Biology subjects

Dy, J.

Publications and source records attributed to Dy, J..

5 recordsLinked to original sources

bioRxiv2026

Improving splice site usage prediction with SPLAIRE

BackgroundAlternative splicing, the mechanism by which intronic sequences are excised from pre-mRNAs to produce mature mRNA, affects >95% of human protein-coding genes and is a major driver of human disease states. The spliceosome, a protein-RNA complex responsible for splicing pre-mRNA, identifies candidate splice sites partly through the recognition of characteristic sequence motifs at exon-intron junctions. Deep learning models that predict the presence of splice sites from pre-mRNA sequence have achieved breakthrough performance relative to previous machine-learning techniques, and these models have improved our ability to identify pathogenic genetic variants that alter splicing. ResultsWe show that, while overall performance measures from these models suggest near-perfect performance, substantial gaps in prediction remain, including the identification of splice sites with low usage rates and tissue-specific splice sites. We leverage one of the largest paired RNA and genotyping datasets used to date to train a novel splicing model optimized for a specific cell type, human airway epithelial cells. We trained a dilated convolutional neural network on data from cultured airway epithelial cells from 100 donors, and showed that this model outperforms current state-of-the-art models on splice site identification and splice site usage quantification, including on multiple tissues not included in the model training data. ConclusionsWe present the most comprehensive evaluation of state-of-the-art splicing models published to date, revealing reasonable performance across models for genetic variant effect prediction along with important performance gaps and insights into directions for future model development.

genomics↗

bioRxiv2026

Explainable machine learning reveals an RBP regulatory logic of exon skipping

RNA binding proteins (RBPs) regulate the life cycle of an mRNA, often through RBP-RNA interactions. This life cycle includes splicing, whereby the intronic sequence of a pre-mRNA is removed and the exons are joined together. However, the patterns of RBP binding that lead to different splicing outcomes are still incompletely understood. Here, we build machine learning models from RBP-RNA binding and knockdown RNA-seq data for over 168 RBPs in two cell lines (HepG2 and K562) to better understand the binding patterns that predict exon skipping, the predominant form of alternative splicing in humans. We show that models trained exclusively on RBP binding patterns are indeed predictive and that a more sophisticated machine learning model (XGBoost) outperforms simpler linear models. In addition, we are able to extract a biologically interpretable logic embedded in these models. We show that SHAP, a machine learning explainability technique, captures activating and repressive behavior of RBP binding that is position-specific. In addition, we find that SHAP values are predictive of changes in unseen splicing events and that SHAP interactions between pairs of RBPs are predictive of protein-protein interactions. Our results demonstrate that using machine learning with interpretability techniques can reveal a regulatory logic of RBP binding. By estimating the impact of an RBP binding site on a splicing event, the SHAP values also provide a directly testable scientific hypothesis. We anticipate that models designed around biological processes and focused on interpretability will yield actionable biological insights both in splicing and genomics generally.

systems biology↗

bioRxiv2020

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

High-throughput in vitro assays and AOP-based approach is promising for the assessment of health and ecotoxicological risks from exposure to pollutants and their mixtures. However, one of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo phenotypic toxicity endpoint. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (MRMR) and classification method (SVM), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction with 20 selected chemicals including model genotoxic chemicals and negative controls, respectively, using an in-vitro toxicogenomic assay that captures real-time proteomic response data of 38 GFP-fused proteins of S. cerevisiae strains covering biomarkers indicative of all known DNA damage and repair pathways in yeast. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both in-vivo carcinogenicity in rats and Ames genotoxicity endpoints. The specific biomarkers identified are different for the two different phenotypic genotoxicity assays. The top-ranked five biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/423486v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@11bc876org.highwire.dtl.DTLVardef@105ebc2org.highwire.dtl.DTLVardef@1b91a63org.highwire.dtl.DTLVardef@1738486_HPS_FORMAT_FIGEXP M_FIG TOC Art C_FIG

pharmacology and toxicology↗

bioRxiv2020

A computational neural model for mapping degenerate neural architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFAs utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI and the implications of applying NTFA to do so.

neuroscience↗

bioRxiv2020

Improved Prediction of Smoking Status via Isoform-Aware RNA-seq Deep Learning Models

Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in over 2,000 subjects in the COPDGene Study. We observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published five gene prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, our exon-based models including an exon-to-isoform mapping layer achieved a test set AUC of 0.88 using data from the same 5 genes and an AUC of 0.94 using a larger set of exon quantifications. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models. Author summaryPredictive models based on gene expression are already a part of medical decision making for selected situations such as early breast cancer treatment. Most of these models are based on measures that do not capture critical aspects of gene splicing, but with RNA sequencing it is possible to capture some of these aspects of alternative splicing and use them to improve clinical predictions. Building on previous models to predict cigarette smoking status, we show that measures of alternative splicing significantly improve the accuracy of these predictive models.

genomics↗