# Querying protein features

Johannes Rainer

#### 6 May 2018

#### Package

ensembldb 2.4.1

# 1 Introduction

From Bioconductor release 3.5 on, `EnsDb` databases/packages created by the `ensembldb` package contain also, for transcripts with a coding regions, mappings between transcripts and proteins. Thus, in addition to the RNA/DNA-based features also the following protein related information is available:

- `protein_id`: the Ensembl protein ID. This is the primary ID for the proteins defined in Ensembl and each (protein coding) Ensembl transcript has one protein ID assigned to it.
- `protein_sequence`: the amino acid sequence of a protein.
- `uniprot_id`: the Uniprot ID for a protein. Note that not every Ensembl `protein_id` has an Uniprot ID, and each `protein_id` might be mapped to several `uniprot_id`. Also, the same Uniprot ID might be mapped to different `protein_id`.
- `uniprot_db`: the name of the Uniprot database in which the feature is annotated. Can be either SPTREMBL or SWISSPROT.
- `uniprot_mapping_type`: the type of the mapping method that was used to assign the Uniprot ID to the Ensembl protein ID.
- `protein_domain_id`: the ID of the protein domain according to the source/analysis in/by which is was defined.
- `protein_domain_source`: the source of the protein domain information, one of pfscan, scanprosite, superfamily, pfam, prints, smart, pirsf or tigrfam.
- `interpro_accession`: the Interpro accession ID of the protein domain (if available).
- `prot_dom_start`: the start of the protein domain within the sequence of the protein.
- `prot_dom_start`: the end position of the protein domain within the sequence of the protein.

Thus, for protein coding transcripts, these annotations can be fetched from the database too, given that protein annotations are available. Note that only `EnsDb` databases created through the Ensembl Perl API contain protein annotation, while databases created using `ensDbFromAH`, `ensDbFromGff`, `ensDbFromGRanges` and `ensDbFromGtf` don’t.

    library(ensembldb)
    library(EnsDb.Hsapiens.v86)
    edb <- EnsDb.Hsapiens.v86
    ## Evaluate whether we have protein annotation available
    hasProteinData(edb)

    ## [1] TRUE

If protein annotation is available, the additional tables and columns are also listed by the `listTables` and `listColumns` methods.

    listTables(edb)

    ## $gene
    ## [1] "gene_id"          "gene_name"        "gene_biotype"
    ## [4] "gene_seq_start"   "gene_seq_end"     "seq_name"
    ## [7] "seq_strand"       "seq_coord_system" "symbol"
    ##
    ## $tx
    ## [1] "tx_id"            "tx_biotype"       "tx_seq_start"
    ## [4] "tx_seq_end"       "tx_cds_seq_start" "tx_cds_seq_end"
    ## [7] "gene_id"          "tx_name"
    ##
    ## $tx2exon
    ## [1] "tx_id"    "exon_id"  "exon_idx"
    ##
    ## $exon
    ## [1] "exon_id"        "exon_seq_start" "exon_seq_end"
    ##
    ## $chromosome
    ## [1] "seq_name"    "seq_length"  "is_circular"
    ##
    ## $protein
    ## [1] "tx_id"            "protein_id"       "protein_sequence"
    ##
    ## $uniprot
    ## [1] "protein_id"           "uniprot_id"           "uniprot_db"
    ## [4] "uniprot_mapping_type"
    ##
    ## $protein_domain
    ## [1] "protein_id"            "protein_domain_id"     "protein_domain_source"
    ## [4] "interpro_accession"    "prot_dom_start"        "prot_dom_end"
    ##
    ## $entrezgene
    ## [1] "gene_id"  "entrezid"
    ##
    ## $metadata
    ## [1] "name"  "value"

In the following sections we show examples how to 1) fetch protein annotations as additional columns to gene/transcript annotations, 2) fetch protein annotation data and 3) map proteins to the genome.

# 2 Fetch protein annotation for genes and transcripts

Protein annotations for (protein coding) transcripts can be retrieved by simply adding the desired annotation columns to the `columns` parameter of the e.g. `genes` or `transcripts` methods.

