ArticleBMC bioinformatics2015
Software for the analysis and visualization of deep mutational scanning data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed, 166 citations in OpenAlex.
- CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis.bioRxiv : the preprint server for biology · 2026Article
- CD164 is an endolysomal host factor for entry of Clade A New World Arenaviruses.bioRxiv : the preprint server for biology · 2026Article
- Recurrent mutations drive rapid HIV escape from two broadly neutralizing antibodies in vivo.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Comparative analysis of deep mutational scanning datasets in enteroviruses A and B identifies functional divergence and therapeutic targets.Nature ecology & evolution · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Coevolutionary constraints of Zika virus nonstructural protein 5 replication and interferon antagonism activities.bioRxiv : the preprint server for biology · 2025Article
- Deep Mutational Scanning in Immunology: Techniques and Applications.Pathogens (Basel, Switzerland) · 2025Review
- Combined crystallographic fragment screening and deep mutational scanning enable discovery of Zika virus NS2B-NS3 protease inhibitors.Nature communications · 2025Article
- Fostemsavir analog BMS-818251 has enhanced viral neutralization potency and similar escape mutation profile.Antimicrobial agents and chemotherapy · 2025Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Comparative Analysis of Deep Mutational Scanning Datasets in Enteroviruses A and B Identifies Functional Divergence and Therapeutic Targets.Research square · 2025Article
- Distinct modes of evolution drive HIV escape from two broadly neutralizing antibodies.bioRxiv : the preprint server for biology · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- A conserved opal termination codon optimizes a temperature-dependent trade-off between protein production and processing in alphaviruses.Science advances · 2025Article
- Deep mutationally scanned CHIKV E3/E2 virus library maps viral amino acid preferences and predicts viral escape mutants of neutralizing CHIKV antibodies.Journal of virology · 2025Article
- A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.Journal of the Royal Society, Interface · 2025Article
- Deep mutational scanning of the Trypanosoma brucei developmental regulator RBP6 reveals an essential disordered region influenced by positive residues.Nature communications · 2025Article
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- Deep mutationally scanned (DMS) CHIKV E3/E2 virus library maps viral amino acid preferences and predicts viral escape mutants of neutralizing CHIKV antibodies.bioRxiv : the preprint server for biology · 2024Article
- popDMS infers mutation effects from deep mutational scanning data.Bioinformatics (Oxford, England) · 2024Article
33 more citing papers are in PubMed but not listed here.
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
backgroundDeep mutational scanning is a technique to estimate the impacts of mutations on a gene by using deep sequencing to count mutations in a library of variants before and after imposing a functional selection. The impacts of mutations must be inferred from changes in their counts after selection.
resultsI describe a software package, dms_tools, to infer the impacts of mutations from deep mutational scanning data using a likelihood-based treatment of the mutation counts. I show that dms_tools yields more accurate inferences on simulated data than simply calculating ratios of counts pre- and post-selection. Using dms_tools, one can infer the preference of each site for each amino acid given a single selection pressure, or assess the extent to which these preferences change under different selection pressures. The preferences and their changes can be intuitively visualized with sequence-logo-style plots created using an extension to weblogo.
conclusionsdms_tools implements a statistically principled approach for the analysis and subsequent visualization of deep mutational scanning data.
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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.