Biology subjects

Minary, P.

Publications and source records attributed to Minary, P..

5 recordsLinked to original sources

bioRxiv2026

Probabilistic Modelling of Base Editing Efficiency and Bystander Outcome Composition

Abstract. Base editors install single nucleotide substitutions without a double strand break or donor template, but editing at multiple susceptible positions can generate bystander alleles. Predicting the frequency of an intended outcome therefore requires both editing efficiency and the composition of edited products. Here we present crispAIBE, a probabilistic framework that uses separately trained transformer convolutional networks to predict Dirichlet distributions for editing efficiency and conditional allele composition. Split-conformal density-level regions are calibrated on data reserved from model selection, with composition thresholds fitted by outcome-set size. On held-out data, crispAIBE achieves Pearson correlations of 0.669 for efficiency and 0.896 for composition on measured allele sets, with empirical coverages of 0.902 and 0.906 at a nominal level of 0.90. On the published CRISPRon-BE benchmark, with test protospacers excluded from training, crispAIBE ranks first in seven of eight comparisons under the two-column Pearson statistic and first or second in seven of eight under the rank statistic. Within predicted-efficiency quintiles, the largest- and smallest-region deciles differ 1.9-fold in mean absolute error on average, compared with 1.7-fold for a boundary-distance ranking. When evaluated on U2OS, which was excluded from training, larger predictive regions were associated with larger absolute prediction errors. Local calibration with 100 U2OS protospacers yields median empirical coverage of 0.918 at the nominal 0.90 level. The crispAIBE repository is https://github.com/furkanozdenn/crispAIBE.

bioinformatics↗

bioRxiv2025

Probabilistic Modelling of Prime Editing Variant CorrectionEfficiency

Prime editing installs precise substitutions, insertions, and deletions without double-strand breaks or donor templates, but the efficiency of an individual pegRNA design is hard to predict in advance, and existing tools give only point estimates, leaving designers unable to judge which predictions to trust. We present crispAIPE, a transformer-based probabilistic framework that quantifies per pegRNA-target pair uncertainty by modelling the three competing prime-editing outcomes on the 2-simplex with a Dirichlet likelihood and pairing the posterior with split-conformal highest-density regions calibrated on a held-out fold, giving finite-sample coverage guarantees on the simplex without requiring the Dirichlet model itself to be well-calibrated. Trained on 92,423 PRIDICT Library-1 pegRNAs under mutation-level target-disjoint splitting, crispAIPE attains Spearman{rho} = 0.835, 0.843, and 0.693 on the edited, unedited, and indel fractions (Pearson r = 0.845, 0.855, 0.659), and its conformal regions match nominal coverage where region-construction baselines do not. pegRNA architecture and edit context, in particular GC content of the mutated reverse transcription template (RTT) and edit size for deletions, are associated with prediction uncertainty. Reusing the Library-1 calibration quantile unchanged on PRIDICT Library-2, the conformal region area generalises as an actionable filter for cross-cell-type design, identifying the pegRNAs whose Library-1 predictions transfer best while small-sample head fine-tuning further improves accuracy. Tool and trained models: https://github.com/furkanozdenn/pe-uncert.

bioinformatics↗

bioRxiv2025

DeepEmbCas9: Cas9 coevolution and sgRNA structural information for CRISPR-Cas9 cleavage activity prediction

The development of CRISPR-Cas9 cleavage activity prediction tools hinges on data produced from high-throughput guide-target lentiviral library screens for different Cas9 variants. However, the majority of such tools remain limited to predictions for one or few Cas9 variants, making it difficult to quantify the effects of Cas9 residues on cleavage activity. To bridge the gap, we introduce 4 interpretable DeepEmbCas9 models for the cleavage activity prediction of 40 type II-A and II-C Cas9 variants -- DeepEmbCas9, DeepEmbCas9-MVE, DeepEnsEmbCas9 naive, and DeepEnsEmbCas9 -- leveraging protein and RNA language model embeddings to encode Cas9 and sgRNA, respectively. Among the 4 neural network models, DeepEnsEmbCas9 naive performed the best in both in-distribution and out-of-distribution settings, where DeepEnsEmbCas9 naive outperformed individual Cas9 cleavage activity prediction tools on 18 out of 51 and 17 out of 48 benchmark test sets, respectively, and performed comparably otherwise. Concerning uncertainty quantification, DeepEnsEmbCas9 yields quantile-calibrated uncertainty estimates while keeping a minimal performance drop compared to DeepEnsEmbCas9 naive. SHAP importance analysis on DeepEmbCas9 reaffirms the importance of Cas9-target PAM binding as a first step for Cas9 cleavage, and reveals the L2 linker and PLL-WED-PI as important Cas9 domains modulating DeepEmbCas9s predicted activity change when introducing increased-fidelity and PAM-altering Cas9 mutations, respectively. Our findings demonstrate the usefulness of protein language model embeddings in uncertainty-aware Cas9 cleavage activity prediction. More generally, DeepEmbCas9 models serves as an initial step towards cleavage activity prediction modelling for the whole Cas9 protein family.

bioinformatics↗

bioRxiv2023

Quantifying uncertainty in off-target activity for CRISPR guide RNAs

CRISPR-based genome editing technologies have revolutionised the field of molecular biology, offering unprecedented opportunities for precise genetic manipulation. However, off-target effects remain a significant challenge, potentially leading to unintended consequences and limiting the applicability of CRISPR-based genome editing technologies in clinical settings. Current literature predominantly focuses on point predictions for off-target activity, which may not fully capture the range of possible outcomes and associated risks. Here, we present crispAI, a neural network architecture-based approach for predicting uncertainty estimates for off-target cleavage activity, providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) design. Our approach makes use of the count noise model Zero Inflated Negative Binomial (ZINB) to model the uncertainty in the off-target cleavage activity data. In addition, we present the first-of-its-kind genome-wide sgRNA efficiency score, crispAI-aggregate, enabling prioritization among sgRNAs with similar point aggregate predictions by providing richer information compared to existing aggregate scores. We show that uncertainty estimates of our approach are calibrated and its predictive performance is superior to state-of-the-art in silico off-target cleavage activity prediction methods.

bioinformatics↗

bioRxiv2021

piCRISPR: Physically Informed Features Improve Deep Learning Models for CRISPR/Cas9 Off-Target Cleavage Prediction

CRISPR/Cas programmable nuclease systems have become ubiquitous in the field of gene editing. With progressing development, applications in in vivo therapeutic gene editing are increasingly within reach, yet limited by possible adverse side effects from unwanted edits. Recent years have thus seen continuous development of off-target prediction algorithms trained on in vitro cleavage assay data gained from immortalised cell lines. It has been shown that in contrast to experimental epigenetic features, computed physically informed features are so far underutilised despite bearing considerably larger correlation with cleavage activity. Here, we implement state-of-the-art deep learning algorithms and feature encodings for off-target prediction with emphasis on physically informed features that capture the biological environment of the cleavage site, hence terming our approach piCRISPR. Features were gained from the large, diverse crisprSQL off-target cleavage dataset. We find that our best-performing models highlight the importance of sequence context and chromatin accessibility for cleavage prediction and compare favourably with literature standard prediction performance. We further show that our novel, environmentally sensitive features are crucial to accurate prediction on sequence-identical locus pairs, making them highly relevant for clinical guide design. The source code and trained models can be found ready to use at github.com/florianst/picrispr.

bioinformatics↗