PT  - JOURNAL ARTICLE
AU  - Gjoni, Ketrin
AU  - Pollard, Katherine S.
TI  - SuPreMo: a computational tool for streamlining <em>in silico</em> perturbation using sequence-based predictive models
AID  - 10.1101/2023.11.03.565556
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.11.03.565556
4099  - http://biorxiv.org/content/early/2023/11/05/2023.11.03.565556.short
4100  - http://biorxiv.org/content/early/2023/11/05/2023.11.03.565556.full
AB  - Computationally editing genome sequences is a common bioinformatics task, but current approaches 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 in silico mutagenesis. 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/tree/main.Contact katherine.pollard{at}gladstone.ucsf.eduSupplementary information Supplementary data are available at Bioinformatics online.Competing Interest StatementThe authors have declared no competing interest.