Satoshi Oota
After properly exclusion of exogenous etiologies, monogenic causes accounted for nearly half of the cases of rhabdomyolysis. Genetic testing should be pursued in those patients with persistent CK elevation, presence of myalgia, objective weakness or parental consanguinity.
Advances in artificial intelligence (AI) and machine learning (ML) have rapidly transformed bioinformatics, offering new solutions for data-intensive challenges in genomics. In this chapter, we introduce a practical protocol that contrasts a conventional variant-calling approach with a modern AI/ML-based method using single-cell RNA sequencing (scRNA-seq) data. Focusing on somatic variant detection in a normal human tissue, we explore the characteristics of variant calls-including variant types, read depth, and variant allele frequency (VAF)-and address technical challenges such as alignment artifacts near splice junctions and noise in RNA-seq data. We demonstrate that AI-based methods, such as DeepVariant, provide enhanced accuracy and confidence in genotype prediction compared to rule-based tools such as RNA-Mutect2. By outlining comparative workflows and analysis outputs, this chapter highlights the growing potential of AI/ML in improving variant discovery and interpretation in transcriptomic data, particularly in contexts lacking matched-normal controls.