# GA4GHclient

Welliton Souza1

1University of Campinas, Campinas, Brazil

#### 30 October 2017

#### Package

GA4GHclient 1.2.0

# 1 Introduction

The Global Alliance for Genomics and Health (GA4GH) was formed to help accelerate the potential of genomic medicine to advance human health. It brings together over 400 leading institutions working in healthcare, research, disease advocacy, life science, and information technology. The Data Working Group of the GA4GH developed data model schemas and application program interfaces (APIs) for genomic data. These APIs are specifically designed to allow sharing of genomics data in a standardized manner and without having to exchange complete experiments. They developed a reference implementation for these APIs providing a web server for hosting genomic data.

The Bioconductor project provides tools for the analysis and comprehension of high-throughput genomic data. It uses the R statistical programming language, and is open source and open development. The Bioconductor provides stable representations for genomics data such as biological sequences and genomic variants.

We developed GA4GHclient, a Bioconductor package that provides an easy way to access public data servers through GA4GH APIs. The requested output data are converted into tidy data and presented as data frames. Our package provides methods for converting genomic variant data into VCF data allowing the creation of complex data analysis using public genomic data and well validated Bioconductor packages. This package also provides an interactive web application for interacting with genomics data through GA4GH APIs.

Public data servers that use GA4GH Genomics API:

- Hosting Thousand Genomes Project
- Ensembl REST API

# 2 Available request methods

The table below shows all available methods in GA4GHclient package. These methods are based on the official GA4GH schemas. Let us know if some method is missing by opening an issue at https://github.com/labbcb/GA4GHclient/issues.

Method | Description
--- | ---
searchDatasets | Search for datasets
searchReferenceSets | Search for reference sets (reference genomes)
searchReferences | Search for references (genome sequences, e.g. chromosomes)
listReferenceBases | Get the sequence bases of a reference genome by genomic range
searchVariantSets | Search for for variant sets (VCF files)
searchVariants | Search for variants by genomic ranges (lines of VCF files)
searchCallSets | Search for call sets (sample columns of VCF files)
searchVariantAnnotationSets | Search for variant annotation sets (annotated VCF files)
searchVariantAnnotations | Search for annotated variants by genomic range
searchFeatureSets | Search for feature sets (genomic features, e.g. GFF files)
searchFeatures | Search for features (lines of genomic feature files)
searchReadGroupSets | Search for read group sets (sequence alignement, e.g BAM files)
searchReads | Search for reads by genomic range (bases of aligned sequences)
searchBiosamples | Search for biosamples
searchIndividuals | Search for individuals
searchRnaQuantificationSets | Search for RNA quantification sets
searchRnaQuantifications | Search for RNA quantifications
searchExpressionLevels | Search for expression levels
searchPhenotypeAssociationSets | Search for phenotype association sets
searchPhenotypeAssociations | Search for phenotype associations
getDataset | Get a dataset by its ID
getReferenceSet | Get a reference set by its ID
getReference | Get a reference by its ID
getVariantSet | Get a variant set by its ID
getVariant | Get a variant by its ID with all call sets for this variant
getCallSet | Get a call set by its ID
getVariantAnnotationSet | Get a variant annotation set by its ID
getVariantAnnotation | Get an annotated variant by its ID
getFeatureSet | Get a feature set by its ID
getFeature | Get a feature by its ID
getReadGroupSet | Get a read group set by its ID
getBiosample | Get a biosample by its ID
getIndividual | Get an individual by its ID
getRnaQuantificationSet | Get an RNA quantification set by its ID
getRnaQuantification | Get an RNA quantification by its ID
getExpressionLevel | Get an expression level by its ID

# 3 Retrieving Thousand Genomes Project data through GA4GHclient package

First, load required packages for this vignette.

    library(GA4GHclient)
    library(org.Hs.eg.db)
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    library(VariantAnnotation)

For this example we will use Hosting Thousand Genomes Project server. It contains data from the 1000 Genomes project. Set the URL of GA4GH API data server. This variable will be used by all request functions.

    host <- "http://1kgenomes.ga4gh.org/"

Get basic information about data server. The dataset may contain many variant sets. A variant set represents the header of an VCF file. A collection of variants represent lines of an VCF file. The `nrow` attribute define the amount of entries we want to get from the server. For the dataset and variant set we want only the first entry.

