Introduction to Bioconductor Annotation Resources

Bioc 2019: 24-27 June

Instructors

James W. MacDonald (jmacdon@umich.edu)
Lori Shepherd (lori.shepherd@roswellpark.org)

Workshop Description

There are various annotation packages provided by the Bioconductor project that can be used to incorporate additional information to results from high-throughput experiments. This can be as simple as mapping Ensembl IDs to corresponding HUGO gene symbols, to much more complex queries involving multiple data sources. In this workshop we will cover the various classes of annotation packages, what they contain, and how to use them efficiently.

Prerequisites

Basic knowledge of R syntax
Basic understanding of the various annotation sources (NCBI, EBI/EMBL)

Useful background reading

The AnnotationDbi vignette.
The biomaRt vignette.
The GenomicFeatures vignette.

Workshop Participation

After each type of annotation package is introduced, students will be given the opportunity to practice making their own queries.

R / Bioconductor packages used

AnnotationDbi
AnnotationHub
BSgenome
biomaRt
ensembldb
org.Hs.eg.db
TxDb.Hsapiens.UCSC.hg19.knownGene
EnsDb.Hsapiens.v79
EnsDb.Mmusculus.v79
Homo.sapiens
BSgenome.Hsapiens.UCSC.hg19
hugene20sttranscriptcluster.db

Workshop goals and objectives

Annotating data is a complex task. For any high-throughput experiment the analyst usually starts with a set of identifiers for each thing that was measured, and in order to make the results useful to collaborators these identifiers need to be mapped to other identifiers that are either more familiar to collaborators, or that can be used for further analyses. As an example, RNA-Seq data may only have Entrez Gene IDs for each gene measured, and as part of the output you may want to include the gene symbols, which are more likely to be familiar to a Biologist.

Learning goals

Understand what sort of annotation data are available
Understand the difference between annotation sources (NCBI and EBI/EMBL)
Gain familiarity with the various ways to query annotation packages
Get some practice making queries

Learning objectives

Be able to use select and mapIds to map between identifiers
Be able to extract data from TxDb and EnsDb packages
Be able to make queries using biomaRt
Extract and utilize various data from AnnotationHub

Annotation Workshop

What do we mean by annotation?

Map a known ID to other functional or positional information

Specific goal

We have data and statistics, and we want to add other useful information

The end result might be as simple as a data.frame or HTML table, or as complex as a RangedSummarizedExperiment

Data containers

ExpressionSet

load(file.path(system.file(package = "Bioc2019Anno", "extdata"), "eset.Rdata"))
eset

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 33552 features, 6 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: GSM2194079 GSM2194080 ... GSM2194084 (6 total)
##   varLabels: title characteristics_ch1.1
##   varMetadata: labelDescription
## featureData
##   featureNames: 16657436 16657440 ... 17118478 (33552 total)
##   fvarLabels: PROBEID ENTREZID SYMBOL GENENAME
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation: pd.hugene.2.0.st

ExpressionSet (continued)

head(exprs(eset))

##          GSM2194079 GSM2194080 GSM2194081 GSM2194082 GSM2194083 GSM2194084
## 16657436   8.505158   9.046577   8.382674   9.115481   8.715343   8.566301
## 16657440   7.948860   8.191222   7.901911   8.459781   8.191793   8.219658
## 16657450  10.932934  11.228553  10.948120  11.462231  11.300046  11.300886
## 16657469   9.172462   9.344630   9.193450   9.465584   9.464020   9.135715
## 16657473   6.222049   6.551035   6.000246   6.398798   5.892654   5.592125
## 16657476   8.514300   8.474073   8.407196   8.811238   8.780833   8.874606

head(pData(phenoData(eset)))

##                          title characteristics_ch1.1
## GSM2194079   SW620-miR625-rep1     shRNA: miR-625-3p
## GSM2194080   SW620-miR625-rep2     shRNA: miR-625-3p
## GSM2194081   SW620-miR625-rep3     shRNA: miR-625-3p
## GSM2194082 SW620-scramble-rep1       shRNA: scramble
## GSM2194083 SW620-scramble-rep2       shRNA: scramble
## GSM2194084 SW620-scramble-rep3       shRNA: scramble

ExpressionSet (continued)

head(pData(featureData(eset)))

##           PROBEID  ENTREZID      SYMBOL
## 16657436 16657436     84771     DDX11L2
## 16657440 16657440 100302278   MIR1302-2
## 16657450 16657450    402483   LINC01000
## 16657469 16657469    140849 LINC00266-1
## 16657473 16657473    729759      OR4F29
## 16657476 16657476    388574   RPL23AP87
##                                                   GENENAME
## 16657436                     DEAD/H-box helicase 11 like 2
## 16657440                                   microRNA 1302-2
## 16657450       long intergenic non-protein coding RNA 1000
## 16657469      long intergenic non-protein coding RNA 266-1
## 16657473 olfactory receptor family 4 subfamily F member 29
## 16657476              ribosomal protein L23a pseudogene 87

BioC containers vs basic structures

Pros

Validity checking
Subsetting
Function dispatch
Automatic behaviors

Cons

Difficult to create
Cumbersome to extract data by hand
Useful only within R

Annotation sources

Package type	Example
ChipDb	hugene20sttranscriptcluster.db
OrgDb	org.Hs.eg.db
TxDb/EnsDb	TxDb.Hsapiens.UCSC.hg19.knownGene; EnsDb.Hsapiens.v75
OrganismDb	Homo.sapiens
BSgenome	BSgenome.Hsapiens.UCSC.hg19
Others	GO.db; KEGG.db
AnnotationHub	Online resource
biomaRt	Online resource

Interacting with AnnoDb packages

The main function is select:

select(annopkg, keys, columns, keytype)

Where

annopkg is the annotation package
keys are the IDs that we know
columns are the values we want
keytype is the type of key used
- if the keytype is the central key, it can remain unspecified

Simple example

Say we have analyzed data from an Affymetrix Human Gene ST 2.0 array and want to know what the genes are. For purposes of this lab, we just select some IDs at random.

library(hugene20sttranscriptcluster.db)
set.seed(12345)
ids <- featureNames(eset)[sample(1:25000, 5)]
ids

## [1] "16858710" "17001289" "16894087" "16813565" "16951883"

select(hugene20sttranscriptcluster.db, ids, "SYMBOL")

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

##    PROBEID      SYMBOL
## 1 16858710        JUNB
## 2 17001289 PPP2R2B-IT1
## 3 16894087   LINC01247
## 4 16813565   NR2F2-AS1
## 5 16951883     OSBPL10

Questions!

