Description

This track shows AlphaMissense predictions for all possible single amino acid substitutions in the human proteome.

AlphaMissense is a deep learning method for predicting the pathogenicity of missense variants in human proteins. It classifies 32% of all missense variants as likely pathogenic and 57% as likely benign using a cutoff yielding 90% precision on the ClinVar dataset.

Display Conventions and Configuration

There are four lettered subtracks, one for every nucleotide, showing scores for mutation from the reference to that nucleotide. All subtracks show the AlphaMissense score on mouseover. Across the exome, 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, "0.0". AlphaMissense only takes into account amino acid changes, so a nucleotide change that results in no amino acid change (synonymous) is not scored. These are shown in the tracks with score "0.0".

When using this track, zoom in until you can see every basepair at the top of the display. Otherwise, there are several nucleotides per pixel under your mouse cursor and no score will be shown on the mouseover tooltip.

Track colors

This track is colored according to the am_class column in the AlphaMissense_hg38.tsv file.

Data access

AlphaMissense scores are available at the AlphaMissense cloud storage site. The site provides precomputed AlphaMissense scores for all possible human missense variants to facilitate the identification of pathogenic variants among the large number of rare variants discovered in sequencing studies.

The AlphaMissense data on the UCSC Genome Browser can be explored interactively with the Table Browser or the Data Integrator.

For automated download and analysis, the genome annotation is stored at UCSC in bigWig format that can be downloaded from our download server. The files for this track are called a.bw, c.bw, g.bw, t.bw. Individual regions or the whole genome annotation can be obtained using our tool bigWigToWig 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. For example, to extract only annotations in a given region, you could use the following command:

bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 http://hgdownload.soe.ucsc.edu/gbdb/hg38/alphaMissense/a.bw stdout

Methods

Data were converted from the files provided on the AlphaMissense Downloads website. As with all other tracks, a full log of all commands used for the conversion is available in our source repository, for hg19 and hg38. The release used for each assembly is shown on the track description page.

Credits

Thanks to

References

Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, Pritzel A, Wong LH, Zielinski M, Sargeant T et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023 Sep 22;381(6664):eadg7492. PMID: 37733863