    ## Get also protein information for ZBTB16 transcripts
    txs <- transcripts(edb, filter = GenenameFilter("ZBTB16"),
               columns = c("protein_id", "uniprot_id", "tx_biotype"))
    txs

    ## GRanges object with 11 ranges and 5 metadata columns:
    ##                   seqnames              ranges strand |      protein_id
    ##                      <Rle>           <IRanges>  <Rle> |     <character>
    ##   ENST00000335953       11 114059593-114250676      + | ENSP00000338157
    ##   ENST00000335953       11 114059593-114250676      + | ENSP00000338157
    ##   ENST00000541602       11 114059725-114189764      + |            <NA>
    ##   ENST00000544220       11 114059737-114063646      + | ENSP00000437716
    ##   ENST00000535700       11 114060257-114063744      + | ENSP00000443013
    ##   ENST00000392996       11 114060507-114250652      + | ENSP00000376721
    ##   ENST00000392996       11 114060507-114250652      + | ENSP00000376721
    ##   ENST00000539918       11 114064412-114247344      + | ENSP00000445047
    ##   ENST00000545851       11 114180766-114247296      + |            <NA>
    ##   ENST00000535379       11 114237207-114250557      + |            <NA>
    ##   ENST00000535509       11 114246790-114250476      + |            <NA>
    ##                    uniprot_id              tx_biotype           tx_id
    ##                   <character>             <character>     <character>
    ##   ENST00000335953      Q05516          protein_coding ENST00000335953
    ##   ENST00000335953  A0A024R3C6          protein_coding ENST00000335953
    ##   ENST00000541602        <NA>         retained_intron ENST00000541602
    ##   ENST00000544220      F5H6C3          protein_coding ENST00000544220
    ##   ENST00000535700      F5H5Y7          protein_coding ENST00000535700
    ##   ENST00000392996      Q05516          protein_coding ENST00000392996
    ##   ENST00000392996  A0A024R3C6          protein_coding ENST00000392996
    ##   ENST00000539918      H0YGW2 nonsense_mediated_decay ENST00000539918
    ##   ENST00000545851        <NA>    processed_transcript ENST00000545851
    ##   ENST00000535379        <NA>    processed_transcript ENST00000535379
    ##   ENST00000535509        <NA>         retained_intron ENST00000535509
    ##                     gene_name
    ##                   <character>
    ##   ENST00000335953      ZBTB16
    ##   ENST00000335953      ZBTB16
    ##   ENST00000541602      ZBTB16
    ##   ENST00000544220      ZBTB16
    ##   ENST00000535700      ZBTB16
    ##   ENST00000392996      ZBTB16
    ##   ENST00000392996      ZBTB16
    ##   ENST00000539918      ZBTB16
    ##   ENST00000545851      ZBTB16
    ##   ENST00000535379      ZBTB16
    ##   ENST00000535509      ZBTB16
    ##   -------
    ##   seqinfo: 1 sequence from GRCh38 genome

The gene ZBTB16 has protein coding and non-coding transcripts, thus, we get the protein ID for the coding- and `NA` for the non-coding transcripts. Note also that we have a transcript targeted for nonsense mediated mRNA-decay with a protein ID associated with it, but no Uniprot ID.

    ## Subset to transcripts with tx_biotype other than protein_coding.
    txs[txs$tx_biotype != "protein_coding", c("uniprot_id", "tx_biotype",
                          "protein_id")]

    ## GRanges object with 5 ranges and 3 metadata columns:
    ##                   seqnames              ranges strand |  uniprot_id
    ##                      <Rle>           <IRanges>  <Rle> | <character>
    ##   ENST00000541602       11 114059725-114189764      + |        <NA>
    ##   ENST00000539918       11 114064412-114247344      + |      H0YGW2
    ##   ENST00000545851       11 114180766-114247296      + |        <NA>
    ##   ENST00000535379       11 114237207-114250557      + |        <NA>
    ##   ENST00000535509       11 114246790-114250476      + |        <NA>
    ##                                tx_biotype      protein_id
    ##                               <character>     <character>
    ##   ENST00000541602         retained_intron            <NA>
    ##   ENST00000539918 nonsense_mediated_decay ENSP00000445047
    ##   ENST00000545851    processed_transcript            <NA>
    ##   ENST00000535379    processed_transcript            <NA>
    ##   ENST00000535509         retained_intron            <NA>
    ##   -------
    ##   seqinfo: 1 sequence from GRCh38 genome

