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A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.

Yuhua Fu1,2, Jingya Xu1, Zhenshuang Tang1

  • 1Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, 430070, Wuhan, Hubei, P.R. China.

Communications Biology
|September 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for integrating multi-omics data to identify candidate genes in livestock like pigs. The developed swine knowledgebase (ISwine) aids in prioritizing genes for experimental validation.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Multi-omics data analysis is crucial for identifying candidate genes related to complex traits.
  • Integrating multi-omics data, especially in non-model organisms, presents significant challenges for gene prioritization.
  • Effective candidate gene prioritization is essential for guiding experimental validation and advancing biological research.

Purpose of the Study:

  • To develop a general convolutional neural network model for integrating multi-omics information to prioritize candidate genes.
  • To apply the model to Sus scrofa (swine), a vital non-model livestock species.
  • To create an accessible online knowledgebase (ISwine) for swine multi-omics data.

Main Methods:

  • Development of a general convolutional neural network (CNN) model for multi-omics data integration.
  • Application of the CNN model to prioritize candidate genes in Sus scrofa.
  • Compilation and integration of published swine multi-omics data into the ISwine knowledgebase.

Main Results:

  • The CNN model achieved high prediction performance in Sus scrofa with 72.9% precision, 73.5% recall, and 73.4% F1-Measure.
  • The model's performance demonstrates a significant improvement compared to previous studies in model organisms like Arabidopsis thaliana and Oryza sativa.
  • The ISwine knowledgebase (http://iswine.iomics.pro/) was established, consolidating extensive swine multi-omics data.

Conclusions:

  • Deep learning strategies offer a powerful approach for effective multi-omics data integration.
  • The developed model and ISwine knowledgebase facilitate the prioritization of candidate genes in non-model organisms.
  • This work will significantly advance future multi-omics integration analyses in livestock and other species.