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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
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Artificial intelligence and database for NGS-based diagnosis in rare disease
Yee Wen Choon1,2, Yee Fan Choon3, Nurul Athirah Nasarudin4
1Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, Malaysia.
Frontiers in Genetics
|February 19, 2024
Summary
Artificial intelligence (AI) enhances rare disease (RD) diagnosis by improving Next-Generation Sequencing (NGS) data analysis. This review explores AI
Area of Science:
- Genomics
- Bioinformatics
- Artificial Intelligence
Background:
- Rare diseases (RDs) affect 300 million globally, presenting complex genetic challenges.
- Next-Generation Sequencing (NGS) has advanced RD genetic heterogeneity research and diagnosis.
- Bioinformatics tools are crucial for analyzing large NGS datasets in RDs.
Purpose of the Study:
- To review the current applications of artificial intelligence (AI) in Next-Generation Sequencing (NGS) for rare disease (RD) research.
- To discuss the future directions and challenges of AI in NGS-based genetics.
- To compare various rare disease databases.
Main Methods:
- Review of current literature on AI applications in NGS for RDs.
- Analysis of AI's role in variant calling, prediction, and electronic health record (EHR) systems.
- Comparative assessment of rare disease databases.
Main Results:
- AI, particularly deep learning, shows significant promise in improving variant calling precision and prediction accuracy in NGS data.
- AI enhances the usability of electronic health record (EHR) systems for NGS-based diagnostics.
- Concerns exist regarding method consistency, data formats, accuracy measures, and database update regularity in current bioinformatics tools.
Conclusions:
- AI is transforming NGS-based rare disease research and diagnostics.
- Addressing challenges in standardization and data management is crucial for AI's continued success in RDs.
- Further research into AI algorithms and database integration is needed to fully realize the potential of NGS in understanding rare genetic diseases.
Related Concept Videos
Next-generation Sequencing
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Single Nucleotide Polymorphisms-SNPs
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