While the mapping from a protein coding transcript to a Ensembl protein ID (column `protein_id`) is 1:1, the mapping between `protein_id` and `uniprot_id` can be n:m, i.e. each Ensembl protein ID can be mapped to 1 or more Uniprot IDs and each Uniprot ID can be mapped to more than one `protein_id` (and hence `tx_id`). This should be kept in mind if querying transcripts from the database fetching Uniprot related additional columns or even protein ID features, as in such cases a redundant list of transcripts is returned.

    ## List the protein IDs and uniprot IDs for the coding transcripts
    mcols(txs[txs$tx_biotype == "protein_coding",
          c("tx_id", "protein_id", "uniprot_id")])

    ## DataFrame with 6 rows and 3 columns
    ##             tx_id      protein_id  uniprot_id
    ##       <character>     <character> <character>
    ## 1 ENST00000335953 ENSP00000338157      Q05516
    ## 2 ENST00000335953 ENSP00000338157  A0A024R3C6
    ## 3 ENST00000544220 ENSP00000437716      F5H6C3
    ## 4 ENST00000535700 ENSP00000443013      F5H5Y7
    ## 5 ENST00000392996 ENSP00000376721      Q05516
    ## 6 ENST00000392996 ENSP00000376721  A0A024R3C6

Some of the n:m mappings for Uniprot IDs can be resolved by restricting either to entries from one Uniprot database (SPTREMBL or SWISSPROT) or to mappings of a certain type of mapping method. The corresponding filters are the `UniprotDbFilter` and the `UniprotMappingTypeFilter` (using the `uniprot_db` and `uniprot_mapping_type` columns of the `uniprot` database table). In the example below we restrict the result to Uniprot IDs with the mapping type DIRECT.

    ## List all uniprot mapping types in the database.
    listUniprotMappingTypes(edb)

    ## [1] "DIRECT"         "SEQUENCE_MATCH"

    ## Get all protein_coding transcripts of ZBTB16 along with their protein_id
    ## and Uniprot IDs, restricting to protein_id to uniprot_id mappings based
    ## on "DIRECT" mapping methods.
    txs <- transcripts(edb, filter = list(GenenameFilter("ZBTB16"),
                          UniprotMappingTypeFilter("DIRECT")),
               columns = c("protein_id", "uniprot_id", "uniprot_db"))
    mcols(txs)

    ## DataFrame with 5 rows and 6 columns
    ##        protein_id  uniprot_id  uniprot_db           tx_id   gene_name
    ##       <character> <character> <character>     <character> <character>
    ## 1 ENSP00000338157      Q05516   SWISSPROT ENST00000335953      ZBTB16
    ## 2 ENSP00000437716      F5H6C3    SPTREMBL ENST00000544220      ZBTB16
    ## 3 ENSP00000443013      F5H5Y7    SPTREMBL ENST00000535700      ZBTB16
    ## 4 ENSP00000376721      Q05516   SWISSPROT ENST00000392996      ZBTB16
    ## 5 ENSP00000445047      H0YGW2    SPTREMBL ENST00000539918      ZBTB16
    ##   uniprot_mapping_type
    ##            <character>
    ## 1               DIRECT
    ## 2               DIRECT
    ## 3               DIRECT
    ## 4               DIRECT
    ## 5               DIRECT

For this example the use of the `UniprotMappingTypeFilter` resolved the multiple mapping of Uniprot IDs to Ensembl protein IDs, but the Uniprot ID Q05516 is still assigned to the two Ensembl protein IDs ENSP00000338157 and ENSP00000376721.

All protein annotations can also be added as metadata columns to the results of the `genes`, `exons`, `exonsBy`, `transcriptsBy`, `cdsBy`, `fiveUTRsByTranscript` and `threeUTRsByTranscript` methods by specifying the desired column names with the `columns` parameter. For non coding transcripts `NA` will be reported in the protein annotation columns.

