# AnnotationHub How-To’s

#### 7 November 2017

# Contents

Package: AnnotationHub
Authors: Martin Morgan [cre], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb]
Modified: Sun Jun 28 10:41:23 2015
Compiled: Tue Nov 7 19:17:44 2017

# 1 Accessing Genome-Scale Data

## 1.1 Non-model organism gene annotations

Bioconductor offers pre-built `org.*` annotation packages for model organisms, with their use described in the OrgDb section of the Annotation work flow. Here we discover available `OrgDb` objects for less-model organisms

    library(AnnotationHub)
    ah <- AnnotationHub()

    ## snapshotDate(): 2017-10-27

    query(ah, "OrgDb")

    ## AnnotationHub with 1019 records
    ## # snapshotDate(): 2017-10-27
    ## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
    ## # $species: Escherichia coli, 'Caballeronia concitans', 'Chlorella vulgaris'_C-169, 'Frankia cas...
    ## # $rdataclass: OrgDb
    ## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
    ## #   rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
    ## # retrieve records with, e.g., 'object[["AH57964"]]'
    ##
    ##             title
    ##   AH57964 | org.Ag.eg.db.sqlite
    ##   AH57965 | org.At.tair.db.sqlite
    ##   AH57966 | org.Bt.eg.db.sqlite
    ##   AH57967 | org.Cf.eg.db.sqlite
    ##   AH57968 | org.Gg.eg.db.sqlite
    ##   ...       ...
    ##   AH58988 | org.Flavobacterium_piscicida.eg.sqlite
    ##   AH58989 | org.Bacteroides_fragilis_YCH46.eg.sqlite
    ##   AH58990 | org.Pseudomonas_mendocina_ymp.eg.sqlite
    ##   AH58991 | org.Salmonella_enterica_subsp._enterica_serovar_Typhimurium_str._LT2.eg.sqlite
    ##   AH58992 | org.Acinetobacter_baumannii.eg.sqlite

    orgdb <- query(ah, "OrgDb")[[1]]

    ## loading from cache '/home/biocbuild//.AnnotationHub/64710'

The object returned by AnnotationHub is directly usable with the `select()` interface, e.g., to discover the available keytypes for querying the object, the columns that these keytypes can map to, and finally selecting the SYMBOL and GENENAME corresponding to the first 6 ENTREZIDs

    keytypes(orgdb)

    ##  [1] "ACCNUM"       "ENSEMBL"      "ENSEMBLPROT"  "ENSEMBLTRANS" "ENTREZID"     "ENZYME"
    ##  [7] "EVIDENCE"     "EVIDENCEALL"  "GENENAME"     "GO"           "GOALL"        "ONTOLOGY"
    ## [13] "ONTOLOGYALL"  "PATH"         "PMID"         "REFSEQ"       "SYMBOL"       "UNIGENE"
    ## [19] "UNIPROT"

    columns(orgdb)

    ##  [1] "ACCNUM"       "ENSEMBL"      "ENSEMBLPROT"  "ENSEMBLTRANS" "ENTREZID"     "ENZYME"
    ##  [7] "EVIDENCE"     "EVIDENCEALL"  "GENENAME"     "GO"           "GOALL"        "ONTOLOGY"
    ## [13] "ONTOLOGYALL"  "PATH"         "PMID"         "REFSEQ"       "SYMBOL"       "UNIGENE"
    ## [19] "UNIPROT"

    egid <- head(keys(orgdb, "ENTREZID"))
    select(orgdb, egid, c("SYMBOL", "GENENAME"), "ENTREZID")

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

    ##   ENTREZID          SYMBOL      GENENAME
    ## 1  1267437 AgaP_AGAP012606 AGAP012606-PA
    ## 2  1267439 AgaP_AGAP012559 AGAP012559-PA
    ## 3  1267440 AgaP_AGAP012558 AGAP012558-PA
    ## 4  1267447 AgaP_AGAP012586 AGAP012586-PA
    ## 5  1267450 AgaP_AGAP012834 AGAP012834-PA
    ## 6  1267459 AgaP_AGAP012589 AGAP012589-PA

## 1.2 Roadmap Epigenomics Project

All Roadmap Epigenomics files are hosted here. If one had to download these files on their own, one would navigate through the web interface to find useful files, then use something like the following R code.

    url <- "http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/E001-H3K4me1.broadPeak.gz"
    filename <-  basename(url)
    download.file(url, destfile=filename)
    if (file.exists(filename))
       data <- import(filename, format="bed")

This would have to be repeated for all files, and the onus would lie on the user to identify, download, import, and manage the local disk location of these files.

