An introduction to the GenomicScores package
30 April 2024
Abstract
GenomicScores provides infrastructure to store and access genomewide position-specific scores within R and Bioconductor.
Package
GenomicScores 2.16.0
1 Getting started
GenomicScores is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:
install.packages("BiocManager")
BiocManager::install("GenomicScores")Once GenomicScores is installed, it can be loaded with the following command.
library(GenomicScores)Often, however, GenomicScores will be automatically loaded when working with an annotation package that uses GenomicScores, such as phastCons100way.UCSC.hg38.
2 Genomewide position-specific scores
Genomewide scores assign each genomic position a numeric value denoting an estimated measure of constraint or impact on variation at that position. They are commonly used to filter single nucleotide variants or assess the degree of constraint or functionality of genomic features. Genomic scores are built on the basis of different sources of information such as sequence homology, functional domains, physical-chemical changes of amino acid residues, etc.
One particular example of genomic scores are phastCons scores. They provide a measure of conservation obtained from genomewide alignments using the program phast (Phylogenetic Analysis with Space/Time models) from Siepel et al. (2005). The GenomicScores package allows one to retrieve these scores through annotation packages (Section 4) or as AnnotationHub resources (Section 5).
Often, genomic scores such as phastCons are used within workflows running on top of R and Bioconductor. The purpose of the GenomicScores package is to enable an easy and interactive access to genomic scores within those workflows.
3 Lossy storage of genomic scores with compressed vectors
Storing and accessing genomic scores within R is challenging when their values cover large regions of the genome, resulting in gigabytes of double-precision numbers. This is the case, for instance, for phastCons (Siepel et al. 2005) or CADD (Kircher et al. 2014).
We address this problem by using lossy compression, also called quantization, coupled with run-length encoding (Rle) vectors. Lossy compression attempts to trade off precision for compression without compromising the scientific integrity of the data (Zender 2016).
Sometimes, measurements and statistical estimates under certain models generate false precision. False precision is essentialy noise that wastes storage space and it is meaningless from the scientific point of view (Zender 2016). In those circumstances, lossy compression not only saves storage space, but also removes false precision.
The use of lossy compression leads to a subset of quantized values much smaller than the original set of genomic scores, resulting in long runs of identical values along the genome. These runs of identical values can be further compressed using the implementation of Rle vectors available in the S4Vectors Bioconductor package.
To enable a seamless access to genomic scores stored with quantized values
in compressed vectors the GenomicScores defines the GScores
class of objects. This class manages the location, loading and dequantization
