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Cited by 13Genomics and Rare DiseasesCRISPR and Genetic EngineeringEvolution and Genetic Dynamics

Variant scoring tools for deep mutational scanning

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Çubuk, Hasan, Jin, Xinyi, Phipson, Belinda, Marsh, Joseph A, Rubin, Alan F·2025·Molecular Systems Biology
DOI 10.1038/s44320-025-00137-xPMC PMC12494760
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Abstract

Deep mutational scanning (DMS) can systematically assess the effects of thousands of genetic variants in a single assay, providing insights into protein function, evolution, host-pathogen interactions, and clinical impacts. Accurate scoring of variant effects is crucial, yet the diversity of tools and experimental designs contributes considerable heterogeneity that complicates data analysis. Here, we review and compare 12 computational tools for processing DMS sequencing data and scoring variant effects. We systematically outline each tool’s statistical approaches, supported experimental designs, input/output requirements, software implementation, visualisation capabilities, and key assumptions. By highlighting the strengths and limitations of these tools, we hope to guide researchers in selecting methods appropriate for their specific experiments. Furthermore, we discuss current challenges, including the need for standardised analysis protocols and sustainable software maintenance, as well as opportunities for future methods development. Ultimately, this review seeks to advance the application and adoption of DMS, facilitating deeper biological understanding and improved clinical translation.

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  • Enrich2 is widely adopted for MAVEs utilising barcode sequencing.

    “Examples include TileSeqMave v1.0 (47), commonly used in MAVEs employing a direct/tile sequence approach, and Enrich2 (100), widely adopted for MAVEs utilising barcode sequencing (89).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [89]

    The Enrich2 section describes Enrich2 as a commonly used DMS scoring tool and says it can process barcode-sequencing data. It does not establish that Enrich2 is widely adopted specifically among MAVEs using barcode sequencing; that is more specific than the usage claim in the source. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Once mapping of barcodes to variants has been established, variant abundance in the functional assay can be quantified by sequencing only the short barcode region.

    “Once this mapping has been established, variant abundance in the functional assay can be quantified by sequencing only the short barcode region, rather than the full variant sequence (89).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [89]

  • Some MAVE data analysis tools can analyse experiments with multiple time points or conditions, while some are limited to analysing single pre-selected and post-selected cell populations.

    “At least seven different MAVE data analysis tools have been released (89); some are able to analyse experiments with multiple time points or conditions and some are limited to analysing single pre-selected and post-selected cell populations.”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [89]

  • At least seven different MAVE data analysis tools have been released.

    “At least seven different MAVE data analysis tools have been released (89); some are able to analyse experiments with multiple time points or conditions and some are limited to analysing single pre-selected and post-selected cell populations.”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [89]

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Authors
Çubuk, H., Jin, X., Phipson, B., Marsh, J. A., & Rubin, A. F.
Affiliations
  • Institute of Genetics and Cancer (GB)
  • The University of Melbourne (AU)
Corresponding author
  • Marsh, Joseph A
  • Rubin, Alan F
ORCID
Venue
Molecular Systems Biology
Publisher
Springer Science and Business Media LLC
Published
2025-08-08
Volume
21
Issue
10
Pages
1293-1305
ISSN
1744-4292 (ISSN-L)
DOI
10.1038/s44320-025-00137-x
PMC
PMC12494760
OpenAlex
W4413055808
Cited by
13
References
3
Citations by year
2026: 12 · 2025: 1
FWCI
1.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Systems Biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
  • HHS | NIH | National Human Genome Research Institute (UM1HG011969, RM1HG010461)
  • EC | European Research Council (101001169)
  • UKRI | Medical Research Council (MC_UU_00035/9)
  • Federal Government | DHAC | National Health and Medical Research Council (GNT2025641)
  • CSL Limited
  • University of Melbourne
  • Walter and Eliza Hall Institute
  • Medical Research Future Fund
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
deep mutational scanning, computational tools, protein function, protein evolution, host-pathogen interaction, statistical analysis, experimental design, software comparison, software maintenance
MeSH terms
DNA Mutational Analysis, Humans, Mutation, Software, Genetic Variation, Computational Biology, High-Throughput Nucleotide Sequencing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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