv : the preprint server for biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.GigaScience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.