Note: Released on Oct 31, 2022
The "Constraint scores" container track includes several subtracks showing the results of
constraint prediction algorithms. These try to find regions of negative
selection, where variations likely have functional impact. The algorithms do
not use multi-species alignments to derive evolutionary constraint, but use
primarily human variation, usually from variants collected by gnomAD (see the
gnomAD V2 or V3 tracks on hg19 and hg38) or TOPMED (contained in our dbSNP
tracks and available as a filter). One of the subtracks is based on UK Biobank
variants, which are not available publicly, so we have no track with the raw data.
The number of human genomes that are used as the input for these scores are
76k, 53k and 110k for gnomAD, TOPMED and UK Biobank, respectively.
Note that another important constraint score, gnomAD
constraint, is not part of this container track but can be found in the hg38 gnomAD
The algorithms included in this track are:
JARVIS - "Junk" Annotation genome-wide Residual Variation Intolerance Score:
JARVIS scores were created by first scanning the entire genome with a
sliding-window approach (using a 1-nucleotide step), recording the number of
all TOPMED variants and common variants, irrespective of their predicted effect,
within each window, to eventually calculate a single-nucleotide resolution
genome-wide residual variation intolerance score (gwRVIS). That score, gwRVIS
was then combined with primary genomic sequence context, and additional genomic
annotations with a multi-module deep learning framework to infer
pathogenicity of noncoding regions that still remains naive to existing
phylogenetic conservation metrics. The higher the score, the more deleterious
the prediction. This score covers the entire genome, except the gaps.
HMC - Homologous Missense Constraint:
Homologous Missense Constraint (HMC) is a amino acid level measure
of genetic intolerance of missense variants within human populations.
For all assessable amino-acid positions in Pfam domains, the number of
missense substitutions directly observed in gnomAD (Observed) was counted
and compared to the expected value under a neutral evolution
model (Expected). The upper limit of a 95% confidence interval for the
Observed/Expected ratio is defined as the HMC score. Missense variants
disrupting the amino-acid positions with HMC<0.8 are predicted to be
likely deleterious. This score only covers PFAM domains within coding regions.
MetaDome - Tolerance Landscape Score (hg19 only):
MetaDome Tolerance Landscape scores are computed as a missense over synonymous
variant count ratio, which is calculated in a sliding window (with a size of 21
codons/residues) to provide
a per-position indication of regional tolerance to missense variation. The
variant database was gnomAD and the score corrected for codon composition. Scores
<0.7 are considered intolerant. This score covers only coding regions.
MTR - Missense Tolerance Ratio (hg19 only):
Missense Tolerance Ratio (MTR) scores aim to quantify the amount of purifying
selection acting specifically on missense variants in a given window of
protein-coding sequence. It is estimated across sliding windows of 31 codons
(default) and uses observed standing variation data from the WES component of
gnomAD / the Exome Aggregation Consortium Database (ExAC), version 2.0. Scores
were computed using Ensembl v95 release. The number of gnomAD 2 exomes used here
is higher than the number of gnomAD 3 samples (125 exoms versus 76k full genomes),
but this score only covers coding regions.
UK Biobank depletion rank score (hg38 only):
Halldorsson et al. tabulated the number of UK Biobank variants in each
500bp window of the genome and compared this number to an expected number
given the heptamer nucleotide composition of the window and the fraction of
heptamers with a sequence variant across the genome and their mutational
classes. A variant depletion score was computed for every overlapping set
of 500-bp windows in the genome with a 50-bp step size. They then assigned
a rank (depletion rank (DR)) from 0 (most depletion) to 100 (least
depletion) for each 500-bp window. Since the windows are overlapping, we
plot the value only in the central 50bp of the 500bp window, following
advice from the author of the score,
Hakon Jonsson, deCODE Genetics. He suggested that the value of the central
window, rather than the worst possible score of all overlapping windows, is
the most informative for a position. This score covers almost the entire genome,
only very few regions were excluded, where the genome sequence had too many gap characters.
Display Conventions and Configuration
JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position.
Mousing over the bars displays the exact values. The scores were downloaded and converted to a single bigWig file.
Move the mouse over the bars to display the exact values. A horizontal line is shown at the 0.733
value which signifies the 90th percentile.
See hg19 makeDoc and
Interpretation: The authors offer a suggested guideline of > 0.9998 for identifying
higher confidence calls and minimizing false positives. In addition to that strict threshold, the
following two more relaxed cutoffs can be used to explore additional hits. Note that these
thresholds are offered as guidelines and are not necessarily representative of pathogenicity.
|Percentile||JARVIS score threshold|
HMC scores are displayed as a signal ("wiggle") track, with one score per genome position.
Mousing over the bars displays the exact values. The highly-constrained cutoff
of 0.8 is indicated with a line.
A protein residue with HMC score <1 indicates that missense variants affecting
the homologous residues are significantly under negative selection (P-value <
0.05) and likely to be deleterious. A more stringent score threshold of HMC<0.8
is recommended to prioritize predicted disease-associated variants.
MetaDome data can be found on two tracks, MetaDome and MetaDome All Data.
The MetaDome track should be used by default for data exploration. In this track
the raw data containing the MetaDome tolerance scores were converted into a signal ("wiggle")
track. Since this data was computed on the proteome, there was a small amount of coordinate
overlap, roughly 0.42%. In these regions the lowest possible score was chosen for display
in the track to maintain sensitivity. For this reason, if a protein variant is being evaluated,
the MetaDome All Data track can be used to validate the score. More information
on this data can be found in the MetaDome FAQ.
