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Review
. 2024 Jan 25:14:1258083.
doi: 10.3389/fgene.2023.1258083. eCollection 2023.

Artificial intelligence and database for NGS-based diagnosis in rare disease

Yee Wen Choon  1   2 , Yee Fan Choon  3 , Nurul Athirah Nasarudin  4 , Fatma Al Jasmi  4 , Muhamad Akmal Remli  1   2 , Mohammed Hassan Alkayali  5 , Mohd Saberi Mohamad  4
Affiliations
Review

Artificial intelligence and database for NGS-based diagnosis in rare disease

Yee Wen Choon et al. Front Genet. .

Abstract

Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.

Keywords: artificial intelligence; data science; diagnosis; machine learning; next-generation sequencing; rare disease.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The use of sequencing in precision medicine.
FIGURE 2
FIGURE 2
A framework of WES/WGS data analysis from individual patients with rare diseases. (GATK-Genome Analysis Toolkit, VQSR-Variant Quality Score Recalibration, BWA-Burrows-Wheeler Alignment, SAMtools-Sequence Alignment/Map tools, VEP-Ensembl Variant Effect Predictor, HGC-Hierarchical Graph-based Clustering, GTEx-Genotype Tissue Expression, IGV-Integrative Genomics Viewer).
FIGURE 3
FIGURE 3
General workflow for NGS data analysis.
See this image and copyright information in PMC

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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was sponsored by the ASPIRE, the technology program management pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), via the ASPIRE Precision Medicine Research Institute Abu Dhabi (ASPIREPMRIAD) award grant number VRI-20‐10. The United Arab Emirates University also supported this work through the Research Start‐up Program (Grant # 12M109).

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