JARVIS scores are shown as a signal ("wiggle") track, with one score per genome position. @@ -82,30 +90,60 @@
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.
Interpretation: 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.
++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 |
| Intolerant | ≤ 0.525 |
| 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 @@ -139,88 +177,112 @@
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 parse 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 bedToBigBed
+utility.
+See the hg19 makeDoc for details including the build script.
+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.
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
here.
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/$db/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 Slavé Petrovski for helping clean up the hg38 JARVIS files for providing guidance on interpretation. +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 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
+Wiel L, Baakman C, Gilissen D, Veltman JA, Vriend G, Gilissen C. + +MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein +domains. +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