Store AlphaMissense.v2023.hg19 AnnotationHub Resource Metadata.
AlphaMissense.v2023.hg19 3.18.2
The AlphaMissense.v2023.hg19 package provides metadata for the
AnnotationHub resources associated with human AlphaMissense
pathogenicity scores [@cheng2023accurate]. The original data can be found at
the Google DeepMind download
site.
Details about how those original data were processed into
AnnotationHub resources can be found in the source
file:
AlphaMissense.v2023.hg19/scripts/make-metadata_AlphaMissense.v2023.hg19.RThe pathogenicity scores for AlphaMissense.v2023.hg19 can be retrieved using
the AnnotationHub,
which is a web resource that provides a central location where genomic files
(e.g., VCF, bed, wig) and other resources from standard (e.g., UCSC, Ensembl) and
distributed sites, can be found. A Bioconductor AnnotationHub web
resource creates and manages a local cache of files retrieved by the user,
helping with quick and reproducible access.
While the AnnotationHub API can be used to query those resources, we encourage to use the GenomicScores API [@puigdevall2018genomicscores], as follows. The first step to retrieve genomic scores is to check the ones available to download.
library(GenomicScores)
availableGScores()## [1] "AlphaMissense.v2023.hg19" "AlphaMissense.v2023.hg38"
## [3] "cadd.v1.6.hg19" "cadd.v1.6.hg38"
## [5] "fitCons.UCSC.hg19" "linsight.UCSC.hg19"
## [7] "mcap.v1.0.hg19" "phastCons7way.UCSC.hg38"
## [9] "phastCons27way.UCSC.dm6" "phastCons30way.UCSC.hg38"
## [11] "phastCons35way.UCSC.mm39" "phastCons46wayPlacental.UCSC.hg19"
## [13] "phastCons46wayPrimates.UCSC.hg19" "phastCons60way.UCSC.mm10"
## [15] "phastCons100way.UCSC.hg19" "phastCons100way.UCSC.hg38"
## [17] "phyloP35way.UCSC.mm39" "phyloP60way.UCSC.mm10"
## [19] "phyloP100way.UCSC.hg19" "phyloP100way.UCSC.hg38"The selected resource can be downloaded with the function getGScores().
After the resource is downloaded the first time, the cached copy will
enable a quicker retrieval later. In this case, because AlphaMissense
scores are distributed under a
CC BY-NC-SA 4.0 license, we should add the argument
accept.license=TRUE to non-interactively obtain the data. If we
do call getGScores() interactively without that argument, the function
will ask us to accept the license.
am23 <- getGScores("AlphaMissense.v2023.hg19", accept.license=TRUE)
am23## GScores object
## # organism: Homo sapiens (UCSC, hg19)
## # provider: Google DeepMind
## # provider version: v2023
## # download date: Oct 10, 2023
## # loaded sequences: chr1
## # maximum abs. error: 0.005
## # license: CC BY-NC-SA 4.0, see https://creativecommons.org/licenses/by-nc-sa/4.0
## # use 'citation()' to cite these data in publicationscitation(am23)## Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvilė Žemgulytė,
## Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski,
## Tobias Sargeant, Rosalia G. Schneider, Andrew W. Senior, John Jumper,
## Demis Hassabis, Pushmeet Kohli, Žiga Avsec (2023). "Accurate
## proteome-wide missense variant effect prediction with AlphaMissense."
## _Science_, *381*, eadg7492. doi:10.1126/science.adg7492
## <https://doi.org/10.1126/science.adg7492>.Finally, the AlphaMissense pathogenicity score of a particular genomic position
is retrieved using the function ‘gscores()’. Please consult the documentation
of the GenomicScores package for details on how to use it. For
instance, @cheng2023accurate report likely pathogenic scores for variants in
the human glucose sensor GCK. If we would like to retrieve the AlphaMissense
score of the variant
NM_000162.5(GCK):c.1174C>T (p.Arg392Cys),
classified as pathogenic in the ClinVar database, we should call gscores()
as follows.
gscores(am23, GRanges("chr7:44185175"), ref="C", alt="T")## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | default
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr7 44185175 * | 0.87
## -------
## seqinfo: 25 sequences (1 circular) from hg19 genomeRetrieving genomic scores through AnnotationHub resources requires an internet
connection and we may want to work with such resources offline. For that purpose,
we can create ourselves an annotation package, such as
phastCons100way.UCSC.hg19,
from a GScores object corresponding to a downloaded AnnotationHub resource.
To do that we use the function makeGScoresPackage() as follows:
makeGScoresPackage(am23, maintainer="Me <me@example.com>", author="Me", version="1.0.0")## Creating package in ./AlphaMissense.v2023.hg19An argument, destDir, which by default points to the current working
directory, can be used to change where in the filesystem the package is created.
Afterwards, we should still build and install the package via, e.g.,
R CMD build and R CMD INSTALL, to be able to use it offline.
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
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## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## time zone: America/New_York
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] GenomicScores_2.14.0 GenomicRanges_1.54.0 GenomeInfoDb_1.38.0
## [4] IRanges_2.36.0 S4Vectors_0.40.0 AnnotationHub_3.10.0
## [7] BiocFileCache_2.10.0 dbplyr_2.3.4 BiocGenerics_0.48.0
## [10] BiocStyle_2.30.0
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## [3] blob_1.2.4 filelock_1.0.2
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## [9] promises_1.2.1 XML_3.99-0.14
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## [43] DBI_1.1.3 cachem_1.0.8
## [45] rhdf5_2.46.0 zlibbioc_1.48.0
## [47] AnnotationDbi_1.64.0 BiocManager_1.30.22
## [49] XVector_0.42.0 matrixStats_1.0.0
## [51] vctrs_0.6.4 Matrix_1.6-1.1
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## [65] GenomeInfoDbData_1.2.11 R6_2.5.1
## [67] evaluate_0.22 shiny_1.7.5.1
## [69] Biobase_2.62.0 lattice_0.22-5
## [71] png_0.1-8 memoise_2.0.1
## [73] httpuv_1.6.12 bslib_0.5.1
## [75] Rcpp_1.0.11 SparseArray_1.2.0
## [77] xfun_0.40 MatrixGenerics_1.14.0
## [79] pkgconfig_2.0.3