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Collection and Extraction of Saliva DNA for Next Generation Sequencing
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DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets,
Muhammad Nabeel Asim1,2, Muhammad Ali Ibrahim1,3, Arooj Zaib3
1German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany.
Frontiers in Medicine
|April 23, 2025
Summary
This paper bridges artificial intelligence (AI) and deoxyribonucleic acid (DNA) sequence analysis, detailing 44 tasks and AI methods. It provides resources for integrating AI into genomics for faster, more accurate genetic insights.
Area of Science:
- Genomics and Bioinformatics
- Artificial Intelligence in Biology
- Computational Biology
Background:
- Deoxyribonucleic acid (DNA) is the genetic blueprint, with mutations leading to hereditary conditions and cancer.
- Traditional DNA sequence analysis is labor-intensive, costly, and error-prone.
- Artificial intelligence (AI) offers potential to accelerate DNA sequence analysis and interpretation.
Purpose of the Study:
- To bridge the gap between AI researchers and biologists in DNA sequence analysis.
- To provide a comprehensive overview of DNA sequence analysis tasks and AI methodologies.
- To facilitate the integration of AI into genomic data analysis.
Main Methods:
- Cataloging 44 distinct DNA sequence analysis tasks and aligning them with AI paradigms (classification, regression, clustering).
- Consolidating information on 36 biological databases for benchmark dataset creation.
- Surveying 39 word embeddings and 67 language models for predictive pipelines.
Main Results:
- Detailed insights into 140 benchmark datasets for 44 DNA sequence analysis tasks.
- Performance values for AI predictors, including word embeddings and language models.
- Comparison with top-performing traditional sequence encoding-based predictors.
Conclusions:
- AI significantly enhances the efficiency and accuracy of DNA sequence analysis.
- This work provides essential resources for developing and integrating AI tools in genomics.
- Effective AI-genomics integration requires interdisciplinary understanding and accessible benchmarks.
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