How do you know what the central keys are?

If it’s a ChipDb, the central key are the manufacturer’s probe IDs
It’s sometimes in the name - org.Hs.eg.db, where ‘eg’ means Entrez Gene ID
You can see examples using e.g., head(keys(annopkg)), and infer from that
But note that it’s never necessary to know the central key, as long as you specify the keytype

Another example

There is one issue with select however.

ids <- c('16737401','16657436' ,'16678303')
select(hugene20sttranscriptcluster.db, ids, c("SYMBOL","MAP"))

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

##     PROBEID       SYMBOL     MAP
## 1  16737401        TRAF6   11p12
## 2  16657436      DDX11L1 1p36.33
## 3  16657436 LOC102725121 1p36.33
## 4  16657436      DDX11L2  2q14.1
## 5  16657436      DDX11L9 15q26.3
## 6  16657436     DDX11L10 16p13.3
## 7  16657436      DDX11L5  9p24.3
## 8  16657436     DDX11L16    Xq28
## 9  16657436     DDX11L16    Yq12
## 10 16678303         ARF1 1q42.13

The `mapIds` function

An alternative to select is mapIds, which gives control of duplicates

Same arguments as select with slight differences
- The columns argument can only specify one column
- The keytype argument must be specified
- An additional argument, multiVals used to control duplicates

mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID")

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

##  16737401  16657436  16678303 
##   "TRAF6" "DDX11L1"    "ARF1"

Choices for multiVals

Default is first, where we just choose the first of the duplicates. Other choices are list, CharacterList, filter, asNA or a user-specified function.

mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "list")

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

## $`16737401`
## [1] "TRAF6"
## 
## $`16657436`
## [1] "DDX11L1"      "LOC102725121" "DDX11L2"      "DDX11L9"     
## [5] "DDX11L10"     "DDX11L5"      "DDX11L16"    
## 
## $`16678303`
## [1] "ARF1"

Choices for multiVals (continued)

mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "CharacterList")

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

## CharacterList of length 3
## [["16737401"]] TRAF6
## [["16657436"]] DDX11L1 LOC102725121 DDX11L2 DDX11L9 DDX11L10 DDX11L5 DDX11L16
## [["16678303"]] ARF1

mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "filter")

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

## 16737401 16678303 
##  "TRAF6"   "ARF1"

mapIds(hugene20sttranscriptcluster.db, ids, "SYMBOL", "PROBEID", multiVals = "asNA")

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

## 16737401 16657436 16678303 
##  "TRAF6"       NA   "ARF1"

ChipDb/OrgDb questions

Using either the hugene20sttranscriptcluster.db or org.Hs.eg.db package,

What gene symbol corresponds to Entrez Gene ID 1000?
What is the Ensembl Gene ID for PPARG?
What is the UniProt ID for GAPDH?
How many of the probesets from the ExpressionSet (eset) we loaded map to a single gene? How many don’t map to a gene at all?

TxDb packages

TxDb packages contain positional information; the contents can be inferred by the package name

TxDb.Species.Source.Build.Table

TxDb.Hsapiens.UCSC.hg19.knownGene
- Homo sapiens
- UCSC genome browser
- hg19 (their version of GRCh37)
- knownGene table

TxDb.Dmelanogaster.UCSC.dm3.ensGene TxDb.Athaliana.BioMart.plantsmart22

EnsDb packages

EnsDb packages are similar to TxDb packages, but based on Ensembl mappings

EnsDb.Rnorvegicus.v79

Transcript packages

As with ChipDb and OrgDb packages, select and mapIds can be used to make queries

select(TxDb.Hsapiens.UCSC.hg19.knownGene, c("1","10"),
       c("TXNAME","TXCHROM","TXSTART","TXEND"), "GENEID")

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

##   GENEID     TXNAME TXCHROM  TXSTART    TXEND
## 1      1 uc002qsd.4   chr19 58858172 58864865
## 2      1 uc002qsf.2   chr19 58859832 58874214
## 3     10 uc003wyw.1    chr8 18248755 18258723

select(EnsDb.Hsapiens.v79, c("1", "10"),
       c("GENEID","GENENAME","SEQNAME","GENESEQSTART","GENESEQEND"), "ENTREZID")

##   ENTREZID          GENEID GENENAME SEQNAME GENESEQSTART GENESEQEND
## 1        1 ENSG00000121410     A1BG      19     58345178   58353499
## 2       10 ENSG00000156006     NAT2       8     18391245   18401218

But this is not how one normally uses them…

GRanges

The normal use case for transcript packages is to extract positional information into a GRanges or GRangesList object. An example is the genomic position of all genes:

gns <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
gns

## GRanges object with 23056 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
##     ...      ...                 ...    ... .         ...
##    9991     chr9 114979995-115095944      - |        9991
##    9992    chr21   35736323-35743440      + |        9992
##    9993    chr22   19023795-19109967      - |        9993
##    9994     chr6   90539619-90584155      + |        9994
##    9997    chr22   50961997-50964905      - |        9997
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

GRangesList

Or the genomic position of all transcripts by gene:

txs <- transcriptsBy(TxDb.Hsapiens.UCSC.hg19.knownGene)
txs

## GRangesList object of length 23459:
## $`1`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr19 58858172-58864865      - |     70455  uc002qsd.4
##   [2]    chr19 58859832-58874214      - |     70456  uc002qsf.2
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`10`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]     chr8 18248755-18258723      + |     31944  uc003wyw.1
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`100`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr20 43248163-43280376      - |     72132  uc002xmj.3
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## ...
## <23456 more elements>

Other accessors

Positional information can be extracted for transcripts, genes, coding sequences (cds), promoters and exons.