of genomic scores stored separately on each chromosome, while it also provides
rich metadata on the provenance, citation and licensing of the original data.
4 Availability and retrieval of genomic scores
The access to genomic scores through GScores objects is available either
through annotation packages or as AnnotationHub resources. To
find out what kind of genomic scores are available, through which mechanism,
and in which organism, we may use the function availableGScores().
avgs <- availableGScores()
avgs
Organism Category
AlphaMissense.v2023.hg19 Homo sapiens Pathogenicity
AlphaMissense.v2023.hg38 Homo sapiens Pathogenicity
cadd.v1.3.hg19 <NA> <NA>
cadd.v1.6.hg19 Homo sapiens Pathogenicity
cadd.v1.6.hg38 Homo sapiens Pathogenicity
fitCons.UCSC.hg19 Homo sapiens Constraint
linsight.UCSC.hg19 Homo sapiens Constraint
MafDb.1Kgenomes.phase1.GRCh38 Homo sapiens MAF
MafDb.1Kgenomes.phase1.hs37d5 Homo sapiens MAF
MafDb.1Kgenomes.phase3.GRCh38 Homo sapiens MAF
MafDb.1Kgenomes.phase3.hs37d5 Homo sapiens MAF
MafDb.ExAC.r1.0.GRCh38 Homo sapiens MAF
MafDb.ExAC.r1.0.hs37d5 Homo sapiens MAF
MafDb.ExAC.r1.0.nonTCGA.GRCh38 Homo sapiens MAF
MafDb.ExAC.r1.0.nonTCGA.hs37d5 Homo sapiens MAF
MafDb.gnomAD.r2.1.GRCh38 Homo sapiens MAF
MafDb.gnomAD.r2.1.hs37d5 Homo sapiens MAF
MafDb.gnomADex.r2.1.GRCh38 Homo sapiens MAF
MafDb.gnomADex.r2.1.hs37d5 Homo sapiens MAF
MafDb.TOPMed.freeze5.hg19 Homo sapiens MAF
MafDb.TOPMed.freeze5.hg38 Homo sapiens MAF
MafH5.gnomAD.v3.1.2.GRCh38 Homo sapiens MAF
mcap.v1.0.hg19 Homo sapiens Pathogenicity
phastCons100way.UCSC.hg19 Homo sapiens Conservation
phastCons100way.UCSC.hg38 Homo sapiens Conservation
phastCons27way.UCSC.dm6 Drosophila melanogaster Conservation
phastCons30way.UCSC.hg38 Homo sapiens Conservation
phastCons35way.UCSC.mm39 Mus musculus Conservation
phastCons46wayPlacental.UCSC.hg19 Homo sapiens Conservation
phastCons46wayPrimates.UCSC.hg19 Homo sapiens Conservation
phastCons60way.UCSC.mm10 Mus musculus Conservation
phastCons7way.UCSC.hg38 Homo sapiens Conservation
phyloP100way.UCSC.hg19 Homo sapiens Conservation
phyloP100way.UCSC.hg38 Homo sapiens Conservation
phyloP35way.UCSC.mm39 Mus musculus Conservation
phyloP60way.UCSC.mm10 Mus musculus Conservation
Installed Cached BiocManagerInstall
AlphaMissense.v2023.hg19 FALSE TRUE FALSE
AlphaMissense.v2023.hg38 FALSE TRUE FALSE
cadd.v1.3.hg19 FALSE FALSE FALSE
cadd.v1.6.hg19 FALSE TRUE FALSE
cadd.v1.6.hg38 FALSE TRUE FALSE
fitCons.UCSC.hg19 FALSE FALSE TRUE
linsight.UCSC.hg19 FALSE FALSE FALSE
MafDb.1Kgenomes.phase1.GRCh38 FALSE FALSE TRUE
MafDb.1Kgenomes.phase1.hs37d5 TRUE FALSE TRUE
MafDb.1Kgenomes.phase3.GRCh38 TRUE FALSE TRUE
MafDb.1Kgenomes.phase3.hs37d5 TRUE FALSE TRUE
MafDb.ExAC.r1.0.GRCh38 FALSE FALSE TRUE
MafDb.ExAC.r1.0.hs37d5 TRUE FALSE TRUE
MafDb.ExAC.r1.0.nonTCGA.GRCh38 FALSE FALSE TRUE
MafDb.ExAC.r1.0.nonTCGA.hs37d5 FALSE FALSE TRUE
MafDb.gnomAD.r2.1.GRCh38 FALSE FALSE TRUE
MafDb.gnomAD.r2.1.hs37d5 FALSE FALSE TRUE
MafDb.gnomADex.r2.1.GRCh38 FALSE FALSE TRUE
MafDb.gnomADex.r2.1.hs37d5 TRUE FALSE TRUE
MafDb.TOPMed.freeze5.hg19 FALSE FALSE TRUE
MafDb.TOPMed.freeze5.hg38 FALSE FALSE TRUE
