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IAnimal: a cross-species omics knowledgebase for animals.

Yuhua Fu1,2, Hong Liu1, Jingwen Dou1

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IAnimal is a large-scale, cross-species knowledgebase integrating multi-omics data for 21 species. It uses deep learning to mine gene-trait relationships, aiding biological system analysis.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Systems Biology

Background:

  • Exponential growth in multi-omics data presents challenges and opportunities for biological interpretation.
  • Integrating diverse omics data is crucial for understanding gene regulation and biological systems.

Purpose of the Study:

  • To develop IAnimal, a cross-species, multi-omics knowledgebase, to enhance data utilization and simplify multi-omics integration.
  • To mine genetic mechanisms underlying objective traits by leveraging integrated omics information.
  • To build a deep learning model for extracting gene and trait entities from scientific literature.

Main Methods:

  • Curated and integrated 61,191 omics datasets (genome, transcriptome, epigenome) for 21 species.
  • Developed a deep learning model using BioBERT and AutoNER to mine gene and trait entities from 2,794,237 abstracts.
  • Provided user-friendly web interfaces, APIs, and functional modules for data querying and visualization.

Main Results:

  • IAnimal hosts 846.46 TB of clean data across multiple omics layers for diverse species.
  • The deep learning model effectively identifies gene and trait relationships from literature.
  • Users can easily query, mine, and visualize omics data to understand gene regulation.

Conclusions:

  • IAnimal facilitates the comprehensive analysis of biological systems by integrating massive multi-omics data.
  • The knowledgebase and associated deep learning model provide valuable insights into gene regulation and trait inheritance.
  • IAnimal empowers researchers to explore complex biological mechanisms through accessible multi-omics data exploration.