EnsDb.Rnorvegicus.v79

Transcript packages

As with ChipDb and OrgDb packages, select and mapIds can be used to make queries

select(TxDb.Hsapiens.UCSC.hg19.knownGene, c("1","10"),
       c("TXNAME","TXCHROM","TXSTART","TXEND"), "GENEID")

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

##   GENEID     TXNAME TXCHROM  TXSTART    TXEND
## 1      1 uc002qsd.4   chr19 58858172 58864865
## 2      1 uc002qsf.2   chr19 58859832 58874214
## 3     10 uc003wyw.1    chr8 18248755 18258723

select(EnsDb.Hsapiens.v79, c("1", "10"),
       c("GENEID","GENENAME","SEQNAME","GENESEQSTART","GENESEQEND"), "ENTREZID")

##   ENTREZID          GENEID GENENAME SEQNAME GENESEQSTART GENESEQEND
## 1        1 ENSG00000121410     A1BG      19     58345178   58353499
## 2       10 ENSG00000156006     NAT2       8     18391245   18401218

But this is not how one normally uses them…

GRanges

The normal use case for transcript packages is to extract positional information into a GRanges or GRangesList object. An example is the genomic position of all genes:

gns <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
gns

## GRanges object with 23056 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
##     ...      ...                 ...    ... .         ...
##    9991     chr9 114979995-115095944      - |        9991
##    9992    chr21   35736323-35743440      + |        9992
##    9993    chr22   19023795-19109967      - |        9993
##    9994     chr6   90539619-90584155      + |        9994
##    9997    chr22   50961997-50964905      - |        9997
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

GRangesList

Or the genomic position of all transcripts by gene:

txs <- transcriptsBy(TxDb.Hsapiens.UCSC.hg19.knownGene)
txs

## GRangesList object of length 23459:
## $`1`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr19 58858172-58864865      - |     70455  uc002qsd.4
##   [2]    chr19 58859832-58874214      - |     70456  uc002qsf.2
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`10`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]     chr8 18248755-18258723      + |     31944  uc003wyw.1
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`100`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr20 43248163-43280376      - |     72132  uc002xmj.3
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## ...
## <23456 more elements>

Other accessors

Positional information can be extracted for transcripts, genes, coding sequences (cds), promoters and exons.
EnsDb.Hsapiens.v79
EnsDb.Mmusculus.v79
EnsDb.Rnorvegicus.v79

Transcript packages

As with ChipDb and OrgDb packages, select and mapIds can be used to make queries

select(TxDb.Hsapiens.UCSC.hg19.knownGene, c("1","10"),
       c("TXNAME","TXCHROM","TXSTART","TXEND"), "GENEID")

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

##   GENEID     TXNAME TXCHROM  TXSTART    TXEND
## 1      1 uc002qsd.4   chr19 58858172 58864865
## 2      1 uc002qsf.2   chr19 58859832 58874214
## 3     10 uc003wyw.1    chr8 18248755 18258723

select(EnsDb.Hsapiens.v79, c("1", "10"),
       c("GENEID","GENENAME","SEQNAME","GENESEQSTART","GENESEQEND"), "ENTREZID")

##   ENTREZID          GENEID GENENAME SEQNAME GENESEQSTART GENESEQEND
## 1        1 ENSG00000121410     A1BG      19     58345178   58353499
## 2       10 ENSG00000156006     NAT2       8     18391245   18401218

But this is not how one normally uses them…

GRanges

The normal use case for transcript packages is to extract positional information into a GRanges or GRangesList object. An example is the genomic position of all genes:

gns <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
gns

## GRanges object with 23056 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
##     ...      ...                 ...    ... .         ...
##    9991     chr9 114979995-115095944      - |        9991
##    9992    chr21   35736323-35743440      + |        9992
##    9993    chr22   19023795-19109967      - |        9993
##    9994     chr6   90539619-90584155      + |        9994
##    9997    chr22   50961997-50964905      - |        9997
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

GRangesList

Or the genomic position of all transcripts by gene:

txs <- transcriptsBy(TxDb.Hsapiens.UCSC.hg19.knownGene)
txs

## GRangesList object of length 23459:
## $`1`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr19 58858172-58864865      - |     70455  uc002qsd.4
##   [2]    chr19 58859832-58874214      - |     70456  uc002qsf.2
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`10`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]     chr8 18248755-18258723      + |     31944  uc003wyw.1
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`100`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr20 43248163-43280376      - |     72132  uc002xmj.3
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## ...
## <23456 more elements>

Other accessors

Positional information can be extracted for transcripts, genes, coding sequences (cds), promoters and exons.
Positional information can be extracted for most of the above, grouped by a second element. For example, our transcriptsBy call was all transcripts, grouped by gene.
More detail on these *Ranges objects is beyond the scope of this workshop, but why we want them is not.

Why *Ranges objects

The main rationale for *Ranges objects is to allow us to easily select and subset data based on genomic position information. This is really powerful!

GRanges and GRangesLists act like data.frames and lists, and can be subsetted using the [ function. As a really artificial example:

txs[txs %over% gns[1:2,]]

## GRangesList object of length 3:
## $`1`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr19 58858172-58864865      - |     70455  uc002qsd.4
##   [2]    chr19 58859832-58874214      - |     70456  uc002qsf.2
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`10`
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]     chr8 18248755-18258723      + |     31944  uc003wyw.1
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome
## 
## $`162968`
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames            ranges strand |     tx_id     tx_name
##          <Rle>         <IRanges>  <Rle> | <integer> <character>
##   [1]    chr19 58865723-58874214      - |     70457  uc002qsh.2
##   [2]    chr19 58865723-58874214      - |     70458  uc002qsi.2
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

*Ranges use cases

Gene expression changes near differentially methylated CpG islands
Closest genes to a set of interesting SNPs
Genes near DNAseI hypersensitivity clusters
Number of CpGs measured over Gene X by Chip Y

SummarizedExperiment objects

SummarizedExperiment objects are like ExpressionSets, but the row-wise annotations are GRanges, so you can subset by genomic locations:

SummarizedExperiment objects are popular objects for representing expression data and other rectangular data (feature x sample data). Incoming packages are now strongly recommended to use this class representation instead of ExpressionSet.

TxDb exercises

How many transcripts does PPARG have, according to UCSC?
Does Ensembl agree?
How many genes are between 2858473 and 3271812 on chr2 in the hg19 genome?
- Hint: you make a GRanges like this - GRanges("chr2", IRanges(2858473,3271812))

OrganismDb packages

OrganismDb packages are meta-packages that contain an OrgDb, a TxDb, and a GO.db package and allow cross-queries between those packages.

All previous accessors work; select, mapIds, transcripts, etc.

library(Homo.sapiens)
Homo.sapiens

## OrganismDb Object:
## # Includes GODb Object:  GO.db 
## # With data about:  Gene Ontology 
## # Includes OrgDb Object:  org.Hs.eg.db 
## # Gene data about:  Homo sapiens 
## # Taxonomy Id:  9606 
## # Includes TxDb Object:  TxDb.Hsapiens.UCSC.hg19.knownGene 
## # Transcriptome data about:  Homo sapiens 
## # Based on genome:  hg19 
## # The OrgDb gene id ENTREZID is mapped to the TxDb gene id GENEID .