In addition to retrieve protein annotations from the database, we can also use protein data to filter the results. In the example below we fetch for example all genes from the database that have a certain protein domain in the protein encoded by any of its transcripts.

    ## Get all genes encoded on chromosome 11 which protein contains
    ## a certain protein domain.
    gns <- genes(edb, filter = ~ prot_dom_id == "PS50097" & seq_name == "11")
    length(gns)

    ## [1] 9

    sort(gns$gene_name)

    ## [1] "ABTB2"  "BTBD18" "KBTBD3" "KBTBD4" "KCTD21" "KLHL35" "ZBTB16" "ZBTB3"
    ## [9] "ZBTB44"

So, in total we got 152 genes with that protein domain. In addition to the `ProtDomIdFilter`, also the `ProteinidFilter` and the `UniprotidFilter` can be used to query the database for entries matching conditions on their protein ID or Uniprot ID.

# 3 Use methods from the `AnnotationDbi` package to query protein annotation

The `select`, `keys` and `mapIds` methods from the `AnnotationDbi` package can also be used to query `EnsDb` objects for protein annotations. Supported columns and key types are returned by the `columns` and `keytypes` methods.

    ## Show all columns that are provided by the database
    columns(edb)

    ##  [1] "ENTREZID"            "EXONID"              "EXONIDX"
    ##  [4] "EXONSEQEND"          "EXONSEQSTART"        "GENEBIOTYPE"
    ##  [7] "GENEID"              "GENENAME"            "GENESEQEND"
    ## [10] "GENESEQSTART"        "INTERPROACCESSION"   "ISCIRCULAR"
    ## [13] "PROTDOMEND"          "PROTDOMSTART"        "PROTEINDOMAINID"
    ## [16] "PROTEINDOMAINSOURCE" "PROTEINID"           "PROTEINSEQUENCE"
    ## [19] "SEQCOORDSYSTEM"      "SEQLENGTH"           "SEQNAME"
    ## [22] "SEQSTRAND"           "SYMBOL"              "TXBIOTYPE"
    ## [25] "TXCDSSEQEND"         "TXCDSSEQSTART"       "TXID"
    ## [28] "TXNAME"              "TXSEQEND"            "TXSEQSTART"
    ## [31] "UNIPROTDB"           "UNIPROTID"           "UNIPROTMAPPINGTYPE"

    ## Show all key types/filters that are supported
    keytypes(edb)

    ##  [1] "ENTREZID"            "EXONID"              "GENEBIOTYPE"
    ##  [4] "GENEID"              "GENENAME"            "PROTDOMID"
    ##  [7] "PROTEINDOMAINID"     "PROTEINDOMAINSOURCE" "PROTEINID"
    ## [10] "SEQNAME"             "SEQSTRAND"           "SYMBOL"
    ## [13] "TXBIOTYPE"           "TXID"                "TXNAME"
    ## [16] "UNIPROTID"

Below we fetch all Uniprot IDs annotated to the gene ZBTB16.

    select(edb, keys = "ZBTB16", keytype = "GENENAME",
           columns = "UNIPROTID")

    ##   GENENAME  UNIPROTID
    ## 1   ZBTB16     Q05516
    ## 2   ZBTB16 A0A024R3C6
    ## 3   ZBTB16       <NA>
    ## 4   ZBTB16     F5H6C3
    ## 5   ZBTB16     F5H5Y7
    ## 6   ZBTB16     H0YGW2

This returns us all Uniprot IDs of all proteins encoded by the gene’s transcripts. One of the transcripts from ZBTB16, while having a CDS and being annotated to a protein, does not have an Uniprot ID assigned (thus `NA` is returned by the above call). As we see below, this transcript is targeted for non sense mediated mRNA decay.

