From tradition to innovation: conventional and deep learning frameworks in genome annotation

文献类型： 外文期刊

第一作者： Chen, Zhaojia

作者： Chen, Zhaojia;ul Ain, Noor;Zhang, Xingtan;Chen, Zhaojia;Zhao, Qian;Zhao, Qian

作者机构：

关键词： genome annotation; genome sequence; bioinformatic; RNA-Seq technology; deep learning

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：6.8； 五年影响因子：7.9 ）

ISSN： 1467-5463

年卷期： 2024 年 25 卷 3 期

页码：

收录情况： SCI

摘要： Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.

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