OrganismDb packages

Updateable - can change TxDb object
columns and keytypes span all underlying objects
Calls to TxDb accessors include a ‘columns’ argument

head(genes(Homo.sapiens, columns = c("ENTREZID","ALIAS","UNIPROT")),4)

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

## GRanges object with 4 ranges and 3 metadata columns:
##        seqnames            ranges strand |                 ALIAS
##           <Rle>         <IRanges>  <Rle> |       <CharacterList>
##      1    chr19 58858172-58874214      - |       A1B,ABG,GAB,...
##     10     chr8 18248755-18258723      + |   AAC2,NAT-2,PNAT,...
##    100    chr20 43248163-43280376      - |                   ADA
##   1000    chr18 25530930-25757445      - | CD325,CDHN,CDw325,...
##                         UNIPROT     ENTREZID
##                 <CharacterList> <FactorList>
##      1            P04217,V9HWD8            1
##     10            A4Z6T7,P11245           10
##    100 A0A0S2Z381,P00813,F5GWI4          100
##   1000        P19022,A0A024RC42         1000
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

OrganismDb exercises

Get all the GO terms for BRCA1
What gene does the UCSC transcript ID uc002fai.3 map to?
How many other transcripts does that gene have?
Get all the transcripts from the hg19 genome build, along with their Ensembl gene ID, UCSC transcript ID and gene symbol

Organism.dplyr package

Combines the data from TxDb and Org.Db associated packages into local database.
Allows functions from both org.* and TxDb.*
- keytypes(), select(), …
- exons(), promoters(), …
Allows for filtering and display of combined TxDb and Org.Db information through dplyr functions.

library(Organism.dplyr)

# src = src_organism("TxDb.Hsapiens.UCSC.hg19.knownGene")
# ?src_organism
src <- src_organism(dbpath = hg38light())
src

## src:  sqlite 3.22.0 [/usr/local/lib/R/site-library/Organism.dplyr/extdata/light.hg38.knownGene.sqlite]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
##   id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_tx

Organism.dplyr

Get promoters from a TxDb object (we use a small version)

options(width = 120)
promoters(src)

## GRanges object with 77 ranges and 2 metadata columns:
##                                seqnames              ranges strand |     tx_id           tx_name
##                                   <Rle>           <IRanges>  <Rle> | <integer>       <character>
##   ENST00000336199.9                chr1 243843037-243845236      - |     18165 ENST00000336199.9
##   ENST00000366540.5                chr1 243843385-243845584      - |     18166 ENST00000366540.5
##   ENST00000263826.9                chr1 243843083-243845282      - |     18167 ENST00000263826.9
##   ENST00000366539.5                chr1 243849929-243852128      - |     18168 ENST00000366539.5
##   ENST00000492957.1                chr1 243614947-243617146      - |     18169 ENST00000492957.1
##                 ...                 ...                 ...    ... .       ...               ...
##   ENST00000619536.4 chr1_KI270763v1_alt       855483-857682      - |    206918 ENST00000619536.4
##   ENST00000621586.3 chr1_KI270763v1_alt       855069-857268      - |    206919 ENST00000621586.3
##   ENST00000629115.1 chr1_KI270763v1_alt       867343-869542      - |    206920 ENST00000629115.1
##   ENST00000629924.2 chr1_KI270763v1_alt       826591-828790      - |    206921 ENST00000629924.2
##   ENST00000627932.1 chr1_KI270763v1_alt       830085-832284      - |    206922 ENST00000627932.1
##   -------
##   seqinfo: 595 sequences (1 circular) from hg38 genome

Organism.dplyr

Extract a table from the underlying database

tbl(src, "id")

## # Source:   table<id> [?? x 6]
## # Database: sqlite 3.22.0 []
##    entrez map      ensembl         symbol genename               alias   
##    <chr>  <chr>    <chr>           <chr>  <chr>                  <chr>   
##  1 1      19q13.43 ENSG00000121410 A1BG   alpha-1-B glycoprotein A1B     
##  2 1      19q13.43 ENSG00000121410 A1BG   alpha-1-B glycoprotein ABG     
##  3 1      19q13.43 ENSG00000121410 A1BG   alpha-1-B glycoprotein GAB     
##  4 1      19q13.43 ENSG00000121410 A1BG   alpha-1-B glycoprotein HYST2477
##  5 1      19q13.43 ENSG00000121410 A1BG   alpha-1-B glycoprotein A1BG    
##  6 10     8p22     ENSG00000156006 NAT2   N-acetyltransferase 2  AAC2    
##  7 10     8p22     ENSG00000156006 NAT2   N-acetyltransferase 2  NAT-2   
##  8 10     8p22     ENSG00000156006 NAT2   N-acetyltransferase 2  PNAT    
##  9 10     8p22     ENSG00000156006 NAT2   N-acetyltransferase 2  NAT2    
## 10 100    20q13.12 ENSG00000196839 ADA    adenosine deaminase    ADA     
## # … with more rows

Organism.dplyr

Make a complex query between tables in the underlying database

inner_join(tbl(src, "id"), tbl(src, "ranges_gene")) %>%
            filter(symbol %in% c("ADA", "NAT2")) %>%
            dplyr::select(gene_chrom, gene_start, gene_end,
            gene_strand, symbol, alias, map)

## Joining, by = "entrez"

## # Source:   lazy query [?? x 7]
## # Database: sqlite 3.22.0 []
##   gene_chrom gene_start gene_end gene_strand symbol alias map     
##   <chr>           <int>    <int> <chr>       <chr>  <chr> <chr>   
## 1 chr8         18391245 18401218 +           NAT2   AAC2  8p22    
## 2 chr8         18391245 18401218 +           NAT2   NAT-2 8p22    
## 3 chr8         18391245 18401218 +           NAT2   PNAT  8p22    
## 4 chr8         18391245 18401218 +           NAT2   NAT2  8p22    
## 5 chr20        44619522 44652233 -           ADA    ADA   20q13.12

Organism.dplyr exercises

How many supported organisms are implemented in Organism.dplyr?
Display the ensembl Id and genename description for symbol “NAT2”.
Show all the alias for “NAT2” in the database.
Display Gene ontology (GO) information for gene symbol “Nat2”.

