Biology subjects

Gudkov, M.

Publications and source records attributed to Gudkov, M..

3 recordsLinked to original sources

bioRxiv2026

SVlog: a logic programming framework for understanding structural variation in genomic disease

Structural variants (SVs) are a diverse group of genetic variants defined by a minimum size of 50 base pairs. SVs account for the majority of all variant bases in a persons genome and are commonly implicated in inherited disease and cancer. However, SV analysis is complex due to their wide variation in type and size, degree of polymorphism, involvement of repetitive sequences, and the myriad ways they may elicit a functional impact, as well as technical factors like imprecise breakpoint detection, and alternative representations of the same event. Despite recent advances in the detection and characterisation of SVs, it remains difficult to assess them beyond basic annotations and comparisons. Here we introduce SVlog, a transparent and extensible meta-programming framework for SV analysis. With the logic programming language Souffle as its engine, SVlog provides a declarative ontology describing relationships among SVs, genes and other genomic elements. Genome annotations and SV datasets - both user-provided and public reference data - are converted into relational facts, to which SVlog applies logical rules that define predicates. Predicates are specific, transparent and deterministic, yet fully flexible and composable, enabling detailed evaluation of SVs without relying on stochastic "black box" approaches. To showcase SVlog, we have developed a ready-made predicate library for SV annotation, comparison and prioritisation in the context of rare inherited disease. Despite its compact codebase, SVlog evaluates more than 50 input predicates to generate over 70 informative output predicates. It synthesises evidence from population and clinical genomic databases, and applies a tiered filtering strategy to identify candidate pathogenic SVs in patients with inherited disease. By focusing on explainability and modularity, SVlog offers a fast, reliable library for SV analysis and is a powerful deterministic alternative to traditional bioinformatics pipelines for clinical variant curation.

bioinformatics↗

bioRxiv2025

Benchmarking of variant pathogenicity prediction methods using a population genetics approach

MotivationVariant pathogenicity predictors are essential for identifying new associations between genetic variants and rare diseases. However, despite the availability of numerous predictors, there is no clear consensus on which methods provide the most reliable results. The common practice of training, testing, and benchmarking these predictors using known variant sets from disease or mutagenesis studies raises concerns about ascertainment bias and data circularity. ResultsWe benchmarked commonly used pathogenicity predictors using an orthogonal approach that does not rely on predefined "ground truth" datasets. By leveraging population-level genomic data from gnomAD and the Context-Adjusted Proportion of Singletons (CAPS) metric, we identified CADD and REVEL as the best-performing predictors for distinguishing extremely deleterious variants from moderately deleterious ones. REVEL demonstrated superior calibration. Additionally, we show that CAPS can serve as a meta-analysis tool for interpreting variant annotations and highlight biases in ClinVar-based predictor training. Availability and ImplementationCAPS analysis and benchmarking results are available at https://github.com/mgudVCCRI/PopGenVariantFiltering Contacte.giannoulatou@victorchang.edu.au

genomics↗

bioRxiv2024

Improving estimates of negative selection in human genome using CAPS

Despite ongoing efforts, variant interpretation in disease sequencing studies is often hindered by the lack of well-established ways of determining the potential pathogenicity of genetic variation, especially for understudied classes of single-nucleotide variants (SNVs). Population genetics methods offer an attractive solution to this problem by enabling the assessment of the effects of SNVs through their distributions in human populations. For instance, negative selection is known to shift site-frequency spectra of genetic variation, thus affecting the ratio of singleton variants. It has been shown that the extent of negative selection can serve as a proxy for deleteriousness. An example of this approach is the Mutability-Adjusted Proportion of Singletons (MAPS) metric. Although MAPS proves a useful instrument for the assessment of selection-based deleteriousness in SNVs, it is highly sensitive to the calibration of the singletons-by-mutability model, which results in potentially biased estimates for some classes of variants. Building up on the methodology used in MAPS, we developed a novel metric of negative selection in the human genome -- CAPS, or Context-Adjusted Proportion of Singletons. Compared to its predecessor, CAPS provides estimates of negative selection that are less biased and have more accurate confidence intervals. CAPS inherits some of the same features that make MAPS useful for studying SNVs, yet the key difference of our method is the complete elimination of the mutability layer in the model, which makes the metric more robust and reliable. We believe that CAPS holds promise for improving the discovery of new disease-variant associations in clinical and research settings.

genomics↗