AnnotationHub reduces this task to just a few lines of R code

    library(AnnotationHub)
    ah = AnnotationHub()

    ## snapshotDate(): 2017-10-27

    epiFiles <- query(ah, "EpigenomeRoadMap")

A look at the value returned by `epiFiles` shows us that 18248 roadmap resources are available via AnnotationHub. Additional information about the files is also available, e.g., where the files came from (dataprovider), genome, species, sourceurl, sourcetypes.

    epiFiles

    ## AnnotationHub with 18248 records
    ## # snapshotDate(): 2017-10-27
    ## # $dataprovider: BroadInstitute
    ## # $species: Homo sapiens
    ## # $rdataclass: BigWigFile, GRanges, data.frame
    ## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
    ## #   rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
    ## # retrieve records with, e.g., 'object[["AH28856"]]'
    ##
    ##             title
    ##   AH28856 | E001-H3K4me1.broadPeak.gz
    ##   AH28857 | E001-H3K4me3.broadPeak.gz
    ##   AH28858 | E001-H3K9ac.broadPeak.gz
    ##   AH28859 | E001-H3K9me3.broadPeak.gz
    ##   AH28860 | E001-H3K27me3.broadPeak.gz
    ##   ...       ...
    ##   AH49540 | E058_mCRF_FractionalMethylation.bigwig
    ##   AH49541 | E059_mCRF_FractionalMethylation.bigwig
    ##   AH49542 | E061_mCRF_FractionalMethylation.bigwig
    ##   AH49543 | E081_mCRF_FractionalMethylation.bigwig
    ##   AH49544 | E082_mCRF_FractionalMethylation.bigwig

A good sanity check to ensure that we have files only from the Roadmap Epigenomics project is to check that all the files in the returned smaller hub object come from Homo sapiens and the hg19 genome

    unique(epiFiles$species)

    ## [1] "Homo sapiens"

    unique(epiFiles$genome)

    ## [1] "hg19"

Broadly, one can get an idea of the different files from this project looking at the sourcetype

    table(epiFiles$sourcetype)

    ##
    ##    BED BigWig    GTF    Zip    tab
    ##   8298   9932      3     14      1

To get a more descriptive idea of these different files one can use:

    sort(table(epiFiles$description), decreasing=TRUE)

    ##
    ##                       Bigwig File containing -log10(p-value) signal tracks from EpigenomeRoadMap Project
    ##                                                                                                     6881
    ##                       Bigwig File containing fold enrichment signal tracks from EpigenomeRoadMap Project
    ##                                                                                                     2947
    ##                          Narrow ChIP-seq peaks for consolidated epigenomes from EpigenomeRoadMap Project
    ##                                                                                                     2894
    ##                           Broad ChIP-seq peaks for consolidated epigenomes from EpigenomeRoadMap Project
    ##                                                                                                     2534
    ##                          Gapped ChIP-seq peaks for consolidated epigenomes from EpigenomeRoadMap Project
    ##                                                                                                     2534
    ##                              Narrow DNasePeaks for consolidated epigenomes from EpigenomeRoadMap Project
    ##                                                                                                      131
    ##                                           15 state chromatin segmentations from EpigenomeRoadMap Project
    ##                                                                                                      127
    ##      Broad domains on enrichment for DNase-seq for consolidated epigenomes from EpigenomeRoadMap Project
    ##                                                                                                       78
    ##                                         RRBS fractional methylation calls from EpigenomeRoadMap Project
    ##                                                                                                       51
    ##                       Whole genome bisulphite fractional methylation calls from EpigenomeRoadMap Project
    ##                                                                                                       37
    ##                               MeDIP/MRE(mCRF) fractional methylation calls from EpigenomeRoadMap Project
    ##                                                                                                       16
    ## GencodeV10 gene/transcript coordinates and annotations corresponding to hg19 version of the human genome
    ##                                                                                                        3
    ##                                       RNA-seq read count matrix for intronic protein-coding RNA elements
    ##                                                                                                        2
    ##                                                      RNA-seq read counts matrix for ribosomal gene exons
    ##                                                                                                        2
    ##                                                          RPKM expression matrix for ribosomal gene exons
    ##                                                                                                        2
    ##                                                                    Metadata for EpigenomeRoadMap Project
    ##                                                                                                        1
    ##                                                           RNA-seq read counts matrix for non-coding RNAs
    ##                                                                                                        1
    ##                                                      RNA-seq read counts matrix for protein coding exons
    ##                                                                                                        1
    ##                                                      RNA-seq read counts matrix for protein coding genes
    ##                                                                                                        1
    ##                                                           RNA-seq read counts matrix for ribosomal genes
    ##                                                                                                        1
    ##                                                               RPKM expression matrix for non-coding RNAs
    ##                                                                                                        1
    ##                                                          RPKM expression matrix for protein coding exons
    ##                                                                                                        1
    ##                                                          RPKM expression matrix for protein coding genes
    ##                                                                                                        1
    ##                                                                RPKM expression matrix for ribosomal RNAs
    ##                                                                                                        1

The ‘metadata’ provided by the Roadmap Epigenomics Project is also available. Note that the information displayed about a hub with a single resource is quite different from the information displayed when the hub references more than one resource.

    metadata.tab <- query(ah , c("EpigenomeRoadMap", "Metadata"))
    metadata.tab

    ## AnnotationHub with 1 record
    ## # snapshotDate(): 2017-10-27
    ## # names(): AH41830
    ## # $dataprovider: BroadInstitute
    ## # $species: Homo sapiens
    ## # $rdataclass: data.frame
    ## # $rdatadateadded: 2015-05-11
    ## # $title: EID_metadata.tab
    ## # $description: Metadata for EpigenomeRoadMap Project
    ## # $taxonomyid: 9606
    ## # $genome: hg19
    ## # $sourcetype: tab
    ## # $sourceurl: http://egg2.wustl.edu/roadmap/data/byFileType/metadata/EID_metadata.tab
    ## # $sourcesize: 18035
    ## # $tags: c("EpigenomeRoadMap", "Metadata")
    ## # retrieve record with 'object[["AH41830"]]'

