Structural variants are a common cause of human diseases, but their detection from short-read sequencing remains challenging, despite being the technology underlying most clinical workflows. We present dicast, a machine-learning method that scores SV calls from short-read data using alignment and genomic-context features. dicast is trained on a new multi-technology ground truth built from nine samples, with extensive manual curation. It outperforms existing short-read callers and consensus approaches, recovering substantially more true positives at high precision. We also demonstrate dicast’s applicability for diagnostics, identifying all pathogenic variants in multiple disease cohorts, and 20% more candidate pathogenic deletions than consensus approaches.
Germline copy number variants (gCNVs) are vital for diagnosing rare monogenic disorders. Here, we present a containerized, agent-assisted Snakemake protocol for detecting, annotating, and visualizing gCNVs from short-read whole-exome sequencing data. We describe steps for constructing baseline models, executing CNV cal...
ContextSV, a framework that integrates alignment evidence with copy number predictions derived from sequencing coverage and single-nucleotide variant allele frequencies to improve SV detection, serves as a valuable complement to existing long-read SV detection approaches by improving sensitivity for large and clinicall...
J. E. Perdomo, Mian Umair Ahsan, Jasmine Akoto et al.· NAR Genomics and Bioinformat...· 0 citations
High-throughput genomic sequencing generates vast amounts of data, yet the interpretation of individual-genetic variants remains hindered by the dispersion of relevant evidence across various databases. We present a modular, web-based framework (GenomeVAP) designed for deterministic evidence integration in genomic rese...
Anusha Sunder· Current Trends in Biomedical...· 0 citations
Rapid medical triage following ionizing radiation exposure is critical for emergency management, yet traditional alignment-based bioinformatics are too computationally intensive for mass-casualty scenarios. To address this, we developed STUART (Sequence Triage and qUAntification of Read Transcripts), a mapping-free mac...
Tomasz Strzoda, Lourdes Cruz-Garcia, Mustafa Najim et al.· 0 citations
A protocol for using cuteHap, a robust framework for haplotype-aware SV detection through phased alignment reads generated by diverse long-read sequencing platforms, and procedures for single-nucleotide variant (SNV) calling, read phasing, SV calling, and genotyping are described.
Shu-Qi Cao, Chuanmin Wu, Yue-Jin He et al.· STAR Protocols· 0 citations
This chapter introduces 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, and demonstrates that AI-based methods, such as DeepVariant, provide enhanced accuracy and confidence in genotype prediction compared to...
Satoshi Oota· Methods in molecular biology· 0 citations
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