ArticleBioinformatics (Oxford, England)2022
Accelerating in silico saturation mutagenesis using compressed sensing.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rethinking peptide developability with sequence-only models: interpretable screening of microplastic-binding peptides with gated query pooling.Chemical science · 2026Article
- Deciphering the 3D genome organization across species from Hi-C data.Nucleic acids research · 2026Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- A two-task predictor for discovering phase separation proteins and their undergoing mechanism.Briefings in bioinformatics · 2024Article
- Quick and effective approximation of in silico saturation mutagenesis experiments with first-order taylor expansion.iScience · 2024Article
- Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task.PLoS computational biology · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
motivationIn silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined.
resultsIn this work, we propose a modification of ISM that leverages the principles of compressed sensing to require only a constant number of forward passes, regardless of sequence length, when applied to models that contain operations with a limited receptive field, such as convolutions. Our method, named Yuzu, can reduce the time that ISM spends in convolution operations by several orders of magnitude and, consequently, Yuzu can speed up ISM on several commonly used architectures in genomics by over an order of magnitude. Notably, we found that Yuzu provides speedups that increase with the complexity of the convolution operation and the length of the sequence being analyzed, suggesting that Yuzu provides large benefits in realistic settings. AVAILABILITY AND IMPLEMENTATION: We have made this tool available at https://github.com/kundajelab/yuzu.
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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.