BSgenome packages

BSgenome packages contain sequence information for a given species/build. There are many such packages - you can get a listing using available.genomes

library(BSgenome)
head(available.genomes())

## [1] "BSgenome.Alyrata.JGI.v1"                 "BSgenome.Amellifera.BeeBase.assembly4"  
## [3] "BSgenome.Amellifera.UCSC.apiMel2"        "BSgenome.Amellifera.UCSC.apiMel2.masked"
## [5] "BSgenome.Aofficinalis.NCBI.V1"           "BSgenome.Athaliana.TAIR.04232008"

BSgenome packages

We can load and inspect a BSgenome package

library(BSgenome.Hsapiens.UCSC.hg19)
Hsapiens

## Human genome:
## # organism: Homo sapiens (Human)
## # provider: UCSC
## # provider version: hg19
## # release date: Feb. 2009
## # release name: Genome Reference Consortium GRCh37
## # 93 sequences:
## #   chr1                  chr2                  chr3                  chr4                  chr5                 
## #   chr6                  chr7                  chr8                  chr9                  chr10                
## #   chr11                 chr12                 chr13                 chr14                 chr15                
## #   chr16                 chr17                 chr18                 chr19                 chr20                
## #   chr21                 chr22                 chrX                  chrY                  chrM                 
## #   ...                   ...                   ...                   ...                   ...                  
## #   chrUn_gl000227        chrUn_gl000228        chrUn_gl000229        chrUn_gl000230        chrUn_gl000231       
## #   chrUn_gl000232        chrUn_gl000233        chrUn_gl000234        chrUn_gl000235        chrUn_gl000236       
## #   chrUn_gl000237        chrUn_gl000238        chrUn_gl000239        chrUn_gl000240        chrUn_gl000241       
## #   chrUn_gl000242        chrUn_gl000243        chrUn_gl000244        chrUn_gl000245        chrUn_gl000246       
## #   chrUn_gl000247        chrUn_gl000248        chrUn_gl000249                                                   
## # (use 'seqnames()' to see all the sequence names, use the '$' or '[[' operator to access a given sequence)

BSgenome packages

The main accessor is getSeq, and you can get data by sequence (e.g., entire chromosome or unplaced scaffold), or by passing in a GRanges object, to get just a region.

getSeq(Hsapiens, "chr1")

##   249250621-letter "DNAString" instance
## seq: NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN

getSeq(Hsapiens, gns["5467",])

##   A DNAStringSet instance of length 1
##     width seq                                                                                       names               
## [1] 85634 GCGGAGCGTGTGACGCTGCGGCCGCCGCGGACCTGGGGATTAA...ACTTTAAATAAATGGGAATTAAATATTTAAGAGCTGACTGGAA 5467

The Biostrings package contains most of the code for dealing with these *StringSet objects - please see the Biostrings vignettes and help pages for more information.

BSgenome exercises

Get the sequences for all transcripts of the TP53 gene

AnnotationHub

AnnotationHub is a package that allows us to query and download many different annotation objects, without having to explicitly install them.

library(AnnotationHub)
hub <- AnnotationHub()

## Using temporary cache /tmp/RtmpvM348B/BiocFileCache

## snapshotDate(): 2019-05-20

## Using temporary cache /tmp/RtmpvM348B/BiocFileCache

hub

## AnnotationHub with 46259 records
## # snapshotDate(): 2019-05-20 
## # $dataprovider: BroadInstitute, Ensembl, UCSC, ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/, Haemcode, FungiDB, Inparanoid...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Bos taurus, Rattus norvegicus, Pan troglodytes, Dan...
## # $rdataclass: GRanges, BigWigFile, TwoBitFile, OrgDb, Rle, ChainFile, EnsDb, Inparanoid8Db, TxDb, data.frame
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer, rdatadateadded,
## #   preparerclass, tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH5012"]]' 
## 
##             title                                                     
##   AH5012  | Chromosome Band                                           
##   AH5013  | STS Markers                                               
##   AH5014  | FISH Clones                                               
##   AH5015  | Recomb Rate                                               
##   AH5016  | ENCODE Pilot                                              
##   ...       ...                                                       
##   AH73812 | org.Plasmodium_vivax.eg.sqlite                            
##   AH73813 | org.Burkholderia_mallei_ATCC_23344.eg.sqlite              
##   AH73814 | org.Bacillus_cereus_(strain_ATCC_14579_|_DSM_31).eg.sqlite
##   AH73815 | org.Bacillus_cereus_ATCC_14579.eg.sqlite                  
##   AH73816 | org.Schizosaccharomyces_cryophilus_OY26.eg.sqlite

Querying AnnotationHub

Finding the ‘right’ resource on AnnotationHub is like using Google - a well posed query is necessary to find what you are after. Useful queries are based on

Data provider
Data class
Species
Data source

names(mcols(hub))

##  [1] "title"              "dataprovider"       "species"            "taxonomyid"         "genome"            
##  [6] "description"        "coordinate_1_based" "maintainer"         "rdatadateadded"     "preparerclass"     
## [11] "tags"               "rdataclass"         "rdatapath"          "sourceurl"          "sourcetype"

AnnotationHub Data providers

unique(hub$dataprovider)

##  [1] "UCSC"                                  "Ensembl"                              
##  [3] "RefNet"                                "Inparanoid8"                          
##  [5] "NHLBI"                                 "ChEA"                                 
##  [7] "Pazar"                                 "NIH Pathway Interaction Database"     
##  [9] "Haemcode"                              "BroadInstitute"                       
## [11] "PRIDE"                                 "Gencode"                              
## [13] "CRIBI"                                 "Genoscope"                            
## [15] "MISO, VAST-TOOLS, UCSC"                "UWashington"                          
## [17] "Stanford"                              "dbSNP"                                
## [19] "BioMart"                               "GeneOntology"                         
## [21] "KEGG"                                  "URGI"                                 
## [23] "EMBL-EBI"                              "MicrosporidiaDB"                      
## [25] "FungiDB"                               "TriTrypDB"                            
## [27] "ToxoDB"                                "AmoebaDB"                             
## [29] "PlasmoDB"                              "PiroplasmaDB"                         
## [31] "CryptoDB"                              "TrichDB"                              
## [33] "GiardiaDB"                             "The Gene Ontology Consortium"         
## [35] "DrugBank, Broad Institute, STITCH"     "GO"                                   
## [37] "Broad Institute"                       "ENCODE Project"                       
## [39] "ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/" "SchistoDB"

AnnotationHub Data classes

unique(hub$rdataclass)

##  [1] "GRanges"                           "data.frame"                        "Inparanoid8Db"                    
##  [4] "TwoBitFile"                        "ChainFile"                         "SQLiteConnection"                 
##  [7] "biopax"                            "BigWigFile"                        "AAStringSet"                      
## [10] "MSnSet"                            "mzRpwiz"                           "mzRident"                         
## [13] "list"                              "TxDb"                              "Rle"                              
## [16] "EnsDb"                             "VcfFile"                           "igraph"                           
## [19] "data.frame, DNAStringSet, GRanges" "sqlite"                            "environment"                      
## [22] "character"                         "data.table"                        "OrgDb"

