de la Cruz, X. | bioRxiv Science

bioRxiv2024

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfa-tase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among sub-missions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

genetics↗

bioRxiv2022

Choosing variant interpretation tools for clinical applications: context matters

Our inability to solve the Variant Interpretation Problem (VIP) has become a bottleneck in the biomedical/clinical application of Next-Generation Sequencing. This situation has favored the development and use of bioinformatics tools for the VIP. However, choosing the optimal tool for our purposes is difficult because of the high variability of clinical contexts across and within countries. Here, we introduce the use of cost models as a new approach to compare pathogenicity predictors that considers clinical context. An interesting feature of this approach, absent in standard performance measures, is that it treats pathogenicity predictors as rejection classifiers. These classifiers, commonly found in machine learning applications to healthcare, reject low-confidence predictions. Finally, to explore whether context has any impact on predictor selection, we have developed a computational procedure that solves the problem of comparing an arbitrary number of tools across all possible clinical scenarios. We illustrate our approach using a set of seventeen pathogenicity predictors for missense variants. Our results show that there is no optimal predictor for all possible clinical scenarios. We also find that considering rejection gives a view of classifiers contrasting with that of standard performance measures. The Python code for comparing pathogenicity predictors across the clinical space using cost models is available to any interested user at: https://github.com/ClinicalTranslationalBioinformatics/clinical_space_partition SummariesJosu Aguirre earned his doctorate at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Natalia Padilla earned is a post-doctoral researcher at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Selen Ozkan is a Ph.D. student at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Casandra Riera earned her doctorate at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Lidia Feliubadalo earned her doctorate at the Universitat de Barcelona, presently she is a high-level technician working at the Catalan Institute of Oncology (ICO) in the diagnosis of hereditary cancers. Xavier de la Cruz is ICREA Research Professor at the Vall dHebron Institute of Research (VHIR). His research interests revolve around the application of machine learning methods to healthcare problems.

bioinformatics↗

bioRxiv2020

New genes involved in Angelman syndrome-like: expanding the genetic spectrum

Angelman syndrome (AS) is a neurogenetic disorder characterized by severe developmental delay with absence of speech, happy disposition, frequent laughter, hyperactivity, stereotypies, ataxia and seizures with specific EEG abnormalities. There is a 10-15% of patients with an AS phenotype whose genetic cause remains unknown (Angelman-like syndrome, AS-like). Whole-exome sequencing (WES) was performed on a cohort of 14 patients with clinical features of AS and no molecular diagnosis. As a result, we identified 10 de novo and 1 X-linked pathogenic/likely pathogenic variants in 10 neurodevelopmental genes (SYNGAP1, VAMP2, TBL1XR1, ASXL3, SATB2, SMARCE1, SPTAN1, KCNQ3, SLC6A1 and LAS1L) and one deleterious de novo variant in a candidate gene (HSF2). Our results highlight the wide genetic heterogeneity in AS-like patients and expands the differential diagnosis. New AS-like genes do not interact directly with UBE3A gene product but are involved in synapsis and neuron system development.

genetics↗