Biology subjects

Takacs, B.

Publications and source records attributed to Takacs, B..

3 recordsLinked to original sources

bioRxiv2026

DeltaMut: An Integrative Database of AlphaFold2-Derived Missense Variant Structures

The widespread use of next-generation sequencing has led to a surge in the number of identified variants with uncertain effects on protein function. These variants pose a significant challenge in diagnostics and hinder patient treatment strategies. Numerous variant effect predictors (VEPs) are available to assess variant impact, but they primarily rely on sequence-derived information. The recent development of AlphaFold2 has raised questions about whether information retrieved from wild-type or predicted structures of missense variants can improve the predictive power of these algorithms. While the AlphaFold Protein Structure Database serves as a valuable resource for wild-type protein structures, a large-scale collection of missense variant structures is not available, limiting current efforts to wild-type conformations and a handful of modeled variants. To address this limitation, we developed DeltaMut, a comprehensive database containing over 77,000 protein structures, including 65,000 pathogenic and neutral missense variants. All structural models were generated using ParaFold, a high-performance computing-optimized implementation of AlphaFold2. The large-scale and systematic generation of variant protein structures distinguish DeltaMut as a unique resource for both expansive statistical studies and detailed, case-specific investigations of variant-induced structural changes. Furthermore, the DeltaMut database is freely accessible without registration. HighlightsO_LIDeltaMut is currently the largest database of AlphaFold2-predicted variant structures. C_LIO_LIContains 77,713 structures covering 12,101 wild-type and 65,612 variant proteins. C_LIO_LI70.6% of predicted structures have high or very high confidence (pLDDT [≥] 70). C_LIO_LIFreely accessible web server with visualization and download of variant models. C_LI

bioinformatics↗

bioRxiv2026

Assessing the impact of parental linear gene normalization on the performance of statistical models for circular RNA differential expression analysis

BackgroundCircular RNAs (circRNAs) emerged as promising non-invasive cancer biomarkers due to their stability, abundance in body fluids, and regulatory potential. However, circRNA differential expression analysis (DEA) remains challenging, largely owing to lack of consensus on important preprocessing strategies such as filtering and normalization. While well-established bulk RNA-sequencing frameworks are commonly applied to circRNA data, newer approaches such as CIRI-DE (part of CIRI3 suite) integrate both linear and circular transcript information to improve detection. Despite developments, an assessment of these integrative strategies is lacking, and the critical impact of filtering on DEA model performance has not been comprehensively evaluated. ResultsIn this study, we evaluated the impact of multiple normalization and filtering strategies on circRNA DEA using five experimental datasets, including two in-house blood platelet sets and semi-parametric simulated in silico datasets. Our results emphasize the importance of selecting an appropriate filtering threshold, as overly lenient filtering substantially reduced model performance across datasets. We found edgeRs filterByExpr() strategy particularly effective in handling zero counts in circRNA data, while also generating the most reliable results across most datasets. Furthermore, by incorporating linear and circular information as described in CIRI-DE, most methods identified a higher number of differentially expressed (DE) circRNAs compared to circular counts alone. Notably, circRNAs identified by both CIRI-DE and the modified bulk RNA-sequencing pipelines showed substantial overlap. ConclusionOur findings demonstrate that automated filtering combined with linear-aware normalization significantly enhances the sensitivity and reproducibility of circRNA DEA, providing a standardized framework for more reliable biomarker discovery in transcriptomic research.

bioinformatics↗

bioRxiv2023

The influence of the way of regression on the results obtained by the receptorial responsiveness method (RRM), a procedure to estimate a change in the concentration of a pharmacological agonist near the receptor

The receptorial responsiveness method (RRM) enables the estimation of a change in the concentration of a degradable agonist, near its receptor, by fitting its model to (at least) two concentration-effect (E/c) curves of a stable agonist of this receptor. One curve should be generated before this change in concentration, while the other one after this change, in the same (or in identical) biological system(s). It follows that RRM yields a surrogate parameter ("cx"), the concentration of the stable agonist that is equieffective with the change in the concentration of the degradable agonist. However, the curve fitting can be implemented several ways, which can affect accuracy, precision and convenience of use. This study utilized data of previous ex vivo investigations. Known concentrations of stable agonists were estimated with RRM by performing individual (local) or global fitting (with one or two model(s)), combined with the use of a logarithmic (logcx) or non-logarithmic parameter (cx), and with ordinary least-squares or robust regression. We found that the individual regression, the most complicated option, was the most accurate, followed closely by the moderately complicated two-model global regression and then by the easy-to-perform one-model global regression. The two-model global fitting was the most precise, followed by the individual fitting (closely) and by the one-model global fitting (from afar). The use of cx and robust regression did not, whereas pairwise fitting (i.e. fitting only two E/c curves at once) did improve the quality of estimation. Thus, the two-model global fitting, performed pairwise, is recommended for RRM, but the individual fitting is a good alternative.

pharmacology and toxicology↗