    ## Call select, this time providing a GenenameFilter.
    select(edb, keys = GenenameFilter("ZBTB16"),
           columns = c("TXBIOTYPE", "UNIPROTID", "PROTEINID"))

    ##                 TXBIOTYPE  UNIPROTID       PROTEINID GENENAME
    ## 1          protein_coding     Q05516 ENSP00000338157   ZBTB16
    ## 2          protein_coding A0A024R3C6 ENSP00000338157   ZBTB16
    ## 3         retained_intron       <NA>            <NA>   ZBTB16
    ## 4          protein_coding     F5H6C3 ENSP00000437716   ZBTB16
    ## 5          protein_coding     F5H5Y7 ENSP00000443013   ZBTB16
    ## 6          protein_coding     Q05516 ENSP00000376721   ZBTB16
    ## 7          protein_coding A0A024R3C6 ENSP00000376721   ZBTB16
    ## 8 nonsense_mediated_decay     H0YGW2 ENSP00000445047   ZBTB16
    ## 9    processed_transcript       <NA>            <NA>   ZBTB16

Note also that we passed this time a `GenenameFilter` with the `keys` parameter.

# 4 Retrieve proteins from the database

Proteins can be fetched using the dedicated `proteins` method that returns, unlike DNA/RNA-based methods like `genes` or `transcripts`, not a `GRanges` object by default, but a `DataFrame` object. Alternatively, results can be returned as a `data.frame` or as an `AAStringSet` object from the `Biobase` package. Note that this might change in future releases if a more appropriate object to represent protein annotations becomes available.

In the code chunk below we fetch all protein annotations for the gene ZBTB16.

    ## Get all proteins and return them as an AAStringSet
    prts <- proteins(edb, filter = GenenameFilter("ZBTB16"),
             return.type = "AAStringSet")
    prts

    ##   A AAStringSet instance of length 5
    ##     width seq                                            names
    ## [1]   673 MDLTKMGMIQLQNPSHPTGLLC...KPEEIPPDWRIEKTYLYLCYV ENSP00000338157
    ## [2]   673 MDLTKMGMIQLQNPSHPTGLLC...KPEEIPPDWRIEKTYLYLCYV ENSP00000376721
    ## [3]   115 MDLTKMGMIQLQNPSHPTGLLC...KAEDLDDLLYAAEILEIEYLE ENSP00000437716
    ## [4]   148 MDLTKMGMIQLQNPSHPTGLLC...SDDNDTEATMADGGAEEEEDR ENSP00000443013
    ## [5]    55 XGGLLPQGFIQRELFSKLGELA...QCSVCGVELPDNEAVEQHRVF ENSP00000445047

Besides the amino acid sequence, the `prts` contains also additional annotations that can be accessed with the `mcols` method (metadata columns). All additional columns provided with the parameter `columns` are also added to the `mcols` `DataFrame`.

    mcols(prts)

    ## DataFrame with 5 rows and 3 columns
    ##             tx_id      protein_id   gene_name
    ##       <character>     <character> <character>
    ## 1 ENST00000335953 ENSP00000338157      ZBTB16
    ## 2 ENST00000392996 ENSP00000376721      ZBTB16
    ## 3 ENST00000544220 ENSP00000437716      ZBTB16
    ## 4 ENST00000535700 ENSP00000443013      ZBTB16
    ## 5 ENST00000539918 ENSP00000445047      ZBTB16

Note that the `proteins` method will retrieve only gene/transcript annotations of transcripts encoding a protein. Thus annotations for the non-coding transcripts of the gene ZBTB16, that were returned by calls to `genes` or `transcripts` in the previous section are not fetched.

Querying in addition Uniprot identifiers or protein domain data will result at present in a redundant list of proteins as shown in the code block below.

