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AlphaMissense

Overview

AlphaMissense is a deep learning model that predicts the pathogenicity of missense variants across the human proteome. It produces a pathogenicity score between 0 and 1, where higher values indicate more pathogenic predictions.

This release provides pre-computed predictions for all possible human amino acid substitutions across major transcripts and isoforms.

For more details, refer to:

Publication

Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvilė Žemgulytė, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, Rosalia G. Schneider, Andrew W. Senior, John Jumper, Demis Hassabis, Pushmeet Kohli, Žiga Avsec. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023). https://doi.org/10.1126/science.adg7492

Parsing

AlphaMissense provides two tab-separated files:

  • a canonical TSV file (required)

    • AlphaMissense_hg38.tsv.gz for GRCh38

    • AlphaMissense_hg19.tsv.gz for GRCh37

  • an isoforms TSV file (optional)

    • AlphaMissense_isoforms_hg38.tsv.gz only for GRCh38

Only a subset of columns are consumed during parsing. Column positions are 0-based below.

TSV File

AlphaMissense_hg38.tsv.gz Snippet

From the canonical file, the following columns are parsed (0-based indices):

  • 0: #CHROM (reference name)

  • 1: pos (1-based position)

  • 2: ref (reference allele)

  • 3: alt (alternate allele)

  • 6: transcriptId (Ensembl transcript)

  • 7: proteinVariant (amino-acid substitution, e.g., V2L)

  • 8: pathogenicity (0-1)

  • 9: classification (e.g., likely_benign / likely_pathogenic / ambiguous)

Columns present but not consumed:

  • genome

  • uniprot_id (UniProt accession; see release notes: UniProt release notes)

AlphaMissense_isoforms_hg38.tsv.gz Snippet

From the isoforms file, the following columns are parsed (0-based indices):

  • 0: #CHROM

  • 1: pos

  • 2: ref

  • 3: alt

  • 5: transcriptId

  • 6: proteinVariant

  • 7: pathogenicity

  • 8: classification

During ingestion, transcripts present in the canonical file take precedence. Isoform records with the same transcriptId as a canonical record are skipped.

Classification labels

AlphaMissense provides am_class labels such as likely_benign, likely_pathogenic, and ambiguous.

SA Generation

Notes:

  • --i is optional. If omitted, only canonical records are ingested.

  • Output files are written with an automatically derived version name based on the .version sidecar file.

Known Issues

License and Disclaimer

License

We use and redistribute AlphaMissense predictions only, which are licensed under Creative Commons Attribution 4.0 (CC BY 4.0). See CC BY 4.0 legal code.

Attribution (CC BY 4.0): credit DeepMind/AlphaMissense and the authors, link to the license and source, indicate changes, and do not imply endorsement.

AlphaMissense predictions © 2023 DeepMind Technologies Limited, used under CC BY 4.0. Adapted for Illumina Connected Annotations.

Download URL

Contact

For questions about the dataset, contact alphamissense@google.com.

JSON Output

Field
Type
Notes

transcriptId

string

Transcript ID (Ensembl)

proteinVariant

string

Protein change (e.g., V2L)

pathogenicity

float

range: 0 - 1.0

classification

string

e.g., likely_benign, likely_pathogenic, ambiguous

isIsoform

bool

true if the record originated from the isoforms TSV

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