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Annotating regulatory elements by heterogeneous network embedding.
Yurun Lu1,2, Zhanying Feng1,2, Songmao Zhang1
1CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
We developed RE-GOA, a novel method using natural language processing to annotate regulatory elements (REs) and their functions. This approach improves upon existing methods for gene regulation analysis.
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Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Regulatory elements (REs) control gene expression through complex networks.
- Current methods for RE annotation are costly and labor-intensive.
Purpose of the Study:
- To develop a systematic Gene Ontology Annotation method for Regulatory Elements (RE-GOA).
- To leverage natural language processing and network embedding for RE annotation.
Main Methods:
- Assembled a heterogeneous network integrating regulation, protein-protein interactions, and Gene Ontology (GO) terms.
- Performed network embedding to associate REs with GO terms in a low-dimensional vector space.
Main Results:
- RE-GOA outperforms existing methods in annotating transcription factor binding sites (ChIP-seq).
- Demonstrated effectiveness in functional enrichment analysis of ATAC-seq data.
- Showcased utility in revealing genetic correlations from GWAS summary statistics.
Conclusions:
- RE-GOA offers a powerful and efficient approach for regulatory element annotation.
- The method advances understanding of gene regulation and genetic associations.