---

type: allele_score
allele_score_mode: substitutions

table:
  filename: AlphaMissense_hg38_modified.tsv.gz
  format: tabix

  # defined by score_type
  chrom:
    column_name: chrom
  pos_begin:
    column_name: pos
  pos_end:
    column_name: pos
  reference:
    column_name: ref
  alternative:
    column_name: alt

# score values
scores:
  - id: am_pathogenicity
    column_name: am_pathogenicity
    type: float
    desc: |
      AlphaMissense Pathogenicity score is a deleteriousness score for missense variants
    large_values_desc: "more pathogenic"
    small_values_desc: "less pathogenic"
    histogram:
      type: number
      number_of_bins: 100
      view_range:
        min: 0.0
        max: 1.0
      y_log_scale: True
      
  - id: am_class
    column_name: am_class
    type: str
    desc: |
      AlphaMissense Class is a deleteriousness category for missense variants
    histogram:
      type: categorical
      y_log_scale: True

default_annotation:
  - name: am_pathogenicity
    source: am_pathogenicity

meta:
  summary: |

    Functional impact of mutations on protein function

  description: |

    AlphaMissense is a computational tool designed to predict the functional impact of missense mutations on protein structure and function. Leveraging advanced machine learning algorithms, AlphaMissense analyzes genetic variations to determine their potential effects on protein stability, interaction, and activity. By integrating structural bioinformatics with functional annotations, it provides insights into how specific missense mutations might contribute to disease mechanisms or impact drug efficacy. This tool is particularly valuable for researchers in genomics and precision medicine, offering a detailed assessment of mutation-related risks and aiding in the identification of therapeutic targets.
    
    Scores smaller than 0.34 or larger than 0.564 are categorized as likely\_benign or likely\_pathogenic, respectively.
    
    [Cheng et al., Accurate proteome-wide missense variant effect prediction with AlphaMissense, Science 2023](https://www.science.org/doi/10.1126/science.adg7492)
    
    Downloaded on: 07/29/24 [https://zenodo.org/records/8208688](https://zenodo.org/records/8208688)
    
    Processing details: dataPrep.sh prepares the data and index files.    
    

  labels:
    reference_genome: hg38/genomes/GRCh38-hg38