ArticleBioinformatics (Oxford, England)2024
SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine learning reveals sequence and genomic context features underlyingbioRxiv : the preprint server for biology · 2026Article
- De novo structural variants in autism spectrum disorder disrupt distal regulatory interactions of neuronal genes.Genome research · 2026Article
- BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.Bioinformatics (Oxford, England) · 2026Article
- Species-specific chromatin architecture and neurogenesis mediated by a human enhancer.Cell stem cell · 2026Article
- Machine learning-predicted chromatin organization landscape across pediatric tumors.Scientific reports · 2026Article
- Structural variants in human congenital heart disease disrupt distal genomic regulatory contacts of developmental genes.bioRxiv : the preprint server for biology · 2026Article
- Article
- Experimental Validation of MicroRNA Targets: Mutagenesis of Binding Regions Mutagenesis-Based Validation of MicroRNA Target Binding.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Unveiling the Genetic Landscape of Coronary Artery Disease Through Common and Rare Structural Variants.Journal of the American Heart Association · 2025Article
- Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2.PLoS computational biology · 2025Article
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Abstract
summaryThe increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing and supporting in silico mutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences. AVAILABILITY AND IMPLEMENTATION: SuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the GitHub page: https://github.com/ketringjoni/SuPreMo.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.