So far we have been exploring information about resources, without downloading the resource to a local cache and importing it into R. One can retrieve the resource using `[[` as indicated at the end of the show method

    ## loading from cache '/home/biocbuild//.AnnotationHub/47270'

    metadata.tab <- ah[["AH41830"]]

    ## loading from cache '/home/biocbuild//.AnnotationHub/47270'

The metadata.tab file is returned as a data.frame. The first 6 rows of the first 5 columns are shown here:

    metadata.tab[1:6, 1:5]

    ##    EID    GROUP   COLOR          MNEMONIC                                   STD_NAME
    ## 1 E001      ESC #924965            ESC.I3                                ES-I3 Cells
    ## 2 E002      ESC #924965           ESC.WA7                               ES-WA7 Cells
    ## 3 E003      ESC #924965            ESC.H1                                   H1 Cells
    ## 4 E004 ES-deriv #4178AE ESDR.H1.BMP4.MESO H1 BMP4 Derived Mesendoderm Cultured Cells
    ## 5 E005 ES-deriv #4178AE ESDR.H1.BMP4.TROP H1 BMP4 Derived Trophoblast Cultured Cells
    ## 6 E006 ES-deriv #4178AE       ESDR.H1.MSC          H1 Derived Mesenchymal Stem Cells

One can keep constructing different queries using multiple arguments to trim down these 18248 to get the files one wants. For example, to get the ChIP-Seq files for consolidated epigenomes, one could use

    bpChipEpi <- query(ah , c("EpigenomeRoadMap", "broadPeak", "chip", "consolidated"))

To get all the bigWig signal files, one can query the hub using

    allBigWigFiles <- query(ah, c("EpigenomeRoadMap", "BigWig"))

To access the 15 state chromatin segmentations, one can use

    seg <- query(ah, c("EpigenomeRoadMap", "segmentations"))

If one is interested in getting all the files related to one sample

    E126 <- query(ah , c("EpigenomeRoadMap", "E126", "H3K4ME2"))
    E126

    ## AnnotationHub with 6 records
    ## # snapshotDate(): 2017-10-27
    ## # $dataprovider: BroadInstitute
    ## # $species: Homo sapiens
    ## # $rdataclass: BigWigFile, GRanges
    ## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
    ## #   rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
    ## # retrieve records with, e.g., 'object[["AH29817"]]'
    ##
    ##             title
    ##   AH29817 | E126-H3K4me2.broadPeak.gz
    ##   AH30868 | E126-H3K4me2.narrowPeak.gz
    ##   AH31801 | E126-H3K4me2.gappedPeak.gz
    ##   AH32990 | E126-H3K4me2.fc.signal.bigwig
    ##   AH34022 | E126-H3K4me2.pval.signal.bigwig
    ##   AH40177 | E126-H3K4me2.imputed.pval.signal.bigwig

Hub resources can also be selected using `$`, `subset()`, and `display()`; see the main AnnotationHub vignette for additional detail.

Hub resources are imported as the appropriate Bioconductor object for use in further analysis. For example, peak files are returned as GRanges objects.

    ## require("rtracklayer")

    ## loading from cache '/home/biocbuild//.AnnotationHub/35257'

    peaks <- E126[['AH29817']]

    ## loading from cache '/home/biocbuild//.AnnotationHub/35257'

    seqinfo(peaks)

    ## Seqinfo object with 93 sequences (1 circular) from hg19 genome:
    ##   seqnames       seqlengths isCircular genome
    ##   chr1            249250621      FALSE   hg19
    ##   chr2            243199373      FALSE   hg19
    ##   chr3            198022430      FALSE   hg19
    ##   chr4            191154276      FALSE   hg19
    ##   chr5            180915260      FALSE   hg19
    ##   ...                   ...        ...    ...
    ##   chrUn_gl000245      36651      FALSE   hg19
    ##   chrUn_gl000246      38154      FALSE   hg19
    ##   chrUn_gl000247      36422      FALSE   hg19
    ##   chrUn_gl000248      39786      FALSE   hg19
    ##   chrUn_gl000249      38502      FALSE   hg19

BigWig files are returned as BigWigFile objects. A BigWigFile is a reference to a file on disk; the data in the file can be read in using `rtracklayer::import()`, perhaps querying these large files for particular genomic regions of interest as described on the help page `?import.bw`.

