ArticleCell systems2016
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.Journal of translational medicine · 2024Article
- A tractable physical model for the yeast polarity predicts epistasis and fitness.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Hypothesis-free phenotype prediction within a genetics-first framework.Nature communications · 2023Article
- Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.Bioinformatics (Oxford, England) · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
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- A BioID-derived proximity interactome for SARS-CoV-2 proteins.bioRxiv : the preprint server for biology · 2021Article
- Genetic dissection of complex traits using hierarchical biological knowledge.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.
Identifiers
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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.