ArticlePLoS computational biology2012
On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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57 citing papers in PubMed, 126 citations in OpenAlex.
- Evaluating Pretrained Protein Language Model Embeddings as Proxies for Functional Similarity.Journal of molecular evolution · 2025Article
- Unveiling the functional fate of duplicated genes through expression profiling and structural analysis.Genome research · 2025Article
- Structural Changes in Gene Ontology Reveal Modular and Complex Representations of Biological Function.Molecular biology and evolution · 2025Article
- A longitudinal analysis of function annotations of the human proteome reveals consistently high biases.Database : the journal of biological databases and curation · 2025Article
- Model Organisms in Aging Research: Evolution of Database Annotation and Ortholog Discovery.Genes · 2024Article
- A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.Biometrical journal. Biometrische Zeitschrift · 2024Article
- FAS: assessing the similarity between proteins using multi-layered feature architectures.Bioinformatics (Oxford, England) · 2023Article
- Defining the extent of gene function using ROC curvature.Bioinformatics (Oxford, England) · 2022Article
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- Predicting functions of maize proteins using graph convolutional network.BMC bioinformatics · 2020Article
- Recurrent sequence evolution after independent gene duplication.BMC evolutionary biology · 2020Article
- The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction.Bioinformatics (Oxford, England) · 2020Article
- Benchmarking gene ontology function predictions using negative annotations.Bioinformatics (Oxford, England) · 2020Article
- ARTDeco: automatic readthrough transcription detection.BMC bioinformatics · 2020Article
- A Literature Review of Gene Function Prediction by Modeling Gene Ontology.Frontiers in genetics · 2020Review
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- Identifying mouse developmental essential genes using machine learning.Disease models & mechanisms · 2018Article
- OrthoList 2: A New Comparative Genomic Analysis of Human andGenetics · 2018Article
- Accurate prediction of orthologs in the presence of divergence after duplication.Bioinformatics (Oxford, England) · 2018Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
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Abstract
A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the "functional similarity" between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the "ortholog conjecture" (or, more properly, the "ortholog functional conservation hypothesis"). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an "open world assumption" (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.
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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.