Each record inside AnnotationHub is associated with a unique identifier. Most GRanges objects returned by AnnotationHub contain the unique AnnotationHub identifier of the resource from which the GRanges is derived. This can come handy when working with the GRanges object for a while, and additional information about the object (e.g., the name of the file in the cache, or the original sourceurl for the data underlying the resource) that is being worked with.

    metadata(peaks)

    ## $AnnotationHubName
    ## [1] "AH29817"
    ##
    ## $`File Name`
    ## [1] "E126-H3K4me2.broadPeak.gz"
    ##
    ## $`Data Source`
    ## [1] "http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/E126-H3K4me2.broadPeak.gz"
    ##
    ## $Provider
    ## [1] "BroadInstitute"
    ##
    ## $Organism
    ## [1] "Homo sapiens"
    ##
    ## $`Taxonomy ID`
    ## [1] 9606

    ah[metadata(peaks)$AnnotationHubName]$sourceurl

    ## [1] "http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/E126-H3K4me2.broadPeak.gz"

## 1.3 Ensembl GTF and FASTA files for TxDb gene models and sequence queries

Bioconductor represents gene models using ‘transcript’ databases. These are available via packages such as TxDb.Hsapiens.UCSC.hg38.knownGene or can be constructed using functions such as GenomicFeatures::`makeTxDbFromBiomart()`.

AnnotationHub provides an easy way to work with gene models published by Ensembl. Let’s see what Ensembl’s Release-80 has in terms of data for pufferfish, Takifugu rubripes.

    query(ah, c("Takifugu", "release-80"))

    ## AnnotationHub with 7 records
    ## # snapshotDate(): 2017-10-27
    ## # $dataprovider: Ensembl
    ## # $species: Takifugu rubripes
    ## # $rdataclass: FaFile, GRanges
    ## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
    ## #   rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
    ## # retrieve records with, e.g., 'object[["AH47101"]]'
    ##
    ##             title
    ##   AH47101 | Takifugu_rubripes.FUGU4.80.gtf
    ##   AH47475 | Takifugu_rubripes.FUGU4.cdna.all.fa
    ##   AH47476 | Takifugu_rubripes.FUGU4.dna_rm.toplevel.fa
    ##   AH47477 | Takifugu_rubripes.FUGU4.dna_sm.toplevel.fa
    ##   AH47478 | Takifugu_rubripes.FUGU4.dna.toplevel.fa
    ##   AH47479 | Takifugu_rubripes.FUGU4.ncrna.fa
    ##   AH47480 | Takifugu_rubripes.FUGU4.pep.all.fa

We see that there is a GTF file descrbing gene models, as well as various DNA sequences. Let’s retrieve the GTF and top-level DNA sequence files. The GTF file is imported as a GRanges instance, the DNA sequence as a compressed, indexed Fasta file

    gtf <- ah[["AH47101"]]

    ## loading from cache '/home/biocbuild//.AnnotationHub/52579'

    ## using guess work to populate seqinfo

    dna <- ah[["AH47477"]]

    ## loading from cache '/home/biocbuild//.AnnotationHub/53323'
    ##     '/home/biocbuild//.AnnotationHub/53324'

    head(gtf, 3)

    ## GRanges object with 3 ranges and 19 metadata columns:
    ##         seqnames         ranges strand |   source       type     score     phase            gene_id
    ##            <Rle>      <IRanges>  <Rle> | <factor>   <factor> <numeric> <integer>        <character>
    ##   [1] scaffold_1 [10422, 11354]      - |  ensembl       gene      <NA>      <NA> ENSTRUG00000003702
    ##   [2] scaffold_1 [10422, 11354]      - |  ensembl transcript      <NA>      <NA> ENSTRUG00000003702
    ##   [3] scaffold_1 [10422, 11354]      - |  ensembl       exon      <NA>      <NA> ENSTRUG00000003702
    ##       gene_version gene_source   gene_biotype      transcript_id transcript_version
    ##          <numeric> <character>    <character>        <character>          <numeric>
    ##   [1]            1     ensembl protein_coding               <NA>               <NA>
    ##   [2]            1     ensembl protein_coding ENSTRUT00000008740                  1
    ##   [3]            1     ensembl protein_coding ENSTRUT00000008740                  1
    ##       transcript_source transcript_biotype exon_number            exon_id exon_version  protein_id
    ##             <character>        <character>   <numeric>        <character>    <numeric> <character>
    ##   [1]              <NA>               <NA>        <NA>               <NA>         <NA>        <NA>
    ##   [2]           ensembl     protein_coding        <NA>               <NA>         <NA>        <NA>
    ##   [3]           ensembl     protein_coding           1 ENSTRUE00000055472            1        <NA>
    ##       protein_version   gene_name transcript_name
    ##             <numeric> <character>     <character>
    ##   [1]            <NA>        <NA>            <NA>
    ##   [2]            <NA>        <NA>            <NA>
    ##   [3]            <NA>        <NA>            <NA>
    ##   -------
    ##   seqinfo: 2056 sequences (1 circular) from FUGU4 genome; no seqlengths

    dna

    ## class: FaFile
    ## path: /home/biocbuild//.AnnotationHub/53323
    ## index: /home/biocbuild//.AnnotationHub/53324
    ## isOpen: FALSE
    ## yieldSize: NA

    head(seqlevels(dna))

    ## [1] "scaffold_1" "scaffold_2" "scaffold_3" "scaffold_4" "scaffold_5" "scaffold_6"

Let’s identify the 25 longest DNA sequences, and keep just the annotations on these scaffolds.

    keep <- names(tail(sort(seqlengths(dna)), 25))
    gtf_subset <- gtf[seqnames(gtf) %in% keep]