    ## Get also protein domain annotations in addition to the protein annotations.
    pd <- proteins(edb, filter = GenenameFilter("ZBTB16"),
               columns = c("tx_id", listColumns(edb, "protein_domain")),
               return.type = "AAStringSet")
    pd

    ##   A AAStringSet instance of length 81
    ##      width seq                                           names
    ##  [1]   673 MDLTKMGMIQLQNPSHPTGLL...KPEEIPPDWRIEKTYLYLCYV ENSP00000338157
    ##  [2]   673 MDLTKMGMIQLQNPSHPTGLL...KPEEIPPDWRIEKTYLYLCYV ENSP00000338157
    ##  [3]   673 MDLTKMGMIQLQNPSHPTGLL...KPEEIPPDWRIEKTYLYLCYV ENSP00000338157
    ##  [4]   673 MDLTKMGMIQLQNPSHPTGLL...KPEEIPPDWRIEKTYLYLCYV ENSP00000338157
    ##  [5]   673 MDLTKMGMIQLQNPSHPTGLL...KPEEIPPDWRIEKTYLYLCYV ENSP00000338157
    ##  ...   ... ...
    ## [77]   148 MDLTKMGMIQLQNPSHPTGLL...SDDNDTEATMADGGAEEEEDR ENSP00000443013
    ## [78]   148 MDLTKMGMIQLQNPSHPTGLL...SDDNDTEATMADGGAEEEEDR ENSP00000443013
    ## [79]   148 MDLTKMGMIQLQNPSHPTGLL...SDDNDTEATMADGGAEEEEDR ENSP00000443013
    ## [80]   148 MDLTKMGMIQLQNPSHPTGLL...SDDNDTEATMADGGAEEEEDR ENSP00000443013
    ## [81]    55 XGGLLPQGFIQRELFSKLGEL...QCSVCGVELPDNEAVEQHRVF ENSP00000445047

The result contains one row/element for each protein domain in each of the proteins. The number of protein domains per protein and the `mcols` are shown below.

    ## The number of protein domains per protein:
    table(names(pd))

    ##
    ## ENSP00000338157 ENSP00000376721 ENSP00000437716 ENSP00000443013
    ##              36              36               4               4
    ## ENSP00000445047
    ##               1

    ## The mcols
    mcols(pd)

    ## DataFrame with 81 rows and 8 columns
    ##               tx_id      protein_id protein_domain_id protein_domain_source
    ##         <character>     <character>       <character>           <character>
    ## 1   ENST00000335953 ENSP00000338157           PS50157                pfscan
    ## 2   ENST00000335953 ENSP00000338157           PS50157                pfscan
    ## 3   ENST00000335953 ENSP00000338157           PS50157                pfscan
    ## 4   ENST00000335953 ENSP00000338157           PS50157                pfscan
    ## 5   ENST00000335953 ENSP00000338157           PS50157                pfscan
    ## ...             ...             ...               ...                   ...
    ## 77  ENST00000535700 ENSP00000443013           PS50097                pfscan
    ## 78  ENST00000535700 ENSP00000443013          SSF54695           superfamily
    ## 79  ENST00000535700 ENSP00000443013           PF00651                  pfam
    ## 80  ENST00000535700 ENSP00000443013           SM00225                 smart
    ## 81  ENST00000539918 ENSP00000445047                NA                    NA
    ##     interpro_accession prot_dom_start prot_dom_end   gene_name
    ##            <character>      <integer>    <integer> <character>
    ## 1            IPR007087            602          629      ZBTB16
    ## 2            IPR007087            490          517      ZBTB16
    ## 3            IPR007087            630          657      ZBTB16
    ## 4            IPR007087            432          459      ZBTB16
    ## 5            IPR007087            546          573      ZBTB16
    ## ...                ...            ...          ...         ...
    ## 77           IPR000210             34           96      ZBTB16
    ## 78           IPR011333              8          123      ZBTB16
    ## 79           IPR000210             25          124      ZBTB16
    ## 80           IPR000210             34          126      ZBTB16
    ## 81                  NA             NA           NA      ZBTB16

As we can see each protein can have several protein domains with the start and end coordinates within the amino acid sequence being reported in columns `prot_dom_start` and `prot_dom_end`. Also, not all Ensembl protein IDs, like `protein_id` ENSP00000445047 are mapped to an Uniprot ID or have protein domains.

# 5 Map peptide features within proteins to the genome

The coordinate-mapping.Rmd vignette provides a detailed description of all functions that allow to map between genomic, transcript and protein coordinates.