The `searchVariants` function requests genomic variant data from the server. It uses genomic intervals to retrieve these varants. As R programming language, the genomic indice is 1-based. If you want to see what reference names (e.g. chromosomes) are available run the `searchReferences` function.

    datasets <- searchDatasets(host, nrows = 1)
    datasetId <- datasets$id
    variantSets <- searchVariantSets(host, datasetId, nrows = 1)
    variantSetId <- variantSets$id
    variants <- searchVariants(host, variantSetId, referenceName = "1",
        start = 15000, end = 16000, nrows = 10)

    variants

    ## class: CollapsedVCF
    ## dim: 10 0
    ## rowRanges(vcf):
    ##   GRanges with 3 metadata columns: ID, REF, ALT
    ## info(vcf):
    ##   DataFrame with 14 columns: EUR_AF, SAS_AF, AC, AA, AF, AFR_AF, AMR_AF, ...
    ## info(header(vcf)):
    ##                  Number Type    Description
    ##    EUR_AF        A      Float   Allele frequency in the EUR populations c...
    ##    SAS_AF        A      Float   Allele frequency in the SAS populations c...
    ##    AC            A      Integer Total number of alternate alleles in call...
    ##    AA            1      String  Ancestral Allele. Format: AA|REF|ALT|Inde...
    ##    AF            A      Float   Estimated allele frequency in the range (...
    ##    AFR_AF        A      Float   Allele frequency in the AFR populations c...
    ##    AMR_AF        A      Float   Allele frequency in the AMR populations c...
    ##    AN            1      Integer Total number of alleles in called genotypes
    ##    VT            .      String  indicates what type of variant the line r...
    ##    EAS_AF        A      Float   Allele frequency in the EAS populations c...
    ##    NS            1      Integer Number of samples with data
    ##    EX_TARGET     0      Flag    indicates whether a variant is within the...
    ##    DP            1      Integer Total read depth; only low coverage data ...
    ##    MULTI_ALLELIC 0      Flag    indicates whether a site is multi-allelic
    ## geno(vcf):
    ##   SimpleList of length 0:

Almost search functions will return an `DataFrame` object from S4Vector package. The `searchVariants` and `getVariant` functions will return an `VCF` object with header from VariantAnnotation package. The `getVariantSet` function will return an `VCFHeader` object. These three functions will return `DataFrame` object when `asVCF` or `asVCFHeader` parameters be `FALSE`.

    variants.df <- searchVariants(host, variantSetId, referenceName = "1",
        start = 15000, end = 16000, nrows = 10, asVCF = FALSE)

    DT::datatable(as.data.frame(variants.df), options = list(scrollX = TRUE))

Due to internet connection, there is a limit of amount for response data imposed by the server (or the `responseSize` parameter). By default the function will make requests until get all response data. Increasing the value of the `responseSize` parameter will reduce the number os requests to server.

# 4 Search Variants by genomic location of genes

Bioconductor has annotation packages for many genomes. For example, the TxDb.Hsapiens.UCSC.hg19.knownGene pakage exposes an annotation databases generated from UCSC by exposing these as TxDb objects. Before using annotation packages, it is very important to check what version of reference genome was used by the GA4GH API-based data server. In other words, what reference genome was used to align sequencing reads and call for variants. This information can be accessed via `searchReferenceSets` function.

    referenceSets <- searchReferenceSets(host, nrows = 1)
    referenceSets$name

In this case, the version of reference genome is NCBI37, which is compatible with TxDb.Hsapiens.UCSC.hg19.knownGene.

Get name of all available genes.

    head(keys(org.Hs.eg.db, keytype = "SYMBOL"))

    ## [1] "A1BG"  "A2M"   "A2MP1" "NAT1"  "NAT2"  "NATP"

Get genomic location of all genes.

    head(genes(TxDb.Hsapiens.UCSC.hg19.knownGene))

    ## GRanges object with 6 ranges and 1 metadata column:
    ##             seqnames                 ranges strand |     gene_id
    ##                <Rle>              <IRanges>  <Rle> | <character>
    ##           1    chr19 [ 58858172,  58874214]      - |           1
    ##          10     chr8 [ 18248755,  18258723]      + |          10
    ##         100    chr20 [ 43248163,  43280376]      - |         100
    ##        1000    chr18 [ 25530930,  25757445]      - |        1000
    ##       10000     chr1 [243651535, 244006886]      - |       10000
    ##   100008586     chrX [ 49217763,  49233491]      + |   100008586
    ##   -------
    ##   seqinfo: 93 sequences (1 circular) from hg19 genome

Use case: search for variants located in SCN1A gene.

    df <- select(org.Hs.eg.db, keys = "SCN1A", columns = c("SYMBOL", "ENTREZID"), keytype = "SYMBOL")

    ## 'select()' returned 1:1 mapping between keys and columns

    gr <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene, filter=list(gene_id=df$ENTREZID))
    seqlevelsStyle(gr) <- "NCBI"

    variants.scn1a <- searchVariantsByGRanges(host, variantSetId, gr, asVCF = FALSE)