MafH5.gnomAD.v3.1.2.GRCh38 FALSE FALSE TRUE
mcap.v1.0.hg19 FALSE FALSE FALSE
phastCons100way.UCSC.hg19 TRUE FALSE TRUE
phastCons100way.UCSC.hg38 TRUE FALSE TRUE
phastCons27way.UCSC.dm6 FALSE FALSE FALSE
phastCons30way.UCSC.hg38 FALSE FALSE FALSE
phastCons35way.UCSC.mm39 FALSE FALSE FALSE
phastCons46wayPlacental.UCSC.hg19 FALSE FALSE FALSE
phastCons46wayPrimates.UCSC.hg19 FALSE FALSE FALSE
phastCons60way.UCSC.mm10 FALSE FALSE FALSE
phastCons7way.UCSC.hg38 FALSE FALSE TRUE
phyloP100way.UCSC.hg19 FALSE FALSE FALSE
phyloP100way.UCSC.hg38 FALSE FALSE FALSE
phyloP35way.UCSC.mm39 FALSE FALSE FALSE
phyloP60way.UCSC.mm10 FALSE FALSE FALSE
AnnotationHub
AlphaMissense.v2023.hg19 TRUE
AlphaMissense.v2023.hg38 TRUE
cadd.v1.3.hg19 FALSE
cadd.v1.6.hg19 TRUE
cadd.v1.6.hg38 TRUE
fitCons.UCSC.hg19 FALSE
linsight.UCSC.hg19 FALSE
MafDb.1Kgenomes.phase1.GRCh38 FALSE
MafDb.1Kgenomes.phase1.hs37d5 FALSE
MafDb.1Kgenomes.phase3.GRCh38 FALSE
MafDb.1Kgenomes.phase3.hs37d5 FALSE
MafDb.ExAC.r1.0.GRCh38 FALSE
MafDb.ExAC.r1.0.hs37d5 FALSE
MafDb.ExAC.r1.0.nonTCGA.GRCh38 FALSE
MafDb.ExAC.r1.0.nonTCGA.hs37d5 FALSE
MafDb.gnomAD.r2.1.GRCh38 FALSE
MafDb.gnomAD.r2.1.hs37d5 FALSE
MafDb.gnomADex.r2.1.GRCh38 FALSE
MafDb.gnomADex.r2.1.hs37d5 FALSE
MafDb.TOPMed.freeze5.hg19 FALSE
MafDb.TOPMed.freeze5.hg38 FALSE
MafH5.gnomAD.v3.1.2.GRCh38 FALSE
mcap.v1.0.hg19 FALSE
phastCons100way.UCSC.hg19 FALSE
phastCons100way.UCSC.hg38 FALSE
phastCons27way.UCSC.dm6 FALSE
phastCons30way.UCSC.hg38 FALSE
phastCons35way.UCSC.mm39 FALSE
phastCons46wayPlacental.UCSC.hg19 FALSE
phastCons46wayPrimates.UCSC.hg19 FALSE
phastCons60way.UCSC.mm10 FALSE
phastCons7way.UCSC.hg38 FALSE
phyloP100way.UCSC.hg19 FALSE
phyloP100way.UCSC.hg38 FALSE
phyloP35way.UCSC.mm39 FALSE
phyloP60way.UCSC.mm10 FALSEFor example, if we want to use the phastCons conservation scores available through the annotation package phastCons100way.UCSC.hg38, we should first install it (we only need to do this once).
BiocManager::install("phastCons100way.UCSC.hg38")Second, we should load the package, and a GScores object will be created and
named after the package name, during the loading operation. It is often handy
to shorten that name.
library(phastCons100way.UCSC.hg38)
phast <- phastCons100way.UCSC.hg38
class(phast)
[1] "GScores"
attr(,"package")
[1] "GenomicScores"Typing the name of the GScores object shows a summary of its contents and
some of its metadata.
phast
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: UCSC
# provider version: 11May2015
# download date: Apr 10, 2018
# loaded sequences: chr5_GL000208v1_random
# number of sites: 2943 millions
# maximum abs. error: 0.05
# use 'citation()' to cite these data in publicationsThe bibliographic reference to cite the genomic score data stored in a GScores
object can be accessed using the citation() method either on the package name
(in case of annotation packages), or on the GScores object.
citation(phast)
Adam Siepel, Gill Berejano, Jakob S. Pedersen, Angie S. Hinrichs,
Minmei Hou, Kate Rosenbloom, Hiram Clawson, John Spieth, LaDeana W.
Hillier, Stephen Richards, George M. Weinstock, Richard K. Wilson,
Richard A. Gibbs, W. James Kent, Webb Miller, David Haussler (2005).
