Pavel Skums

The paper identifies distinct intrinsic sequence patterns between HIV-1 DNA and RNA across subtypes using a k-mer-based approach and presents a new tool for genomic analysis.

This paper infers intra-tumor selection coefficients from clonal CNA profiles using a simulation-based Bayesian framework and deep learning, achieving better performance than baseline methods.

The paper develops a computational framework to detect emerging viral variants with altered phenotypes by analyzing epistatic networks, achieving early detection of known SARS-CoV-2 strains.

SOPHIE reconstructs viral transmission networks using a maximum likelihood framework integrating phylogenetic and random graph models, without requiring case-specific epidemiological data.

This paper introduces PYCIVO, a framework to identify primary cases in viral outbreaks by analyzing intra-host viral variant populations, achieving 100% accuracy in some scenarios.

This paper clusters SARS-CoV-2 sequences to identify variants and subtypes, showing improved performance over existing methods and detecting rare variants in large datasets.

This paper introduces CliqueSNV, a method to accurately identify minority viral haplotypes from NGS data by reducing sequencing noise, achieving detection at frequencies as low as 0.1%.

The paper compares intra-host HCV population characteristics between recent and persistent infections, finding significant differences and developing a machine learning classifier with 95.22% accuracy.

The paper normalizes heterogeneous genomic data into images and applies machine learning to classify viral infection stages and detect transmission clusters with high accuracy.

This paper introduces a method using k-mer distribution in RNA-seq reads to estimate genetic relatedness between viral samples and infer transmission clusters without requiring full RNA-seq assembly.

This study benchmarks error-correction methods for next-generation sequencing data, finding significant variation in performance across datasets with no single method being optimal overall.

This paper introduces SCIFIL, a tool to infer fitness landscapes of cancer clones from single-cell sequencing data, demonstrating its accuracy on simulated and experimental data.

The paper presents a fast and scalable k-mer-based framework for efficiently extracting genetically similar sequences from large NGS datasets, targeting heterogeneous populations like viruses.