The "Prediction Scores" container track contains subtracks showing the results of variant impact prediction scores. Usually these are prediction algorithms that use protein features, conservation, nucleotide composition and similar signals to determine if a genome variant is pathogenic or not.

BayesDel - Only hg19

BayesDel is a deleteriousness meta-score for coding and non-coding variants, single nucleotide variants, and small insertion/deletions. The range of the score is from -1.29334 to 0.75731. The higher the score, the more likely the variant is pathogenic.

MaxAF stands for maximum allele frequency. The old ACMG (American College of Medical Genetics and Genomics) rules utilize allele frequency to classify variants, so the "BayesDel without MaxAF" tracks were created to avoid double-dipping. However, new ACMG rules will not include allele frequency, so it is okay to use the "BayesDel with MaxAF" for variant classification in the future. For gene discovery research, it is better to use BayesDel with MaxAF.

For gene discovery research, a universal cutoff value (0.0692655 with MaxAF, -0.0570105 without MaxAF) was obtained by maximizing sensitivity and specificity in classifying ClinVar variants; Version 1 (build date 2017-08-24).

For clinical variant classification, Bayesdel thresholds have been calculated for a variant to reach various levels of evidence; please refer to Pejaver et al. 2022 for general application of these scores in clinical applications.

M-CAP - Only hg19

Interpretation: The authors define that at an M-CAP score > 0.025, 5% of pathogenic variants are misclassified as benign. 0.025 is the recommended cutoff.

The Mendelian Clinically Applicable Pathogenicity (M-CAP) score (Jagadeesh et al, Nat Genetics 2016) is a pathogenicity likelihood score that aims to misclassify no more than 5% of pathogenic variants while aggressively reducing the list of variants of uncertain significance. Much like allele frequency, M-CAP is readily interpreted; if it classifies a variant as benign, then that variant can be trusted to be benign with high confidence.

At an M-CAP score > 0.025, 5% of pathogenic variants are misclassified as benign. The score varies from 0.0 - 1.0, following a geometric distribution with a mean of 0.09.

MutScore - hg38/hg19

Interpretation: The authors defined the thresholds <0.140 for a variant to be benign, and > 0.730 for pathogenic with 95% confidence.

The within-gene clustering of pathogenic and benign DNA changes is an important feature of the human exome. MutScore score (Quinodoz, AJHG 2022) integrates qualitative features of DNA substitutions with new additional information derived from positional clustering. Variants of unknown significance that are scored as benign by other algorithms but located close to known pathogenic variants should be weighted more pathogenic by MutScore. The score ranges from 0.0-1.0, resembles a negative binomial distribution with a maximum ~0.05, depending on the nucleotide. MutScore was seen to outperform other scores by papers Porretta et al and Brock et al.

PrimateAI-3D - hg38/hg19

Interpretation: Scores range from 0 to 1, with higher values indicating greater predicted pathogenicity. The authors suggest a clinical threshold of 0.821 for distinguishing pathogenic from benign missense variants. 75% of all possible missense variants are classified as benign, 25% as pathogenic.

PrimateAI-3D (Gao et al, Science 2023) is a semi-supervised 3D convolutional neural network trained on 4.5 million benign missense variants from 233 primate species and common human variants. It operates on voxelized protein structures at 2 Å resolution (from AlphaFold or homology models) combined with multiple sequence alignments from 592 species. The track contains pre-computed scores for all 70.7 million possible single nucleotide missense variants. Pathogenic variants are shown in red, benign in blue. Items can be filtered by prediction and by percentile score.

PromoterAI - hg38

Interpretation: Scores range from -1 to 1. Positive scores indicate predicted disruption of promoter function, negative scores indicate the variant is tolerated.

PromoterAI predicts the impact of single nucleotide variants in gene promoter regions, scoring all possible substitutions within 500 bp of annotated transcription start sites. The track contains four bigWig subtracks (one per alternate allele) covering 39.5 million positions, plus a bigBed track for the 3.8% of positions where overlapping transcripts produce different scores.

ClinPred - hg38/hg19

Interpretation: Scores range from 0 to 1, with higher values indicating greater predicted likelihood of pathogenicity. The authors recommend a threshold of ≥ 0.5 to flag variants as likely disease-relevant.

ClinPred (Alirezaie et al, AJHG 2018) is a machine-learning predictor for nonsynonymous (missense) single-nucleotide variants. It combines existing pathogenicity scores with population allele frequency from gnomAD, and was trained on confidently annotated disease-causing and benign variants from ClinVar. The track contains four bigWig subtracks (one per alternate allele) with pre-computed scores for all possible human missense variants in the exome. Pathogenic variants are shown in red, benign in blue.

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