GREENER principles for environmentally sustainable computational science

Lannelongue, Loïc; Aronson, Hans-Erik G.; Bateman, Alex; Birney, Ewan; Caplan, Talia; Juckes, Martin; McEntyre, Johanna; Morris, Andrew D.; Reilly, Gerry; Inouye, Michael

doi:10.1038/s43588-023-00461-y

Cited by 30 publications

(14 citation statements)

References 67 publications

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“…Finally, we must note the detrimental impact of building large-scale proteome or genome models; we are reaching a point where energy and computational consumption of developing and training models is costly both financially and environmentally (33). In this work, we sought to use a modular approach, enabling us to repurpose previous models, as well as easily update components of the model with future developments at a minimal computational cost.…”

Section: Discussionmentioning

confidence: 99%

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

Orenbuch,

Kollasch,

Spinner

et al. 2023

Preprint

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Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants inknowndisease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders from potentially healthy individuals. popEVE identifies 442 genes in a cohort of developmental disorder cases, including evidence of 119 novel genetic disorders without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable. Interactive web viewer and downloads available atpop.evemodel.org.

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Section: Discussionmentioning

confidence: 99%

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

Orenbuch,

Kollasch,

Spinner

et al. 2023

Preprint

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“…Running algorithms, artificial intelligence, high-performance computing and maintaining hardware all correlate with energy usage and ultimately with an environmental footprint. 23,37,38 Tasks such as training an artificial intelligence model corresponds to 5 times the lifetime emissions of an average American car. 39…”

Section: Environmental Impact Of Laboratoriesmentioning

confidence: 99%

The relevance of sustainable laboratory practices

Freese,

Elzinga,

Heinemann

et al. 2024

RSC Sustain.

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“…Computations ultimately require physical resources, and awareness is growing that both the production and the use of these resources can have a considerable environmental footprint [ 77 , 78 ]. The more reproducible some workflows become, the more accurately their environmental footprint can be assessed [ 79 ].…”

Section: Introductionmentioning

confidence: 99%

Computational reproducibility of Jupyter notebooks from biomedical publications

Samuel,

Mietchen

2024

GigaScience

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Background Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows, including for research publications. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications. Approach We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article’s full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion. Results Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions. Conclusions We zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.

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GREENER principles for environmentally sustainable computational science

Cited by 30 publications

References 67 publications

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders

The relevance of sustainable laboratory practices

Computational reproducibility of Jupyter notebooks from biomedical publications

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