AnnotationHub Species

head(unique(hub$species))

## [1] "Homo sapiens"         "Vicugna pacos"        "Dasypus novemcinctus" "Otolemur garnettii"   "Papio hamadryas"     
## [6] "Papio anubis"

length(unique(hub$species))

## [1] 2295

AnnotationHub Data sources

unique(hub$sourcetype)

##  [1] "UCSC track"   "GTF"          "TSV"          "Inparanoid"   "TwoBit"       "Chain"        "GRASP"       
##  [8] "Zip"          "CSV"          "BioPax"       "BioPaxLevel2" "RData"        "BED"          "BigWig"      
## [15] "tab"          "FASTA"        "mzTab"        "mzML"         "mzid"         "GFF"          "ensembl"     
## [22] "VCF"          "TXT"          "MySQL"        "CEL"          "HDF5"         "JSON"         "NCBI/ensembl"
## [29] "NCBI/UniProt"

AnnotationHub query

qry <- query(hub, c("granges","homo sapiens","ensembl"))

## Using temporary cache /tmp/RtmpvM348B/BiocFileCache

qry

## AnnotationHub with 72 records
## # snapshotDate(): 2019-05-20 
## # $dataprovider: Ensembl, UCSC
## # $species: Homo sapiens
## # $rdataclass: GRanges
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer, rdatadateadded,
## #   preparerclass, tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH5046"]]' 
## 
##             title                                          
##   AH5046  | Ensembl Genes                                  
##   AH5160  | Ensembl Genes                                  
##   AH5311  | Ensembl Genes                                  
##   AH5434  | Ensembl Genes                                  
##   AH5435  | Ensembl EST Genes                              
##   ...       ...                                            
##   AH68821 | Homo_sapiens.GRCh38.95.gtf                     
##   AH69458 | Homo_sapiens.GRCh38.96.abinitio.gtf            
##   AH69459 | Homo_sapiens.GRCh38.96.chr.gtf                 
##   AH69460 | Homo_sapiens.GRCh38.96.chr_patch_hapl_scaff.gtf
##   AH69461 | Homo_sapiens.GRCh38.96.gtf

