Description

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

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

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

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.

Display Conventions and Configuration

BayesDel

There are eight subtracks for the BayesDel track: four include pre-computed MaxAF-integrated BayesDel scores for missense variants, one for each base. The other four are of the same format, but scores are not MaxAF-integrated.

For SNVs, at each genome position, there are three values per position, one for every possible nucleotide mutation. The fourth value, "no mutation", representing the reference allele, (e.g. A to A) is always set to zero.

Note: There are cases in which a genomic position will have one value missing.

When using this track, zoom in until you can see every base pair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and instead of an actual score, the tooltip text will show the average score of all nucleotides under the cursor. This is indicated by the prefix "~" in the mouseover.

Details on suggested ranges for BayesDel can be found in Bergquist et al Genet Med 2025, Table 2:

M-CAP and MutScore

There are four subtracks: one for each nucleotide.

Data Access

For automated download and analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server, there is one subdirectory per score. The files for this track are called usually called by their alternate allele, e.g. mcapA.bw and mutScoreA.bw. Individual regions or the whole genome annotation can be obtained using our tool bigWigToBedGraph which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigWigToBedGraph http://hgdownload.soe.ucsc.edu/gbdb/hg19/mcap/mcapA.bw -chrom=chr21 -start=0 -end=100000000 stdout

The original BayesDel files are available at the BayesDel website.

The other algorithms also have their own download formats, on the M-CAP website and the MutScore Website.

Methods

BayesDel data was converted from the files provided on the BayesDel_170824 Database. The number 170824 is the date (2017-08-24) the scores were created. Both sets of BayesDel scores are available in this database, one integrated MaxAF (named BayesDel_170824_addAF) and one without (named BayesDel_170824_noAF). Data conversion was performed using custom Python scripts.

M-CAP data was converted using a custom Python script and converted to bigWig, as documented in the our makeDoc text file. MutScore was already available in bigWig format to download.

Credits

Thanks to the BayesDel, MutScore and M-CAP teams for providing precomputed data, and to Tiana Pereira, Christopher Lee, Gerardo Perez, and Anna Benet-Pages of the Genome Browser team.

References

Feng BJ. PERCH: A Unified Framework for Disease Gene Prioritization. Hum Mutat. 2017 Mar;38(3):243-251. PMID: 27995669; PMC: PMC5299048

Pejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R, O'Donnell-Luria A, Harrison SM, Tavtigian SV, Greenblatt MS et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet. 2022 Dec 1;109(12):2163-2177. PMID: 36413997; PMC: PMC9748256

Tian Y, Pesaran T, Chamberlin A, Fenwick RB, Li S, Gau CL, Chao EC, Lu HM, Black MH, Qian D. REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification. Sci Rep. 2019 Sep 4;9(1):12752. PMID: 31484976; PMC: PMC6726608

Jagadeesh KA, Wenger AM, Berger MJ, Guturu H, Stenson PD, Cooper DN, Bernstein JA, Bejerano G. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat Genet. 2016 Dec;48(12):1581-1586. PMID: 27776117

Quinodoz M, Peter VG, Cisarova K, Royer-Bertrand B, Stenson PD, Cooper DN, Unger S, Superti-Furga A, Rivolta C. Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity. Am J Hum Genet. 2022 Mar 3;109(3):457-470. PMID: 35120630; PMC: PMC8948164

Bergquist T, Stenton SL, Nadeau EAW, Byrne AB, Greenblatt MS, Harrison SM, Tavtigian SV, O'Donnell-Luria A, Biesecker LG, Radivojac P et al. Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria. Genet Med. 2025 Mar 10;27(6):101402. PMID: 40084623