Interpretation: The authors suggest the following guidelines for evaluating
intolerance. By default, the MetaDome track displays a horizontal line at 0.7 which
signifies the first intolerant bin. For more information see the MetaDome publication.
|Classification||MetaDome Tolerance Score|
|Highly intolerant||≤ 0.175|
|Slightly intolerant||≤ 0.7|
MTR data can be found on two tracks, MTR All data and MTR Scores. In the
MTR Scores track the data has been converted into 4 separate signal tracks
representing each base pair mutation, with the lowest possible score shown when
multiple transcripts overlap at a position. Overlaps can happen since this score
is derived from transcripts and multiple transcripts can overlap.
A horizontal line is drawn on the 0.8 score line
to roughly represent the 25th percentile, meaning the items below may be of particular
interest. It is recommended that the data be explored using
this version of the track, as it condenses the information substantially while
retaining the magnitude of the data.
Any specific point mutations of interest can then be researched in the
MTR All data track. This track contains all of the information from
MTRV2 including more than 3 possible scores per base when transcripts overlap.
A mouse-over on this track shows the ref and alt allele, as well as the MTR score
and the MTR score percentile. Filters are available for MTR score, False Discovery Rate
(FDR), MTR percentile, and variant consequence. By default, only items in the bottom
25 percentile are shown. Items in the track are colored according
to their MTR percentile:
- Green items MTR percentiles over 75
- Black items MTR percentiles between 25 and 75
- Red items MTR percentiles below 25
- Blue items No MTR score
Interpretation: Regions with low MTR scores were seen to be enriched with
pathogenic variants. For example, ClinVar pathogenic variants were seen to
have an average score of 0.77 whereas ClinVar benign variants had an average score
of 0.92. Further validation using the FATHMM cancer-associated training dataset saw
that scores less than 0.5 contained 8.6% of the pathogenic variants while only containing
0.9% of neutral variants. In summary, lower scores are more likely to represent
pathogenic variants whereas higher scores could be pathogenic, but have a higher chance
to be a false positive. For more information see the MTR-Viewer publication.
Scores were downloaded and converted to a single bigWig file. See the
hg19 makeDoc and the
hg38 makeDoc for more info.
Scores were downloaded and converted to .bedGraph files with a custom Python
script. The bedGraph files were then converted to bigWig files, as documented in our
makeDoc hg19 build log.
The authors provided a bed file containing codon coordinates along with the scores.
This file was parsed with a python script to create the two tracks. For the first track
the scores were aggregated for each coordinate, then the lowest score chosen for any
overlaps and the result written out to bedGraph format. The file was then converted
to bigWig with the
bedGraphToBigWig utility. For the second track the file
was reorganized into a bed 4+3 and conveted to bigBed with the
See the hg19 makeDoc for details including the build script.
The raw MetaDome data can also be accessed via their Zenodo handle.
file was downloaded and columns were reshuffled as well as itemRgb added for the
MTR All data track. For the MTR Scores track the file was parsed with a python
script to pull out the highest possible MTR score for each of the 3 possible mutations
at each base pair and 4 tracks built out of these values representing each mutation.
See the hg19 makeDoc entry on MTR for more info.
The raw data can be explored interactively with the Table Browser, or
the Data Integrator. For automated access, this track, like all
others, is available via our API. However, for bulk
processing, it is recommended to download the dataset.
For automated download and analysis, the genome annotation is stored at UCSC in bigWig and bigBed
files that can be downloaded from
our download server.
Individual regions or the whole genome annotation can be obtained using our tools bigWigToWig
or bigBedToBed 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
The tools can also be used to obtain features confined to a given range, e.g.,
bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/hmc/hmc.bw stdout
Please refer to our
Data Access FAQ
for more information.
Thanks to Jean-Madeleine Desainteagathe (APHP Paris, France) for suggesting the JARVIS, MTR, HMC tracks. Thanks to Xialei Zhang for providing the HMC data file and to Dimitrios Vitsios and Slave Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. Additional
thanks to Laurens van de Wiel for providing the MetaDome data as well as guidance on the track development and interpretation.
Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S.
Prioritizing non-coding regions based on human genomic constraint and sequence context with deep
Nat Commun. 2021 Mar 8;12(1):1504.
PMID: 33686085; PMC: PMC7940646
Xiaolei Zhang, Pantazis I. Theotokis, Nicholas Li, the SHaRe Investigators, Caroline F. Wright, Kaitlin E. Samocha, Nicola Whiffin, James S. Ware
Genetic constraint at single amino acid resolution improves missense variant prioritisation and gene discovery.
Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C.
MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein
Hum Mutat. 2019 Aug;40(8):1030-1038.
PMID: 31116477; PMC: PMC6772141
Silk M, Petrovski S, Ascher DB.
MTR-Viewer: identifying regions within genes under purifying selection.
Nucleic Acids Res. 2019 Jul 2;47(W1):W121-W126.
PMID: 31170280; PMC: PMC6602522
Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G,
Hardarson MT, Oddsson A, Jensson BO et al.
The sequences of 150,119 genomes in the UK Biobank.
Nature. 2022 Jul;607(7920):732-740.
PMID: 35859178; PMC: PMC9329122