AnnotationHub query

qry$sourceurl

##  [1] "rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/ensGene"                                  
##  [2] "rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg18/database/ensGene"                                  
##  [3] "rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg17/database/ensGene"                                  
##  [4] "rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg16/database/ensGene"                                  
##  [5] "rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg16/database/ensEstGene"                               
##  [6] "ftp://ftp.ensembl.org/pub/release-70/gtf/homo_sapiens/Homo_sapiens.GRCh37.70.gtf.gz"                     
##  [7] "ftp://ftp.ensembl.org/pub/release-69/gtf/homo_sapiens/Homo_sapiens.GRCh37.69.gtf.gz"                     
##  [8] "ftp://ftp.ensembl.org/pub/release-71/gtf/homo_sapiens/Homo_sapiens.GRCh37.71.gtf.gz"                     
##  [9] "ftp://ftp.ensembl.org/pub/release-72/gtf/homo_sapiens/Homo_sapiens.GRCh37.72.gtf.gz"                     
## [10] "ftp://ftp.ensembl.org/pub/release-73/gtf/homo_sapiens/Homo_sapiens.GRCh37.73.gtf.gz"                     
## [11] "ftp://ftp.ensembl.org/pub/release-74/gtf/homo_sapiens/Homo_sapiens.GRCh37.74.gtf.gz"                     
## [12] "ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/Homo_sapiens.GRCh37.75.gtf.gz"                     
## [13] "ftp://ftp.ensembl.org/pub/release-78/gtf/homo_sapiens/Homo_sapiens.GRCh38.78.gtf.gz"                     
## [14] "ftp://ftp.ensembl.org/pub/release-76/gtf/homo_sapiens/Homo_sapiens.GRCh38.76.gtf.gz"                     
## [15] "ftp://ftp.ensembl.org/pub/release-79/gtf/homo_sapiens/Homo_sapiens.GRCh38.79.gtf.gz"                     
## [16] "ftp://ftp.ensembl.org/pub/release-77/gtf/homo_sapiens/Homo_sapiens.GRCh38.77.gtf.gz"                     
## [17] "ftp://ftp.ensembl.org/pub/release-80/gtf/homo_sapiens/Homo_sapiens.GRCh38.80.gtf.gz"                     
## [18] "ftp://ftp.ensembl.org/pub/release-81/gtf/homo_sapiens/Homo_sapiens.GRCh38.81.gtf.gz"                     
## [19] "ftp://ftp.ensembl.org/pub/release-82/gtf/homo_sapiens/Homo_sapiens.GRCh38.82.gtf.gz"                     
## [20] "ftp://ftp.ensembl.org/pub/release-83/gtf/homo_sapiens/Homo_sapiens.GRCh38.83.gtf.gz"                     
## [21] "ftp://ftp.ensembl.org/pub/release-84/gtf/homo_sapiens/Homo_sapiens.GRCh38.84.abinitio.gtf.gz"            
## [22] "ftp://ftp.ensembl.org/pub/release-84/gtf/homo_sapiens/Homo_sapiens.GRCh38.84.chr.gtf.gz"                 
## [23] "ftp://ftp.ensembl.org/pub/release-84/gtf/homo_sapiens/Homo_sapiens.GRCh38.84.chr_patch_hapl_scaff.gtf.gz"
## [24] "ftp://ftp.ensembl.org/pub/release-84/gtf/homo_sapiens/Homo_sapiens.GRCh38.84.gtf.gz"                     
## [25] "ftp://ftp.ensembl.org/pub/release-85/gtf/homo_sapiens/Homo_sapiens.GRCh38.85.abinitio.gtf.gz"            
## [26] "ftp://ftp.ensembl.org/pub/release-85/gtf/homo_sapiens/Homo_sapiens.GRCh38.85.chr.gtf.gz"                 
## [27] "ftp://ftp.ensembl.org/pub/release-85/gtf/homo_sapiens/Homo_sapiens.GRCh38.85.chr_patch_hapl_scaff.gtf.gz"
## [28] "ftp://ftp.ensembl.org/pub/release-85/gtf/homo_sapiens/Homo_sapiens.GRCh38.85.gtf.gz"                     
## [29] "ftp://ftp.ensembl.org/pub/release-86/gtf/homo_sapiens/Homo_sapiens.GRCh38.86.abinitio.gtf.gz"            
## [30] "ftp://ftp.ensembl.org/pub/release-86/gtf/homo_sapiens/Homo_sapiens.GRCh38.86.chr.gtf.gz"                 
## [31] "ftp://ftp.ensembl.org/pub/release-86/gtf/homo_sapiens/Homo_sapiens.GRCh38.86.chr_patch_hapl_scaff.gtf.gz"
## [32] "ftp://ftp.ensembl.org/pub/release-86/gtf/homo_sapiens/Homo_sapiens.GRCh38.86.gtf.gz"                     
## [33] "ftp://ftp.ensembl.org/pub/release-87/gtf/homo_sapiens/Homo_sapiens.GRCh38.87.abinitio.gtf.gz"            
## [34] "ftp://ftp.ensembl.org/pub/release-87/gtf/homo_sapiens/Homo_sapiens.GRCh38.87.chr.gtf.gz"                 
## [35] "ftp://ftp.ensembl.org/pub/release-87/gtf/homo_sapiens/Homo_sapiens.GRCh38.87.chr_patch_hapl_scaff.gtf.gz"
## [36] "ftp://ftp.ensembl.org/pub/release-87/gtf/homo_sapiens/Homo_sapiens.GRCh38.87.gtf.gz"                     
## [37] "ftp://ftp.ensembl.org/pub/release-88/gtf/homo_sapiens/Homo_sapiens.GRCh38.88.abinitio.gtf.gz"            
## [38] "ftp://ftp.ensembl.org/pub/release-88/gtf/homo_sapiens/Homo_sapiens.GRCh38.88.chr.gtf.gz"                 
## [39] "ftp://ftp.ensembl.org/pub/release-88/gtf/homo_sapiens/Homo_sapiens.GRCh38.88.chr_patch_hapl_scaff.gtf.gz"
## [40] "ftp://ftp.ensembl.org/pub/release-88/gtf/homo_sapiens/Homo_sapiens.GRCh38.88.gtf.gz"                     
## [41] "ftp://ftp.ensembl.org/pub/release-89/gtf/homo_sapiens/Homo_sapiens.GRCh38.89.abinitio.gtf.gz"            
## [42] "ftp://ftp.ensembl.org/pub/release-89/gtf/homo_sapiens/Homo_sapiens.GRCh38.89.chr.gtf.gz"                 
## [43] "ftp://ftp.ensembl.org/pub/release-89/gtf/homo_sapiens/Homo_sapiens.GRCh38.89.chr_patch_hapl_scaff.gtf.gz"
## [44] "ftp://ftp.ensembl.org/pub/release-89/gtf/homo_sapiens/Homo_sapiens.GRCh38.89.gtf.gz"                     
## [45] "ftp://ftp.ensembl.org/pub/release-90/gtf/homo_sapiens/Homo_sapiens.GRCh38.90.abinitio.gtf.gz"            
## [46] "ftp://ftp.ensembl.org/pub/release-90/gtf/homo_sapiens/Homo_sapiens.GRCh38.90.chr.gtf.gz"                 
## [47] "ftp://ftp.ensembl.org/pub/release-90/gtf/homo_sapiens/Homo_sapiens.GRCh38.90.chr_patch_hapl_scaff.gtf.gz"
## [48] "ftp://ftp.ensembl.org/pub/release-90/gtf/homo_sapiens/Homo_sapiens.GRCh38.90.gtf.gz"                     
## [49] "ftp://ftp.ensembl.org/pub/release-91/gtf/homo_sapiens/Homo_sapiens.GRCh38.91.abinitio.gtf.gz"            
## [50] "ftp://ftp.ensembl.org/pub/release-91/gtf/homo_sapiens/Homo_sapiens.GRCh38.91.chr.gtf.gz"                 
## [51] "ftp://ftp.ensembl.org/pub/release-91/gtf/homo_sapiens/Homo_sapiens.GRCh38.91.chr_patch_hapl_scaff.gtf.gz"
## [52] "ftp://ftp.ensembl.org/pub/release-91/gtf/homo_sapiens/Homo_sapiens.GRCh38.91.gtf.gz"                     
## [53] "ftp://ftp.ensembl.org/pub/release-92/gtf/homo_sapiens/Homo_sapiens.GRCh38.92.abinitio.gtf.gz"            
## [54] "ftp://ftp.ensembl.org/pub/release-92/gtf/homo_sapiens/Homo_sapiens.GRCh38.92.chr.gtf.gz"                 
## [55] "ftp://ftp.ensembl.org/pub/release-92/gtf/homo_sapiens/Homo_sapiens.GRCh38.92.chr_patch_hapl_scaff.gtf.gz"
## [56] "ftp://ftp.ensembl.org/pub/release-92/gtf/homo_sapiens/Homo_sapiens.GRCh38.92.gtf.gz"                     
## [57] "ftp://ftp.ensembl.org/pub/release-93/gtf/homo_sapiens/Homo_sapiens.GRCh38.93.abinitio.gtf.gz"            
## [58] "ftp://ftp.ensembl.org/pub/release-93/gtf/homo_sapiens/Homo_sapiens.GRCh38.93.chr.gtf.gz"                 
## [59] "ftp://ftp.ensembl.org/pub/release-93/gtf/homo_sapiens/Homo_sapiens.GRCh38.93.chr_patch_hapl_scaff.gtf.gz"
## [60] "ftp://ftp.ensembl.org/pub/release-93/gtf/homo_sapiens/Homo_sapiens.GRCh38.93.gtf.gz"                     
## [61] "ftp://ftp.ensembl.org/pub/release-94/gtf/homo_sapiens/Homo_sapiens.GRCh38.94.abinitio.gtf.gz"            
## [62] "ftp://ftp.ensembl.org/pub/release-94/gtf/homo_sapiens/Homo_sapiens.GRCh38.94.chr.gtf.gz"                 
## [63] "ftp://ftp.ensembl.org/pub/release-94/gtf/homo_sapiens/Homo_sapiens.GRCh38.94.chr_patch_hapl_scaff.gtf.gz"
## [64] "ftp://ftp.ensembl.org/pub/release-94/gtf/homo_sapiens/Homo_sapiens.GRCh38.94.gtf.gz"                     
## [65] "ftp://ftp.ensembl.org/pub/release-95/gtf/homo_sapiens/Homo_sapiens.GRCh38.95.abinitio.gtf.gz"            
## [66] "ftp://ftp.ensembl.org/pub/release-95/gtf/homo_sapiens/Homo_sapiens.GRCh38.95.chr.gtf.gz"                 
## [67] "ftp://ftp.ensembl.org/pub/release-95/gtf/homo_sapiens/Homo_sapiens.GRCh38.95.chr_patch_hapl_scaff.gtf.gz"
## [68] "ftp://ftp.ensembl.org/pub/release-95/gtf/homo_sapiens/Homo_sapiens.GRCh38.95.gtf.gz"                     
## [69] "ftp://ftp.ensembl.org/pub/release-96/gtf/homo_sapiens/Homo_sapiens.GRCh38.96.abinitio.gtf.gz"            
## [70] "ftp://ftp.ensembl.org/pub/release-96/gtf/homo_sapiens/Homo_sapiens.GRCh38.96.chr.gtf.gz"                 
## [71] "ftp://ftp.ensembl.org/pub/release-96/gtf/homo_sapiens/Homo_sapiens.GRCh38.96.chr_patch_hapl_scaff.gtf.gz"
## [72] "ftp://ftp.ensembl.org/pub/release-96/gtf/homo_sapiens/Homo_sapiens.GRCh38.96.gtf.gz"