"Evolutionarily conserved elements in vertebrate, insect, worm, and
yeast genomes." _Genome Research_, *15*, 1034-1050.
doi:10.1101/gr.3715005 <https://doi.org/10.1101/gr.3715005>.Other methods tracing provenance and other metadata are provider(),
providerVersion(), organism() and seqlevelsStyle(); please consult
the help page of the GScores class for a comprehensive list of available
methods.
provider(phast)
[1] "UCSC"
providerVersion(phast)
[1] "11May2015"
organism(phast)
[1] "Homo sapiens"
seqlevelsStyle(phast)
[1] "UCSC"To retrieve genomic scores for specific consecutive positions we should use the
method gscores(), as follows.
gscores(phast, GRanges(seqnames="chr22",
IRanges(start=50528591:50528596, width=1)))
GRanges object with 6 ranges and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528591 * | 1.0
[2] chr22 50528592 * | 1.0
[3] chr22 50528593 * | 0.8
[4] chr22 50528594 * | 1.0
[5] chr22 50528595 * | 1.0
[6] chr22 50528596 * | 0.0
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genomeFor a single position we may use this other GRanges() constructor.
gscores(phast, GRanges("chr22:50528593"))
GRanges object with 1 range and 1 metadata column:
seqnames ranges strand | default
<Rle> <IRanges> <Rle> | <numeric>
[1] chr22 50528593 * | 0.8
-------
seqinfo: 455 sequences (1 circular) from Genome Reference Consortium GRCh38 genomeWe may also retrieve the score values only with the method score().
score(phast, GRanges(seqnames="chr22",
IRanges(start=50528591:50528596, width=1)))
[1] 1.0 1.0 0.8 1.0 1.0 0.0
score(phast, GRanges("chr22:50528593"))
[1] 0.8Let’s illustrate how to retrieve phastCons scores using data from the GWAS
catalog available through the Bioconductor package gwascat. For
the purpose of this vignette, we will filter the GWAS catalog data by (1)
discarding entries with NA values in either chromosome name or position, or
with multiple positions; (2) storing the data into a GRanges object, including
the GWAS catalog columns STRONGEST SNP-RISK ALLELE and MAPPED_TRAIT, and the
reference and alternate alleles, as metadata columns; (4) restricting variants
to those located in chromosomes 20 to 22; and (3) excluding variants with
multinucleotide alleles, or where reference and alternate alleles are identical.
library(BSgenome.Hsapiens.UCSC.hg38)
library(gwascat)
gwc <- get_cached_gwascat()
mask <- !is.na(gwc$CHR_ID) & !is.na(gwc$CHR_POS) &
!is.na(as.integer(gwc$CHR_POS))
gwc <- gwc[mask, ]
grstr <- sprintf("%s:%s-%s", gwc$CHR_ID, gwc$CHR_POS, gwc$CHR_POS)
gwcgr <- GRanges(grstr, RISK_ALLELE=gwc[["STRONGEST SNP-RISK ALLELE"]],
MAPPED_TRAIT=gwc$MAPPED_TRAIT)
seqlevelsStyle(gwcgr) <- "UCSC"
mask <- seqnames(gwcgr) %in% c("chr20", "chr21", "chr22")
gwcgr <- gwcgr[mask]
ref <- as.character(getSeq(Hsapiens, gwcgr))
alt <- gsub("rs[0-9]+-", "", gwcgr$RISK_ALLELE)
mask <- (ref %in% c("A", "C", "G", "T")) & (alt %in% c("A", "C", "G", "T")) &
nchar(alt) == 1 & ref != alt
gwcgr <- gwcgr[mask]
mcols(gwcgr)$REF <- ref[mask]
mcols(gwcgr)$ALT <- alt[mask]gwcgr
GRanges object with 13540 ranges and 4 metadata columns:
seqnames ranges strand | RISK_ALLELE MAPPED_TRAIT
<Rle> <IRanges> <Rle> | <character> <character>
[1] chr20 35321981 * | rs6088792-T body height
[2] chr22 23250864 * | rs5751614-A body height
[3] chr20 6640246 * | rs967417-C body height
[4] chr20 33130847 * | rs6059101-A ulcerative colitis
[5] chr22 38148291 * | rs2284063-G nevus
... ... ... ... . ... ...
[13536] chr20 12979237 * | rs1321940-G sphingomyelin measur..
[13537] chr20 10148004 * | rs2210584-C sphingomyelin measur..
