ArticleBioinformatics (Oxford, England)2024
popDMS infers mutation effects from deep mutational scanning data.
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 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Scanning the horizon: deep mutational scanning approaches in virology.Journal of virology · 2026Review
- Combining multiplexed functional data to improve variant classification.Genome medicine · 2026Article
- Inferring context-specific site variation with evotuned protein language models.NAR genomics and bioinformatics · 2026Article
- Article
- Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Article
- Back-projection improves inference from sparsely sampled genomic surveillance data.bioRxiv : the preprint server for biology · 2025Article
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- Efficient epistasis inference via higher-order covariance matrix factorization.bioRxiv : the preprint server for biology · 2024Article
- Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
summaryDeep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions. AVAILABILITY AND IMPLEMENTATION: popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.
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Registered trials
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.