    DT::datatable(as.data.frame(variants.scn1a[[1]]), options = list(scrollX = TRUE))

    ## Warning in instance$preRenderHook(instance): It seems your data is too big
    ## for client-side DataTables. You may consider server-side processing: http://
    ## rstudio.github.io/DT/server.html

Use case: locate annotation data for a group of variants located at SCN1A gene.

    variants.scn1a.gr <- makeGRangesFromDataFrame(variants.scn1a[[1]],
        seqnames.field = "referenceName")
    seqlevelsStyle(variants.scn1a.gr) <- "UCSC"
    locateVariants(variants.scn1a.gr, TxDb.Hsapiens.UCSC.hg19.knownGene,
        CodingVariants())

    ## 'select()' returned many:1 mapping between keys and columns

    ## GRanges object with 308 ranges and 9 metadata columns:
    ##         seqnames                 ranges strand | LOCATION  LOCSTART
    ##            <Rle>              <IRanges>  <Rle> | <factor> <integer>
    ##     [1]     chr2 [166847834, 166847834]      - |   coding      5867
    ##     [2]     chr2 [166847834, 166847834]      - |   coding      5918
    ##     [3]     chr2 [166847834, 166847834]      - |   coding      5951
    ##     [4]     chr2 [166847921, 166847921]      - |   coding      5780
    ##     [5]     chr2 [166847921, 166847921]      - |   coding      5831
    ##     ...      ...                    ...    ... .      ...       ...
    ##   [304]     chr2 [166930064, 166930064]      - |   coding        68
    ##   [305]     chr2 [166930064, 166930064]      - |   coding        68
    ##   [306]     chr2 [166930078, 166930078]      - |   coding        54
    ##   [307]     chr2 [166930078, 166930078]      - |   coding        54
    ##   [308]     chr2 [166930078, 166930078]      - |   coding        54
    ##            LOCEND   QUERYID        TXID         CDSID      GENEID
    ##         <integer> <integer> <character> <IntegerList> <character>
    ##     [1]      5867        51       12221         37217        6323
    ##     [2]      5918        51       12222         37217        6323
    ##     [3]      5951        51       12223         37217        6323
    ##     [4]      5780        52       12221         37217        6323
    ##     [5]      5831        52       12222         37217        6323
    ##     ...       ...       ...         ...           ...         ...
    ##   [304]        68      2159       12222         37244        6323
    ##   [305]        68      2159       12223         37244        6323
    ##   [306]        54      2160       12221         37244        6323
    ##   [307]        54      2160       12222         37244        6323
    ##   [308]        54      2160       12223         37244        6323
    ##               PRECEDEID        FOLLOWID
    ##         <CharacterList> <CharacterList>
    ##     [1]
    ##     [2]
    ##     [3]
    ##     [4]
    ##     [5]
    ##     ...             ...             ...
    ##   [304]
    ##   [305]
    ##   [306]
    ##   [307]
    ##   [308]
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

# 5 VariantAnnotation classes

The `searchVariants` and `getVariant` functions return `VCF` objects. These `VCF` objects contains the VCF header. This is an example of how to get variants with call data. We can get calls running the `searchCallSets` function. After convertion, it can be written into disk as VCF file using `writeVcf`.

    callSetIds <- searchCallSets(host, variantSetId, nrows = 5)$id
    vcf <- searchVariants(host, variantSetId, referenceName = "1",
        start = 15000, end = 16000, callSetIds = callSetIds, nrows = 10)

    vcf

    ## class: CollapsedVCF
    ## dim: 10 5
    ## rowRanges(vcf):
    ##   GRanges with 3 metadata columns: ID, REF, ALT
    ## info(vcf):
    ##   DataFrame with 14 columns: EUR_AF, SAS_AF, AC, AA, AF, AFR_AF, AMR_AF, ...
    ## info(header(vcf)):
    ##                  Number Type    Description
    ##    EUR_AF        A      Float   Allele frequency in the EUR populations c...
    ##    SAS_AF        A      Float   Allele frequency in the SAS populations c...
    ##    AC            A      Integer Total number of alternate alleles in call...
    ##    AA            1      String  Ancestral Allele. Format: AA|REF|ALT|Inde...
    ##    AF            A      Float   Estimated allele frequency in the range (...
    ##    AFR_AF        A      Float   Allele frequency in the AFR populations c...
    ##    AMR_AF        A      Float   Allele frequency in the AMR populations c...
    ##    AN            1      Integer Total number of alleles in called genotypes
    ##    VT            .      String  indicates what type of variant the line r...
    ##    EAS_AF        A      Float   Allele frequency in the EAS populations c...
    ##    NS            1      Integer Number of samples with data
    ##    EX_TARGET     0      Flag    indicates whether a variant is within the...
    ##    DP            1      Integer Total read depth; only low coverage data ...
    ##    MULTI_ALLELIC 0      Flag    indicates whether a site is multi-allelic
    ## geno(vcf):
    ##   SimpleList of length 1: GT
    ## geno(header(vcf)):
    ##       Number Type   Description
    ##    GT 1      String Genotype

    header(vcf)

