Biology subjects

Raimondi, D.

Publications and source records attributed to Raimondi, D..

9 recordsLinked to original sources

bioRxiv2026

pyreml: a general-purpose, GPU-accelerated mixed model solver in Python

Linear mixed models (LMM) are a popular modeling and inference framework, widely used in bioinformatics and other scientific fields. To obtain an unbiased estimation of the variance components, an LMM has to be fitted using restricted maximum likelihood (REML), a computationally intensive step. Most REML software is closed source, and currently there is no Python library offering complete LMM functionalities, such as the specification of spatial and genomic variance structures, multivariate analyses, random regression and factors, and heteroscedastic residuals. Here we propose pyreml, an LMM solver that fills this gap, comes with a user-centered interface and speeds up calculations by running on GPU. We show that pyreml fits a genomic best linear unbiased prediction (GBLUP) model more than one order of magnitude (>12\times) faster than the current state-of-the-art, closed source REML software. We also show that \pyreml can estimate a latent factor-analytic variance structure, a challenging feature that no current open source REML solution implements. Finally, we illustrate the use of pyreml to analyze longitudinal data, and to perform spatial analysis. Anchored by 500+ functional tests assessing the equivalence of its outputs to those from reference software (lme4, rrBLUP and BLUPF90), we demonstrate pyreml to be a performant, reliable and open-source solution available for academic, research and industrial LMM users.

bioinformatics↗

bioRxiv2026

Origins Anywhere and Everywhere: Genome replication requires cooperative activation of thousands of origins in each cell

Our model of replication dynamics in a single eukaryotic cell supplants current population-based models. Long-read sequencing reveals cooperative activation of ~8400 closely spaced origins in every S.cerevisiae cell. Origins with bidirectional forks of comparable velocities are evenly distributed across the genome. The probability of early origin activation directly correlates with the average density of ORC (Origin Recognition Complex) binding motifs in a 10-20kbp region. We propose that 3D chromosome folding creates genomic pockets that act as ORC traps to increase local concentrations of pre-RC (Replication Complexes) and favor origin activation. Our results indicate that efficient replication through chromatin requires the activation of thousands of origins spanning the entire genome. This is consistent with genome instability being caused by insufficient origin activation rather than by perturbations in the replication timing program.

genomics↗

bioRxiv2026

When batch correction corrupts gene expression: uncovering distortions in correlation structures

Batch correction is essential for integrating datasets and enabling population-level insights into health and disease. Embedding-based approaches are among the most widely used solutions, but here we highlight a critical, overlooked limitation: these methods can distort feature-to-feature (e.g., gene-gene) relationships, potentially undermining downstream analyses. We investigate this issue and introduce a novel metric to quantify it.

bioinformatics↗

bioRxiv2025

Probing as a new technique to assess single-cell RNA-seq batch correction

Batch correction methods in single-cell RNA sequencing are essential for removing technical variation that can otherwise lead to misleading downstream analyses. The reliability of these methods is typically evaluated using unsupervised metrics. Here, we apply a Machine Learning (ML) technique called probing, formalized as the Batch Probing Score (BPS), to empirically demonstrate across six datasets that the most popular batch correctors fail to fully remove batch signal. In most cases, the batch of origin remains clearly identifiable after correction, even though standard evaluation metrics cannot detect it. We show that existing unsupervised metrics lack the sensitivity and specificity required to capture residual batch signal, whereas ML-based approaches can still detect it. This residual signal can similarly be picked up by downstream analysis tools, potentially leading to biased results. Because BPS is supervised, it directly quantifies batch signal strength by measuring how accurately the batch of origin can be predicted for each sample. It therefore provides an upper bound on the residual ML-actionable batch signal that could otherwise remain unnoticed. Our findings suggest that probing-based metrics should become a standard for assessing batch correction methods in single-cell RNA-seq and other areas of genomics.

bioinformatics↗

bioRxiv2024

The Specification Game: Rethinking the Evaluation of DrugResponse Prediction for Precision Oncology

Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug-response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability to generalize over the immense space of cancer cells and chemical compounds. This paper shows that the commonly used evaluation strategies for DRP methods learn solutions that optimize an unintended DRP score and fail to predict the proper drug-response activity ("specification gaming"). This problem hinders the advancement of the DRP field, and here we propose a new validation paradigm composed by three Aggregation Strategies (Global, Fixed-Drug, and Fixed-Cell Line) and three train-test Splitting Strategies to ensure a realistic assessment of the prediction performance. We also scrutinize the challenges associated with using IC50 as a prediction label, showing how its close correlation with the drug concentration ranges worsens the risk of misleading performance assessment. We thus propose also an alternative prediction label for DRP which is safer from this perspective.

bioinformatics↗

bioRxiv2024

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2024

Alleviating cell-free DNA sequencing biases with optimal transport

Cell-free DNA (cfDNA) is a rich source of biomarkers for various (patho)physiological conditions. Recent developments have used Machine Learning on large cfDNA data sets to enhance the detection of cancers and immunological diseases. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders that influence such data sets and lead to domain shifts (i.e., shifts in data distribution as those confounders vary across time or space). Here, we present a domain adaptation method that builds on the concept of optimal transport, and explicitly corrects for the effect of such preanalytical variables. Our approach can be used to merge cohorts representative of the same population but separated by technical biases. Moreover, we also demonstrate that it improves cancer detection via Machine Learning by alleviating the sources of variation that are not of biological origin. Our method also improves over the widely used GC-content bias correction, both in terms of bias removal and cancer signal isolation. These results open perspectives for the downstream analysis of larger data sets through the integration of cohorts produced by different sequencing pipelines or collected in different centers. Notably, the approach is rather general with the potential for application to many other genomic data analysis problems.

bioinformatics↗

bioRxiv2023

Data-driven probabilistic definition of the low energy conformational states of protein residues

Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g., in a stable helix, or can cover a wide range of conformations, e.g., in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behavior of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1,322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behavior of proteins that is complementary to their static interpretation from single structure models.

bioinformatics↗

bioRxiv2020

Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics

The role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and bioinformatics approaches that accurately delineate disordered protein regions remain essential for their identification and further investigation. Typically, these predictors use only the protein amino acid sequence, without taking into account likely emergent properties that are sequence context dependent, such as protein backbone dynamics. The DisoMine method predicts protein disorder with recurrent neural networks not directly from the amino acid sequence, but instead from more generic predictions of key biophysical properties, here protein dynamics, secondary structure and early folding. The tool is fast and requires only a single sequence, making it applicable for large-scale screening, including poorly studied and orphan proteins. DisoMine compares well to 10 state of the art predictors, also if these use evolutionary information. DisoMine is freely available through an interactive webserver at http://bio2byte.com/disomine/

bioinformatics↗