Fabio Cunial

The paper analyzes long-read sequencing data from the All of Us Research Program to identify structural variants and their associations with diseases in a large cohort of Black or African American participants.

This paper introduces a method called kanpig for accurately genotyping structural variants using long-read alignment pileups, achieving higher concordance than existing tools.

blend-seq improves variant discovery by combining short and low-coverage long reads, enhancing SNP detection and structural variant recall while maintaining precision.

The paper identifies a common flanking variant linked to increased meiotic stability at the FGF14-SCA27B repeat locus.

This paper introduces a deep learning framework called Cue for structural variant discovery and genotyping, showing improved performance over existing methods on synthetic and real data.

The paper develops faster and more memory-efficient tools for computing and analyzing matching statistics between large genomes, enabling practical applications in comparative genomics.

The paper introduces a space-efficient framework for variable-order Markov models, reducing memory usage by up to 100 times compared to previous methods.