It is trivial to make a TxDb instance of this subset (or of the entire gtf)

    library(GenomicFeatures)         # for makeTxDbFromGRanges
    txdb <- makeTxDbFromGRanges(gtf_subset)

    ## Warning in .get_cds_IDX(type, phase): The "phase" metadata column contains non-NA values for features of type stop_codon. This
    ##   information was ignored.

and to use that in conjunction with the DNA sequences, e.g., to find exon sequences of all annotated genes.

    library(Rsamtools)               # for getSeq,FaFile-method
    exons <- exons(txdb)
    length(exons)

    ## [1] 66219

    getSeq(dna, exons)

    ##   A DNAStringSet instance of length 66219
    ##         width seq                                                               names
    ##     [1]    72 ATGGCCTATCAGTTGTACAGGAATACCACTC...CCTGCAGGAGAGTCTGGACGAGCTTATCCAG scaffold_1
    ##     [2]   105 ACTCAGCAGATCACCCCTCAGCTGGCTCTCC...TAATCGTGTCCGCAACCGTGTGAACTTCAGG scaffold_1
    ##     [3]   156 GGTTCTCTCAACACCTACCGGTTCTGTGACA...GAGCGAATCCATGCAAAACAAACTGGATAAA scaffold_1
    ##     [4]    88 CAAACCAATCTCCTCGCTGTCTCTTCTCGTT...CATCAGCCAGAGGGACGGATCATCTCAGGTT scaffold_1
    ##     [5]   271 AGACGAGATGAGTGAGGACGCATTCAACGCC...AACACAGTGTGGAGACTTCAGAGGACGCCAC scaffold_1
    ##     ...   ... ...
    ## [66215]    67 ACGACTGGATGACAACATCAGGACCGTGGTA...TCAGACCAATGTGGGTCAGGATGGCAGACAG scaffold_9
    ## [66216]    50 TCTTTGGCTAATATTGACGATGTGGTAAACAAGATTCGTCTGAAGATTCG                scaffold_9
    ## [66217]    81 GTATTTCCCAGCCAAGACCCGCTGGACAGGG...ATACATCAACACACTGTTTCCCACCGAGCAG scaffold_9
    ## [66218]    87 ATGATGGAGGATGAAGAATTTGAATTTGCGG...GACCCCAGAGGTGCAGCTAGCAATTGAACAG scaffold_9
    ## [66219]   213 GACGACATCCTCGTGTGGGGCCGCTCTAGGG...GACAGCTGCTGTTCGCCTGTTTCCCCCCCCC scaffold_9

There is a one-to-one mapping between the genomic ranges contained in `exons` and the DNA sequences returned by `getSeq()`.

Some difficulties arise when working with this partly assembled genome that require more advanced GenomicRanges skills, see the GenomicRanges vignettes, especially “GenomicRanges HOWTOs” and “An Introduction to GenomicRanges”.

## 1.4 liftOver to map between genome builds

Suppose we wanted to lift features from one genome build to another, e.g., because annotations were generated for hg19 but our experimental analysis used hg18. We know that UCSC provides ‘liftover’ files for mapping between genome builds.

In this example, we will take our broad Peak GRanges from E126 which comes from the ‘hg19’ genome, and lift over these features to their ‘hg38’ coordinates.

    chainfiles <- query(ah , c("hg38", "hg19", "chainfile"))
    chainfiles

    ## AnnotationHub with 2 records
    ## # snapshotDate(): 2017-10-27
    ## # $dataprovider: UCSC
    ## # $species: Homo sapiens
    ## # $rdataclass: ChainFile
    ## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
    ## #   rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
    ## # retrieve records with, e.g., 'object[["AH14108"]]'
    ##
    ##             title
    ##   AH14108 | hg38ToHg19.over.chain.gz
    ##   AH14150 | hg19ToHg38.over.chain.gz

We are interested in the file that lifts over features from hg19 to hg38 so lets download that using

    ## loading from cache '/home/biocbuild//.AnnotationHub/18245'

    chain <- chainfiles[['AH14150']]

    ## loading from cache '/home/biocbuild//.AnnotationHub/18245'

    chain

    ## Chain of length 25
    ## names(25): chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 ... chr18 chr19 chr20 chr21 chr22 chrX chrY chrM

Perform the liftOver operation using `rtracklayer::liftOver()`:

    library(rtracklayer)
    gr38 <- liftOver(peaks, chain)

This returns a GRangeslist; update the genome of the result to get the final result

    genome(gr38) <- "hg38"
    gr38

    ## GRangesList object of length 153266:
    ## [[1]]
    ## GRanges object with 1 range and 5 metadata columns:
    ##       seqnames               ranges strand |        name     score signalValue    pValue    qValue
    ##          <Rle>            <IRanges>  <Rle> | <character> <numeric>   <numeric> <numeric> <numeric>
    ##   [1]     chr1 [28667912, 28670147]      * |      Rank_1       189    10.55845  22.01316  18.99911
    ##
    ## [[2]]
    ## GRanges object with 1 range and 5 metadata columns:
    ##       seqnames               ranges strand |   name score signalValue   pValue   qValue
    ##   [1]     chr4 [54090990, 54092984]      * | Rank_2   188     8.11483 21.80441 18.80662
    ##
    ## [[3]]
    ## GRanges object with 1 range and 5 metadata columns:
    ##       seqnames               ranges strand |   name score signalValue   pValue   qValue
    ##   [1]    chr14 [75293392, 75296621]      * | Rank_3   180     8.89834 20.97714 18.02816
    ##
    ## ...
    ## <153263 more elements>
    ## -------
    ## seqinfo: 23 sequences from hg38 genome; no seqlengths

## 1.5 Working with dbSNP Variants

One may also be interested in working with common germline variants with evidence of medical interest. This information is available at NCBI.

