Victoria Popic

This paper introduces a high-throughput single-cell RNA isoform sequencing method using sequencing-by-expansion (SBX) and the CUDLL algorithm, achieving high accuracy and enabling isoform-level analysis across cell types.

This study integrates long-read WGS and single-nucleus RNA-seq to identify structural variants affecting gene expression in Parkinson's disease with cell type resolution.

The paper introduces LRAA, a framework for accurate isoform discovery and quantification in long-read RNA-seq data, outperforming existing methods across multiple benchmarks.

This paper introduces a hybrid training model to enhance bioinformatics capacity globally, demonstrating its effectiveness through an Eastern European workshop series.

This study shows that whole-genome sequencing can replace chromosomal microarray and Fragile X testing with higher accuracy and broader coverage for genetic disorders.

The paper evaluates and optimizes single-cell RNA isoform sequencing by analyzing artifacts from three platforms and introducing methods to reduce their impact.

The paper creates a database of structural variants in 40 inbred mouse strains and identifies an SV linked to lymphoma susceptibility in SJL mice.

ralphi uses deep reinforcement learning to improve haplotype assembly, achieving lower error rates than existing methods on standard benchmarks.

SF-Relate identifies genetic relatives across distributed genomic datasets using a privacy-preserving federated approach with high accuracy and efficiency.

CTAT-LR-fusion detects fusion transcripts from long and short read RNA-seq data, improving accuracy in bulk and single cell transcriptomes.

This paper introduces MAS-ISO-seq, a method to boost throughput of long-read RNA sequencing for isoform discovery, showing improved detection of differentially spliced genes and cell clustering in T cells.

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.

GATTACA bins metagenomic contigs into species-specific clusters using compact k-mer indexing and MinHash, achieving accuracy comparable to existing methods but with significant speed improvements.