# 6 Session information

    sessionInfo()

    ## R version 3.5.0 (2018-04-23)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 16.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.7-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ##  [1] grid      stats4    parallel  stats     graphics  grDevices utils
    ##  [8] datasets  methods   base
    ##
    ## other attached packages:
    ##  [1] ggbio_1.28.0
    ##  [2] ggplot2_2.2.1
    ##  [3] magrittr_1.5
    ##  [4] BSgenome.Hsapiens.NCBI.GRCh38_1.3.1000
    ##  [5] BSgenome_1.48.0
    ##  [6] rtracklayer_1.40.1
    ##  [7] Biostrings_2.48.0
    ##  [8] XVector_0.20.0
    ##  [9] Gviz_1.24.0
    ## [10] EnsDb.Hsapiens.v86_2.99.0
    ## [11] ensembldb_2.4.1
    ## [12] AnnotationFilter_1.4.0
    ## [13] GenomicFeatures_1.32.0
    ## [14] AnnotationDbi_1.42.0
    ## [15] Biobase_2.40.0
    ## [16] GenomicRanges_1.32.2
    ## [17] GenomeInfoDb_1.16.0
    ## [18] IRanges_2.14.4
    ## [19] S4Vectors_0.18.1
    ## [20] BiocGenerics_0.26.0
    ## [21] BiocStyle_2.8.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] ProtGenerics_1.12.0         bitops_1.0-6
    ##  [3] matrixStats_0.53.1          bit64_0.9-7
    ##  [5] RColorBrewer_1.1-2          progress_1.1.2
    ##  [7] httr_1.3.1                  rprojroot_1.3-2
    ##  [9] tools_3.5.0                 backports_1.1.2
    ## [11] R6_2.2.2                    rpart_4.1-13
    ## [13] Hmisc_4.1-1                 DBI_1.0.0
    ## [15] lazyeval_0.2.1              colorspace_1.3-2
    ## [17] nnet_7.3-12                 gridExtra_2.3
    ## [19] prettyunits_1.0.2           GGally_1.3.2
    ## [21] bit_1.1-12                  curl_3.2
    ## [23] compiler_3.5.0              graph_1.58.0
    ## [25] htmlTable_1.11.2            DelayedArray_0.6.0
    ## [27] labeling_0.3                bookdown_0.7
    ## [29] scales_0.5.0                checkmate_1.8.5
    ## [31] RBGL_1.56.0                 stringr_1.3.0
    ## [33] digest_0.6.15               Rsamtools_1.32.0
    ## [35] foreign_0.8-70              rmarkdown_1.9
    ## [37] base64enc_0.1-3             dichromat_2.0-0
    ## [39] pkgconfig_2.0.1             htmltools_0.3.6
    ## [41] htmlwidgets_1.2             rlang_0.2.0
    ## [43] rstudioapi_0.7              RSQLite_2.1.1
    ## [45] BiocInstaller_1.30.0        BiocParallel_1.14.1
    ## [47] acepack_1.4.1               VariantAnnotation_1.26.0
    ## [49] RCurl_1.95-4.10             GenomeInfoDbData_1.1.0
    ## [51] Formula_1.2-3               Matrix_1.2-14
    ## [53] Rcpp_0.12.16                munsell_0.4.3
    ## [55] stringi_1.2.2               yaml_2.1.19
    ## [57] SummarizedExperiment_1.10.0 zlibbioc_1.26.0
    ## [59] plyr_1.8.4                  blob_1.1.1
    ## [61] lattice_0.20-35             splines_3.5.0
    ## [63] knitr_1.20                  pillar_1.2.2
    ## [65] reshape2_1.4.3              biomaRt_2.36.0
    ## [67] XML_3.98-1.11               evaluate_0.10.1
    ## [69] biovizBase_1.28.0           latticeExtra_0.6-28
    ## [71] data.table_1.11.0           gtable_0.2.0
    ## [73] reshape_0.8.7               assertthat_0.2.0
    ## [75] xfun_0.1                    survival_2.42-3
    ## [77] OrganismDbi_1.22.0          tibble_1.4.2
    ## [79] GenomicAlignments_1.16.0    memoise_1.1.0
    ## [81] cluster_2.0.7-1