Query the dbDNP files in the hub:

This returns a VcfFile which can be read in using `r Biocpkg("VariantAnnotation")`; because VCF files can be large, `readVcf()` supports several strategies for importing only relevant parts of the file (e.g., particular genomic locations, particular features of the variants), see `?readVcf` for additional information.

    variants <- readVcf(vcf, genome="hg19")
    variants

    ## class: CollapsedVCF
    ## dim: 111138 0
    ## rowRanges(vcf):
    ##   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
    ## info(vcf):
    ##   DataFrame with 58 columns: RS, RSPOS, RV, VP, GENEINFO, dbSNPBuildID, SAO, SSR, WGT, VC, PM, T...
    ## info(header(vcf)):
    ##                 Number Type    Description
    ##    RS           1      Integer dbSNP ID (i.e. rs number)
    ##    RSPOS        1      Integer Chr position reported in dbSNP
    ##    RV           0      Flag    RS orientation is reversed
    ##    VP           1      String  Variation Property.  Documentation is at ftp://ftp.ncbi.nlm.nih.g...
    ##    GENEINFO     1      String  Pairs each of gene symbol:gene id.  The gene symbol and id are de...
    ##    dbSNPBuildID 1      Integer First dbSNP Build for RS
    ##    SAO          1      Integer Variant Allele Origin: 0 - unspecified, 1 - Germline, 2 - Somatic...
    ##    SSR          1      Integer Variant Suspect Reason Codes (may be more than one value added to...
    ##    WGT          1      Integer Weight, 00 - unmapped, 1 - weight 1, 2 - weight 2, 3 - weight 3 o...
    ##    VC           1      String  Variation Class
    ##    PM           0      Flag    Variant is Precious(Clinical,Pubmed Cited)
    ##    TPA          0      Flag    Provisional Third Party Annotation(TPA) (currently rs from PHARMG...
    ##    PMC          0      Flag    Links exist to PubMed Central article
    ##    S3D          0      Flag    Has 3D structure - SNP3D table
    ##    SLO          0      Flag    Has SubmitterLinkOut - From SNP->SubSNP->Batch.link_out
    ##    NSF          0      Flag    Has non-synonymous frameshift A coding region variation where one...
    ##    NSM          0      Flag    Has non-synonymous missense A coding region variation where one a...
    ##    NSN          0      Flag    Has non-synonymous nonsense A coding region variation where one a...
    ##    REF          0      Flag    Has reference A coding region variation where one allele in the s...
    ##    SYN          0      Flag    Has synonymous A coding region variation where one allele in the ...
    ##    U3           0      Flag    In 3' UTR Location is in an untranslated region (UTR). FxnCode = 53
    ##    U5           0      Flag    In 5' UTR Location is in an untranslated region (UTR). FxnCode = 55
    ##    ASS          0      Flag    In acceptor splice site FxnCode = 73
    ##    DSS          0      Flag    In donor splice-site FxnCode = 75
    ##    INT          0      Flag    In Intron FxnCode = 6
    ##    R3           0      Flag    In 3' gene region FxnCode = 13
    ##    R5           0      Flag    In 5' gene region FxnCode = 15
    ##    OTH          0      Flag    Has other variant with exactly the same set of mapped positions o...
    ##    CFL          0      Flag    Has Assembly conflict. This is for weight 1 and 2 variant that ma...
    ##    ASP          0      Flag    Is Assembly specific. This is set if the variant only maps to one...
    ##    MUT          0      Flag    Is mutation (journal citation, explicit fact): a low frequency va...
    ##    VLD          0      Flag    Is Validated.  This bit is set if the variant has 2+ minor allele...
    ##    G5A          0      Flag    >5% minor allele frequency in each and all populations
    ##    G5           0      Flag    >5% minor allele frequency in 1+ populations
    ##    HD           0      Flag    Marker is on high density genotyping kit (50K density or greater)...
    ##    GNO          0      Flag    Genotypes available. The variant has individual genotype (in SubI...
    ##    KGPhase1     0      Flag    1000 Genome phase 1 (incl. June Interim phase 1)
    ##    KGPhase3     0      Flag    1000 Genome phase 3
    ##    CDA          0      Flag    Variation is interrogated in a clinical diagnostic assay
    ##    LSD          0      Flag    Submitted from a locus-specific database
    ##    MTP          0      Flag    Microattribution/third-party annotation(TPA:GWAS,PAGE)
    ##    OM           0      Flag    Has OMIM/OMIA
    ##    NOC          0      Flag    Contig allele not present in variant allele list. The reference s...
    ##    WTD          0      Flag    Is Withdrawn by submitter If one member ss is withdrawn by submit...
    ##    NOV          0      Flag    Rs cluster has non-overlapping allele sets. True when rs set has ...
    ##    CAF          .      String  An ordered, comma delimited list of allele frequencies based on 1...
    ##    COMMON       1      Integer RS is a common SNP.  A common SNP is one that has at least one 10...
    ##    CLNHGVS      .      String  Variant names from HGVS.    The order of these variants correspon...
    ##    CLNALLE      .      Integer Variant alleles from REF or ALT columns.  0 is REF, 1 is the firs...
    ##    CLNSRC       .      String  Variant Clinical Chanels
    ##    CLNORIGIN    .      String  Allele Origin. One or more of the following values may be added: ...
    ##    CLNSRCID     .      String  Variant Clinical Channel IDs
    ##    CLNSIG       .      String  Variant Clinical Significance, 0 - Uncertain significance, 1 - no...
    ##    CLNDSDB      .      String  Variant disease database name
    ##    CLNDSDBID    .      String  Variant disease database ID
    ##    CLNDBN       .      String  Variant disease name
    ##    CLNREVSTAT   .      String  no_assertion - No assertion provided, no_criteria - No assertion ...
    ##    CLNACC       .      String  Variant Accession and Versions
    ## geno(vcf):
    ##   SimpleList of length 0:

`rowRanges()` returns information from the CHROM, POS and ID fields of the VCF file, represented as a GRanges instance

    rowRanges(variants)

    ## GRanges object with 111138 ranges and 5 metadata columns:
    ##               seqnames             ranges strand | paramRangeID            REF                ALT
    ##                  <Rle>          <IRanges>  <Rle> |     <factor> <DNAStringSet> <DNAStringSetList>
    ##   rs786201005        1 [1014143, 1014143]      * |         <NA>              C                  T
    ##   rs672601345        1 [1014316, 1014316]      * |         <NA>              C                 CG
    ##   rs672601312        1 [1014359, 1014359]      * |         <NA>              G                  T
    ##   rs115173026        1 [1020217, 1020217]      * |         <NA>              G                  T
    ##   rs201073369        1 [1020239, 1020239]      * |         <NA>              G                  C
    ##           ...      ...                ...    ... .          ...            ...                ...
    ##   rs527236200       MT     [15943, 15943]      * |         <NA>              T                  C
    ##   rs118203890       MT     [15950, 15950]      * |         <NA>              G                  A
    ##   rs199474700       MT     [15965, 15965]      * |         <NA>              A                  G
    ##   rs199474701       MT     [15967, 15967]      * |         <NA>              G                  A
    ##   rs199474699       MT     [15990, 15990]      * |         <NA>              C                  T
    ##                    QUAL      FILTER
    ##               <numeric> <character>
    ##   rs786201005      <NA>           .
    ##   rs672601345      <NA>           .
    ##   rs672601312      <NA>           .
    ##   rs115173026      <NA>           .
    ##   rs201073369      <NA>           .
    ##           ...       ...         ...
    ##   rs527236200      <NA>           .
    ##   rs118203890      <NA>           .
    ##   rs199474700      <NA>           .
    ##   rs199474701      <NA>           .
    ##   rs199474699      <NA>           .
    ##   -------
    ##   seqinfo: 25 sequences from hg19 genome; no seqlengths

Note that the broadPeaks files follow the UCSC chromosome naming convention, and the vcf data follows the NCBI style of chromosome naming convention. To bring these ranges in the same chromosome naming convention (ie UCSC), we would use

    seqlevelsStyle(variants) <-seqlevelsStyle(peaks)

And then finally to find which variants overlap these broadPeaks we would use:

    overlap <- findOverlaps(variants, peaks)
    overlap

    ## Hits object with 10904 hits and 0 metadata columns:
    ##           queryHits subjectHits
    ##           <integer>   <integer>
    ##       [1]        35       20333
    ##       [2]        36       20333
    ##       [3]        37       20333
    ##       [4]        38       20333
    ##       [5]        41        7733
    ##       ...       ...         ...
    ##   [10900]    110761       21565
    ##   [10901]    110762       21565
    ##   [10902]    110763       21565
    ##   [10903]    110764       21565
    ##   [10904]    110765       21565
    ##   -------
    ##   queryLength: 111138 / subjectLength: 153266

Some insight into how these results can be interpretted comes from looking a particular peak, e.g., the 3852nd peak

    idx <- subjectHits(overlap) == 3852
    overlap[idx]

    ## Hits object with 39 hits and 0 metadata columns:
    ##        queryHits subjectHits
    ##        <integer>   <integer>
    ##    [1]    102896        3852
    ##    [2]    102897        3852
    ##    [3]    102898        3852
    ##    [4]    102899        3852
    ##    [5]    102900        3852
    ##    ...       ...         ...
    ##   [35]    102930        3852
    ##   [36]    102931        3852
    ##   [37]    102932        3852
    ##   [38]    102933        3852
    ##   [39]    102934        3852
    ##   -------
    ##   queryLength: 111138 / subjectLength: 153266