Selecting AnnotationHub resource

whatIwant <- qry[["AH50377"]]

We can use these data as they are, or convert to a TxDb format:

GRCh38TxDb <- makeTxDbFromGRanges(whatIwant)
GRCh38TxDb

## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Genome: GRCh38
## # transcript_nrow: 199184
## # exon_nrow: 675836
## # cds_nrow: 270225
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2019-06-22 01:54:14 +0000 (Sat, 22 Jun 2019)
## # GenomicFeatures version at creation time: 1.37.1
## # RSQLite version at creation time: 2.1.1
## # DBSCHEMAVERSION: 1.2

AnnotationHub exercises

How many resources are on AnnotationHub for Atlantic salmon (Salmo salar)?
Get the most recent Ensembl build for domesticated dog (Canis familiaris) and make a TxDb

biomaRt

The biomaRt package allows queries to an Ensembl Biomart server. We can see the choices of servers that we can use:

library(biomaRt)
listMarts()

##                biomart               version
## 1 ENSEMBL_MART_ENSEMBL      Ensembl Genes 96
## 2   ENSEMBL_MART_MOUSE      Mouse strains 96
## 3     ENSEMBL_MART_SNP  Ensembl Variation 96
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 96

biomaRt data sets

And we can then check for the available data sets on a particular server.

mart <- useMart("ENSEMBL_MART_ENSEMBL")
head(listDatasets(mart))

##                        dataset                           description     version
## 1 abrachyrhynchus_gene_ensembl Pink-footed goose genes (ASM259213v1) ASM259213v1
## 2     acalliptera_gene_ensembl      Eastern happy genes (fAstCal1.2)  fAstCal1.2
## 3   acarolinensis_gene_ensembl        Anole lizard genes (AnoCar2.0)   AnoCar2.0
## 4    acitrinellus_gene_ensembl        Midas cichlid genes (Midas_v5)    Midas_v5
## 5        ahaastii_gene_ensembl    Great spotted kiwi genes (aptHaa1)     aptHaa1
## 6    amelanoleuca_gene_ensembl                 Panda genes (ailMel1)     ailMel1

biomaRt queries

After setting up a mart object pointing to the server and data set that we care about, we can make queries. We first set up the mart object.

mart <- useMart("ENSEMBL_MART_ENSEMBL","hsapiens_gene_ensembl")

Queries are of the form

getBM(attributes, filters, values, mart)

where

attributes are the things we want
filters are the types of IDs we have
values are the IDs we have
mart is the mart object we set up

biomaRt attributes and filters

Both attributes and filters have rather inscrutable names, but a listing can be accessed using

atrib <- listAttributes(mart)
filts <- listFilters(mart)
head(atrib)

##                            name                  description         page
## 1               ensembl_gene_id               Gene stable ID feature_page
## 2       ensembl_gene_id_version       Gene stable ID version feature_page
## 3         ensembl_transcript_id         Transcript stable ID feature_page
## 4 ensembl_transcript_id_version Transcript stable ID version feature_page
## 5            ensembl_peptide_id            Protein stable ID feature_page
## 6    ensembl_peptide_id_version    Protein stable ID version feature_page

head(filts)

##              name              description
## 1 chromosome_name Chromosome/scaffold name
## 2           start                    Start
## 3             end                      End
## 4      band_start               Band Start
## 5        band_end                 Band End
## 6    marker_start             Marker Start

biomaRt query

A simple example query

afyids <- c("1000_at","1001_at","1002_f_at","1007_s_at")
getBM(c("affy_hg_u95av2", "hgnc_symbol"), c("affy_hg_u95av2"), afyids, mart)

##   affy_hg_u95av2 hgnc_symbol
## 1      1007_s_at        DDR1
## 2        1000_at       MAPK3
## 3      1002_f_at     CYP2C19
## 4      1002_f_at            
## 5        1001_at        TIE1

biomaRt exercises

Get the Ensembl gene IDs and HUGO symbol for Entrez Gene IDs 672, 5468 and 7157
What do you get if you query for the ‘gene_exon’ for GAPDH?

Introduction to Bioconductor Annotation Resources

Instructors

Workshop Description

Prerequisites

Workshop Participation

R / Bioconductor packages used

Workshop goals and objectives

Learning goals

Learning objectives

Annotation Workshop

What do we mean by annotation?

Specific goal

Data containers

ExpressionSet

ExpressionSet (continued)

ExpressionSet (continued)

BioC containers vs basic structures

Pros

Cons

Annotation sources

Interacting with AnnoDb packages

Simple example

Questions!

More questions!

Another example

The mapIds function

Choices for multiVals

Choices for multiVals (continued)

ChipDb/OrgDb questions

TxDb packages

EnsDb packages

Transcript packages

GRanges

GRangesList

Other accessors

Transcript packages

GRanges

GRangesList

Other accessors

Transcript packages

GRanges

GRangesList

Other accessors

Why *Ranges objects

*Ranges use cases

SummarizedExperiment objects

TxDb exercises

OrganismDb packages

OrganismDb packages

OrganismDb exercises

Organism.dplyr package

Organism.dplyr

Organism.dplyr

Organism.dplyr

Organism.dplyr exercises

BSgenome packages

BSgenome packages

BSgenome packages

BSgenome exercises

AnnotationHub

Querying AnnotationHub

AnnotationHub Data providers

AnnotationHub Data classes

AnnotationHub Species

AnnotationHub Data sources

AnnotationHub query

AnnotationHub query

Selecting AnnotationHub resource

AnnotationHub exercises

biomaRt

biomaRt data sets

biomaRt queries

biomaRt attributes and filters

biomaRt query

biomaRt exercises

The `mapIds` function