    ## class: VCFHeader
    ## samples(0):
    ## meta(0):
    ## fixed(0):
    ## info(27): CIEND CIPOS ... EX_TARGET MULTI_ALLELIC
    ## geno(1): GT

The `vcf` object works as expected. We can get the genotype data for example. More information about working with VCF data see `vignette("VariantAnnotation")`.

    geno(vcf)$GT

    ##       HG00096 HG00097 HG00099 HG00100 HG00101
    ##  [1,] "0|0"   "0|0"   "0|1"   "0|0"   "0|0"
    ##  [2,] "0|0"   "0|0"   "0|1"   "0|0"   "0|0"
    ##  [3,] "0|0"   "0|0"   "0|1"   "0|0"   "0|0"
    ##  [4,] "0|0"   "0|0"   "0|1"   "0|0"   "0|0"
    ##  [5,] "0|0"   "0|0"   "1|0"   "0|0"   "0|0"
    ##  [6,] "0|0"   "0|0"   "0|0"   "0|0"   "1|2"
    ##  [7,] "0|0"   "0|0"   "0|0"   "0|0"   "2|2"
    ##  [8,] "0|0"   "0|0"   "0|0"   "0|0"   "2|2"
    ##  [9,] "0|0"   "0|0"   "0|0"   "0|0"   "1|2"
    ## [10,] "0|0"   "0|0"   "0|0"   "0|0"   "1|2"

# 6 Session Information

    ## R version 3.4.2 Patched (2017-10-07 r73498)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] VariantAnnotation_1.24.0
    ##  [2] Rsamtools_1.30.0
    ##  [3] Biostrings_2.46.0
    ##  [4] XVector_0.18.0
    ##  [5] SummarizedExperiment_1.8.0
    ##  [6] DelayedArray_0.4.0
    ##  [7] matrixStats_0.52.2
    ##  [8] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
    ##  [9] GenomicFeatures_1.30.0
    ## [10] GenomicRanges_1.30.0
    ## [11] GenomeInfoDb_1.14.0
    ## [12] org.Hs.eg.db_3.4.2
    ## [13] AnnotationDbi_1.40.0
    ## [14] IRanges_2.12.0
    ## [15] Biobase_2.38.0
    ## [16] GA4GHclient_1.2.0
    ## [17] S4Vectors_0.16.0
    ## [18] BiocGenerics_0.24.0
    ## [19] BiocStyle_2.6.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] progress_1.1.2           lattice_0.20-35
    ##  [3] htmltools_0.3.6          rtracklayer_1.38.0
    ##  [5] yaml_2.1.14              blob_1.1.0
    ##  [7] XML_3.98-1.9             rlang_0.1.2
    ##  [9] glue_1.2.0               DBI_0.7
    ## [11] BiocParallel_1.12.0      bit64_0.9-7
    ## [13] bindrcpp_0.2             GenomeInfoDbData_0.99.1
    ## [15] bindr_0.1                stringr_1.2.0
    ## [17] zlibbioc_1.24.0          htmlwidgets_0.9
    ## [19] evaluate_0.10.1          memoise_1.1.0
    ## [21] knitr_1.17               biomaRt_2.34.0
    ## [23] highr_0.6                Rcpp_0.12.13
    ## [25] DT_0.2                   backports_1.1.1
    ## [27] BSgenome_1.46.0          jsonlite_1.5
    ## [29] bit_1.1-12               RMySQL_0.10.13
    ## [31] digest_0.6.12            stringi_1.1.5
    ## [33] bookdown_0.5             dplyr_0.7.4
    ## [35] grid_3.4.2               rprojroot_1.2
    ## [37] tools_3.4.2              bitops_1.0-6
    ## [39] magrittr_1.5             RCurl_1.95-4.8
    ## [41] RSQLite_2.0              tibble_1.3.4
    ## [43] pkgconfig_2.0.1          Matrix_1.2-11
    ## [45] prettyunits_1.0.2        httr_1.3.1
    ## [47] assertthat_0.2.0         rmarkdown_1.6
    ## [49] R6_2.2.2                 GenomicAlignments_1.14.0
    ## [51] compiler_3.4.2