ArticleGenome biology2023
mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- A designed ubiquitin-binding protein selectively enriches and visualizes unanchored ubiquitin.Science advances · 2026Article
- Proteome-wide identification of the druggable CRBN interactome.Nature biotechnology · 2026Article
- Scanning the horizon: deep mutational scanning approaches in virology.Journal of virology · 2026Review
- CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis.bioRxiv : the preprint server for biology · 2026Article
- Variant scoring tools for deep mutational scanning.Molecular systems biology · 2025Review
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.Genome biology · 2024Article
- Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions.BMC genomics · 2024Article
- Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage.Genome biology · 2024Article
- Minimum information and guidelines for reporting a multiplexed assay of variant effect.Genome biology · 2024Article
- Identifying molecular subgroups of patients with preeclampsia through bioinformatics.Frontiers in cardiovascular medicine · 2024Article
- mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data.Genome biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan .
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Identifiers
What OpenQuestion holds
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.