Machine learning (ML) and next-generation sequencing (NGS) have significantly transformed genomics, offering valuable tools for understanding the structure, function, and evolution of genomes, as well as advancing disease diagnosis and treatment. ML, a subset of artificial intelligence, facilitates computers in learning from data and making predictions without explicit programming, while NGS is a high-throughput technique capable of rapidly generating massive genomic datasets at a low cost.

Several applications demonstrate the synergy between ML and NGS in genomics:

Base-calling Improvement between Machine Learning and NGS:

ML algorithms enhance the accuracy and speed of base-calling, converting raw signals from NGS into nucleotide sequences by learning patterns and mitigating noise in the data.

Gene Expression Analysis:

ML methods aid in identifying differentially expressed genes, clustering similar samples, and inferring gene regulatory networks, providing insights into gene regulation across diverse cells, tissues, and conditions.

Variant Detection and Annotation:

ML techniques assist in detecting and annotating genetic variations, including SNPs, insertions, deletions, and structural variants, prioritizing relevant variants for further investigation.

Epigenomics Exploration:

ML approaches analyze NGS data from epigenomic assays, such as DNA methylation, histone modifications, and chromatin accessibility, revealing the epigenetic landscape of different cell types and diseases.

While these applications showcase the potential of ML and NGS, challenges and limitations must be addressed:

Data Quality and Quantity:

NGS data can be noisy, incomplete, and biased, impacting ML model performance. Proper data preprocessing, quality control, and normalization are essential to overcome these challenges.

Model Selection and Evaluation:

Choosing the most suitable ML algorithm for specific tasks is challenging. Rigorous model selection, optimization, and comparison are necessary to ensure robust and generalizable results.

Interpretability and Explainability:

ML models can be complex and opaque, making it difficult to understand their decision-making processes. Enhancing interpretability and explainability is crucial for gaining biological insights and avoiding spurious associations.

In summary, the collaboration between ML and NGS provides powerful tools for genomic research and applications. However, careful consideration of data quality, model selection, and interpretability is crucial for ensuring the reliability and validity of results. The field continues to evolve, with ongoing efforts to address these challenges and unlock the full potential of ML and NGS in genomics.