Integration of curated databases to identify genotype-phenotype associations

Author:

Goh Chern-Sing,Gianoulis Tara A,Liu Yang,Li Jianrong,Paccanaro Alberto,Lussier Yves A,Gerstein Mark

Abstract

Abstract Background The ability to rapidly characterize an unknown microorganism is critical in both responding to infectious disease and biodefense. To do this, we need some way of anticipating an organism's phenotype based on the molecules encoded by its genome. However, the link between molecular composition (i.e. genotype) and phenotype for microbes is not obvious. While there have been several studies that address this challenge, none have yet proposed a large-scale method integrating curated biological information. Here we utilize a systematic approach to discover genotype-phenotype associations that combines phenotypic information from a biomedical informatics database, GIDEON, with the molecular information contained in National Center for Biotechnology Information's Clusters of Orthologous Groups database (NCBI COGs). Results Integrating the information in the two databases, we are able to correlate the presence or absence of a given protein in a microbe with its phenotype as measured by certain morphological characteristics or survival in a particular growth media. With a 0.8 correlation score threshold, 66% of the associations found were confirmed by the literature and at a 0.9 correlation threshold, 86% were positively verified. Conclusion Our results suggest possible phenotypic manifestations for proteins biochemically associated with sugar metabolism and electron transport. Moreover, we believe our approach can be extended to linking pathogenic phenotypes with functionally related proteins.

Publisher

Springer Science and Business Media LLC

Subject

Genetics,Biotechnology

Cited by 34 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DeepPPPred: Deep Ensemble Learning with Transformers, Recurrent and Convolutional Neural Networks for Human Protein-Phenotype Co-mention Classification;2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2021-12-09

2. Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes;BMC Bioinformatics;2021-10-16

3. DeepPPPred: An Ensemble of BERT, CNN, and RNN for Classifying Co-mentions of Proteins and Phenotypes;2020-09-20

4. ProPheno 1.0: An Online Dataset for Accelerating the Complete Characterization of the Human Protein-Phenotype Landscape in Biomedical Literature;2020 IEEE 14th International Conference on Semantic Computing (ICSC);2020-02

5. PPPred;Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics;2019-09-04

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