[13538] chr20 11892294 * | rs397865364-C sphingomyelin measur..
[13539] chr20 12823511 * | rs1413019-A sphingomyelin measur..
[13540] chr21 46284982 * | rs9975588-A S100 calcium-binding..
REF ALT
<character> <character>
[1] C T
[2] G A
[3] G C
[4] C A
[5] A G
... ... ...
[13536] A G
[13537] T C
[13538] T C
[13539] C A
[13540] G A
-------
seqinfo: 24 sequences from an unspecified genome; no seqlengthsFinally, let’s obtain the phastCons scores for this GWAS catalog variant set, and examine their summary and cumulative distribution.
pcsco <- score(phast, gwcgr)
summary(pcsco)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0000 0.0000 0.1217 0.0000 1.0000 38
round(cumsum(table(na.omit(pcsco))) / sum(!is.na(pcsco)), digits=2)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.81 0.85 0.87 0.88 0.88 0.89 0.89 0.90 0.90 0.91 1.00 We can observe that only 10% of the variants in chromosomes 20 to 22 have a conservation phastCons score above 0.5. Let’s examine which traits have more fully conserved variants.
xtab <- table(gwcgr$MAPPED_TRAIT[pcsco == 1])
head(xtab[order(xtab, decreasing=TRUE)])
body height
63
neuroimaging measurement
34
mean corpuscular volume
33
high density lipoprotein cholesterol measurement
30
hemoglobin measurement
26
BMI-adjusted waist-hip ratio
23 5 Genomic scores as AnnotationHub resources
The AnnotationHub (AH), is a Bioconductor web resource that provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard (e.g., UCSC, Ensembl) and distributed sites, can be found. An AH web resource creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access.
We can quickly check for the available AH resources by subsetting as follows
the resources names from the previous table obtained with availableGScores().
rownames(avgs)[avgs$AnnotationHub]
[1] "AlphaMissense.v2023.hg19" "AlphaMissense.v2023.hg38"
[3] "cadd.v1.6.hg19" "cadd.v1.6.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. Let’s download other conservation scores, the phyloP scores (Pollard et al. 2010), for human genome version hg38.
phylop <- getGScores("phyloP100way.UCSC.hg38")phylop
GScores object
# organism: Homo sapiens (UCSC, hg38)
# provider: UCSC
# provider version: 11May2015
# download date: May 12, 2017
# loaded sequences: chr20
# maximum abs. error: 0.55
# use 'citation()' to cite these data in publicationsLet’s retrieve the phyloP conservation scores for the previous set of GWAS catalog variants and compare them in Figure @(fig:phastvsphylop).
ppsco <- score(phylop, gwcgr)
plot(pcsco, ppsco, xlab="phastCons", ylab="phyloP",
cex.axis=1.2, cex.lab=1.5, las=1)
Figure 1: Comparison between phastCons and phyloP conservation scores
On the y-axis, phyloP scores as function of phastCons scores on the x-axis, for a set of GWAS catalog variant in the human chromosome 22.
We may observe that the values match in a rather discrete manner due to the
quantization of the scores. In the case of the phastCons annotation package
phastCons100way.UCSC.hg38, the GScore object gives access
in fact to two score populations, the default one in which conservation scores
are rounded to 1 decimal place, and an alternative one, named DP2, in which
they are rounded to 2 decimal places. To figure out what are the available
score populations in a GScores object, we should use the method
populations().
populations(phast)
[1] "default" "DP2" Whenever one of these populations is called default, this is the one used
by default. In other cases we can find out which is the default population as
follows:
defaultPopulation(phast)
[1] "default"To use one of the available score populations we should use the argument pop
in the corresponding method, as follows.
pcsco2 <- score(phast, gwcgr, pop="DP2")
head(pcsco2)
[1] 0.00 0.00 0.00 0.14 0.00 0.00Figure 2 below shows again the comparison of phastCons and phyloP conservation scores, this time at the higher resolution provided by the phastCons scores rounded at two decimal places.
plot(pcsco2, ppsco, xlab="phastCons", ylab="phyloP",
cex.axis=1.2, cex.lab=1.5, las=1)