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). Another constraint score, gnomAD constraint, is not part of this container but can be found in the hg38 gnomAD track. The algorithms covered here are:
-Shown are the scores as a signal ("wiggle") track, with one score per genome position. -Mouse over the bars to see the exact values. -
+JARVIS scores are the scores 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. +See hg19 makeDoc and +hg38 makeDoc. +-For HMC, the highly-constrained cutoff 0.8 is indicated with a line. -
+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. ++The HMC 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.
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 highest possible score represented when +representing each base pair mutation, with the lowest possible score represented when multiple transcripts overlap. It is recommended that the data be explored using this version of the track, as it condenses the information substatially 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. Items in the track are colored according +(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:
-Jarvis: Scores were downloaded and converted to a single bigWig file.
-See hg19 makeDoc and
-hg38 makeDoc.
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-HMC: 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.
-By default, only scores with an MTR percentile over 75 are shown.
+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 .bedGraph files with a custom Python script. The bedGraph files were then converted to bigWig files, as documented in our makeDoc hg19 build log.
Scores were downloaded and converted to a single bigWig file. See hg19 makeDoc and hg38 makeDoc
V2 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.
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 for helping clean up the hg38 Jarvis files.
Vitsios D, Dhindsa RS, Middleton L, Gussow AB, Petrovski S. Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. 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. Medrxiv 2022.02.16.22271023
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