ArticleGenome biology2017
A statistical framework for analyzing deep mutational scanning data.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 168 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
168 citing papers in PubMed.
- Protein Surface Site Determines the Evolutionary Accessibility of Allosteric Regulation.bioRxiv : the preprint server for biology · 2026Article
- Strain-specific epistasis shapes fitness landscapes of APOBEC3G antagonism by HIV-1 Vif proteins.Science advances · 2026Article
- Scanning the horizon: deep mutational scanning approaches in virology.Journal of virology · 2026Review
- Kozak sequence libraries for systematically characterizing transgenes across expression levels.Nucleic acids research · 2026Article
- ADAPT-M: a workflow for rapid, quantitative in vitro measurements of enriched protein libraries.Nature communications · 2026Article
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- Dual functional genomics reveals a broad and convergent landscape of asciminib resistance in BCR::ABL1.Genome medicine · 2026Article
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.bioRxiv : the preprint server for biology · 2026Article
- Article
- Environmental and mutational modulation of collateral fitness effects informs their mechanisms.Molecular biology and evolution · 2026Article
- Massively parallel functional genomic assays in endocrinology: from promise to delivery.Journal of molecular endocrinology · 2026Review
- CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis.bioRxiv : the preprint server for biology · 2026Article
- Mutational Scanning of α-Synuclein using a Clickable Protein Tag Reveals Determinants of Membrane-Induced Aggregation.bioRxiv : the preprint server for biology · 2026Article
- Genotype-by-inhibitor interactions to dissect enterovirus replication.Nature communications · 2026Article
- Determinants of metal import and specificity in a bacterial transporter.bioRxiv : the preprint server for biology · 2026Article
- A small polymerase ribozyme that can synthesize itself and its complementary strand.Science (New York, N.Y.) · 2026Article
- Deep-mutational scanning libraries using Tiled-Region Exchange mutagenesis.G3 (Bethesda, Md.) · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.Nature reviews. Cardiology · 2026Review
- Counting-based inference of mutant growth rates from pooled sequencing across growth regimes.bioRxiv : the preprint server for biology · 2026Article
108 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Deep mutational scanning is a widely used method for multiplex measurement of functional consequences of protein variants. We developed a new deep mutational scanning statistical model that generates error estimates for each measurement, capturing both sampling error and consistency between replicates. We apply our model to one novel and five published datasets comprising 243,732 variants and demonstrate its superiority in removing noisy variants and conducting hypothesis testing. Simulations show our model applies to scans based on cell growth or binding and handles common experimental errors. We implemented our model in Enrich2, software that can empower researchers analyzing 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.