bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.07.08.602580

Assessment of variant effect predictors unveils variants difficulty as a critical performance indicator

Radjasandirane, R.·Diharce, J.·Gelly, J.-C.·de Bervern, A. G.
View original source ↗Export metadata ↓

Abstract

Amino acid substitutions in protein sequences are generally harmless, but a certain number of these changes can lead to disease. Accurate prediction of the impact of genetic variants is crucial for clinicians as it accelerates the diagnosis of patients with missense variants associated with health issues. Numerous computational tools have been developed for prediction of the pathogenicity of genetic variants based on different methodologies. Nowadays, many approaches are based on Machine Learning. Assessment of the performance of these diverse computational tools is crucial to provide guidance to both future users and especially clinicians. In this study, a large-scale study of 65 tools was conducted. Variants from both clinical and functional context have been used, incorporating data from the ClinVar database and bibliographic sources. The analysis showed that AlphaMissense is often performing very well and is actually the best option among existing tools. Additionally, meta-predictors, as expected, are of high quality and perform well on average. Tools using evolution information demonstrated highest performances on functional variants. These results also highlighted some variations in the difficulty to predict some specific variants while others are always well categorized. Strikingly, the majority of variants from the ClinVar database appear to be easy to predict, while variants from other sources of data are more challenging. These results demonstrate that this variant predictability can be classified into three distinct classes: easy, moderate and hard to predict. We analyzed the parameters leading to these differences and show that classes are linked to structural and functional information.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Radjasandirane, R., Diharce, J., Gelly, J.-C., de Bervern, A. G.. 2024-07-09. Assessment of variant effect predictors unveils variants difficulty as a critical performance indicator. https://doi.org/10.1101/2024.07.08.602580

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

deepTools4: Faster, Scalable, and Modernized Genomic Data Analysis

The deepTools suite is widely used for quality control, summarization, and visualization of high-throughput sequencing data. However, as genomic datasets grow in scale and depth, existing tools often become computational bottlenecks. We present deepTools4, a modernized framework designed for high performance, scalability, and enhanced usability, while maintaining seamless integration with the Galaxy platform. Core functionality has been rewritten and thoroughly benchmarked, achieving substantial computational speed-ups. Visualization tools have been modernized to provide more intuitive, high-quality graphical outputs. In addition, our testing framework has been expanded to facilitate continuous development and ensure robustness. We demonstrate the utility of deepTools4 across diverse epigenomics and transcriptomics datasets, highlighting its ability to summarize genome-wide signals, apply a wide range of common normalization methods, and visualize enrichment patterns. The enhanced deepTools4 framework delivers a faster, more scalable solution for reproducible genome-wide analysis and interpretation across many sequencing modalities. Availability: https://github.com/deeptools/deepTools/

bioinformatics↗
bioRxiv2026

Reproducibility and cross-dataset concordance of drug-induced transcriptional responses

Drug-perturbation transcriptomic resources are expanding, but the reproducibility and cross-source consistency of the responses they measure remain critical to their use. We compared six resources using independent replicates, matched-drug analyses, and response decomposition. High agreement in treated expression did not translate into equally repeatable drug-induced changes. Cross-source response concordance was generally limited, including under matched cell and treatment conditions, although some comparisons retained drug-specific information. In Tahoe, a control-expression-only baseline left substantial drug-specific variation unresolved. Measured drug main effects - the average responses across cell lines - provided information for predicting cell-specific interactions, with this relationship retained in a cross-plate validation. Exploratory analyses of structure, target, and external-response representations identified modest signals in selected pipelines. Together, these analyses characterize the reliability and transferability of current perturbation resources and identify prediction of drug main effects as a key bottleneck in the framework examined, motivating shared reference measurements and better recovery of drug-specific responses.

bioinformatics↗
bioRxiv2026

Propensity-Adjusted AUC under Nuisance-Model Misspecification

Propensity adjustment compares medical-image detector scores within groups with similar measured properties, such as stain or focus. We examine its sensitivity to the nuisance model that forms those groups. Under Gaussian latent-index assumptions and exact linear-index strata, a closed-form correlation gives an exact boundary for adjusted AUC to fall below one half. Quadrature matches 36 simulation cells within 0.008. Inflation and attenuation relative to correct adjustment are empirical findings, not general ordering results. An overlap screen and a self-stratified control introduced for this study miss 140 of 141 simulated cells with absolute error above 0.05: checking the fitted propensity cannot certify completeness of the adjustment set. On real pathology corpora, logistic and boosted-tree models using the same 16 features disagree. Among splits where both pass both checks, mean adjusted-AUC gaps are 0.057 on acid-fast bacilli (AFB), 0.048 on Kaggle, and 0.049 on PCam, over three, five, and three splits respectively. These gaps measure model sensitivity; neither estimate is known to be correct. A controlled colour shift moves adjusted AUC relative to model-specific references in the predicted direction in 78 of 80 label-dependent cells. Broader balance and label-fit diagnostics favour trees without certifying their adjusted AUC. We recommend reporting sensitivity across nuisance models alongside these diagnostics. Reversal relative to known truth occurs in constructed stress tests; its frequency in practice remains unknown.

bioinformatics↗