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From tradition to innovation: conventional and deep learning frameworks in genome annotation
Zhaojia Chen1,2, Noor Ul Ain1, Qian Zhao3
1National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou 518120, China.
Briefings in Bioinformatics
|April 6, 2024
Summary
The Encyclopedia of DNA Elements (ENCODE) project uses advanced sequencing and machine learning to understand genome function. This review covers traditional and deep learning methods for gene annotation, highlighting future challenges.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- The Human Genome Project's success led to the ENCODE initiative (2003) to identify functional genomic elements.
- Advancements in sequencing technologies (ChIP-Seq, RNA-Seq) generated massive datasets.
- Extracting biological insights from genomic data is crucial for gene function prediction.
Conclusions:
- Deep learning approaches show significant potential for advancing gene structure prediction and functional annotation.
- Continued development of computational methods is essential for interpreting complex genomic data.
- The field of genome annotation is rapidly evolving, requiring novel strategies to address emerging challenges.
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