There are three variants overlapping this peak; the coordinates of the peak and the overlapping variants are

    peaks[3852]

    ## GRanges object with 1 range and 5 metadata columns:
    ##       seqnames               ranges strand |        name     score signalValue    pValue    qValue
    ##          <Rle>            <IRanges>  <Rle> | <character> <numeric>   <numeric> <numeric> <numeric>
    ##   [1]    chr22 [50622494, 50626143]      * |   Rank_3852        79     6.06768  10.18943   7.99818
    ##   -------
    ##   seqinfo: 93 sequences (1 circular) from hg19 genome

    rowRanges(variants)[queryHits(overlap[idx])]

    ## GRanges object with 39 ranges and 5 metadata columns:
    ##               seqnames               ranges strand | paramRangeID            REF                ALT
    ##                  <Rle>            <IRanges>  <Rle> |     <factor> <DNAStringSet> <DNAStringSetList>
    ##     rs6151429    chr22 [50625049, 50625049]      * |         <NA>              T                  C
    ##     rs6151428    chr22 [50625182, 50625182]      * |         <NA>              C                A,T
    ##   rs774153480    chr22 [50625182, 50625182]      * |         <NA>              C           CG,CGGGG
    ##   rs199476388    chr22 [50625204, 50625204]      * |         <NA>              A                C,G
    ##    rs74315482    chr22 [50625213, 50625213]      * |         <NA>              G                  A
    ##           ...      ...                  ...    ... .          ...            ...                ...
    ##   rs199476369    chr22 [50625936, 50625936]      * |         <NA>              C                  G
    ##     rs2071421    chr22 [50625988, 50625988]      * |         <NA>              T                  C
    ##    rs74315475    chr22 [50626033, 50626033]      * |         <NA>              T                  A
    ##   rs398123419    chr22 [50626052, 50626052]      * |         <NA>              C                  A
    ##   rs398123418    chr22 [50626057, 50626057]      * |         <NA>              G                  A
    ##                    QUAL      FILTER
    ##               <numeric> <character>
    ##     rs6151429      <NA>           .
    ##     rs6151428      <NA>           .
    ##   rs774153480      <NA>           .
    ##   rs199476388      <NA>           .
    ##    rs74315482      <NA>           .
    ##           ...       ...         ...
    ##   rs199476369      <NA>           .
    ##     rs2071421      <NA>           .
    ##    rs74315475      <NA>           .
    ##   rs398123419      <NA>           .
    ##   rs398123418      <NA>           .
    ##   -------
    ##   seqinfo: 25 sequences from hg19 genome; no seqlengths

# 2 sessionInfo

    sessionInfo()

    ## R version 3.4.2 (2017-09-28)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 16.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8
    ##  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C
    ## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ##  [1] BSgenome.Hsapiens.UCSC.hg19_1.4.0 BSgenome_1.46.0
    ##  [3] rtracklayer_1.38.0                VariantAnnotation_1.24.1
    ##  [5] SummarizedExperiment_1.8.0        DelayedArray_0.4.1
    ##  [7] matrixStats_0.52.2                Rsamtools_1.30.0
    ##  [9] Biostrings_2.46.0                 XVector_0.18.0
    ## [11] GenomicFeatures_1.30.0            AnnotationDbi_1.40.0
    ## [13] Biobase_2.38.0                    GenomicRanges_1.30.0
    ## [15] GenomeInfoDb_1.14.0               IRanges_2.12.0
    ## [17] S4Vectors_0.16.0                  AnnotationHub_2.10.1
    ## [19] BiocGenerics_0.24.0               BiocStyle_2.6.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] progress_1.1.2                lattice_0.20-35               htmltools_0.3.6
    ##  [4] yaml_2.1.14                   interactiveDisplayBase_1.16.0 blob_1.1.0
    ##  [7] XML_3.98-1.9                  rlang_0.1.4                   DBI_0.7
    ## [10] BiocParallel_1.12.0           bit64_0.9-7                   GenomeInfoDbData_0.99.1
    ## [13] stringr_1.2.0                 zlibbioc_1.24.0               memoise_1.1.0
    ## [16] evaluate_0.10.1               knitr_1.17                    biomaRt_2.34.0
    ## [19] httpuv_1.3.5                  BiocInstaller_1.28.0          curl_3.0
    ## [22] Rcpp_0.12.13                  xtable_1.8-2                  backports_1.1.1
    ## [25] mime_0.5                      bit_1.1-12                    RMySQL_0.10.13
    ## [28] digest_0.6.12                 stringi_1.1.5                 bookdown_0.5
    ## [31] shiny_1.0.5                   grid_3.4.2                    rprojroot_1.2
    ## [34] tools_3.4.2                   bitops_1.0-6                  magrittr_1.5
    ## [37] RCurl_1.95-4.8                tibble_1.3.4                  RSQLite_2.0
    ## [40] pkgconfig_2.0.1               Matrix_1.2-11                 prettyunits_1.0.2
    ## [43] assertthat_0.2.0              rmarkdown_1.6                 httr_1.3.1
    ## [46] R6_2.2.2                      GenomicAlignments_1.14.0      compiler_3.4.2