R / Bioconductor packages for gene and genome annotation

R / Bioconductor packages for gene and genome
annotation
Martin Morgan
Bioconductor / Fred Hutchinson Cancer Research Center
Seattle, WA, USA
15-19 June 2009

Annotations
Scenario
◮ Differnetial expression analysis complete, probesets for further
investigation identified
Desire: understand genes that have been identified
◮ Gene name?
◮ Chromosome location?
◮ Gene ontologies, pathways?
◮ . . .

Major options
AnnotationDbi packages
◮ Chip, e..g, hgu95av2.db
◮ Organism, e.g., org.Hs.eg.db
◮ Pathways / Ontologies, e.g., GO.db
◮ Other, e.g., HapMap SNPs, http://bioconductor.org/
packages/release/AnnotationData.html
biomaRt
◮ Query web-based ‘biomart’ resources for genes, sequence,
SNPs, homologs, . . .
Manufacturer provided annotations, e.g., in two-color gpr files

Pro and con
AnnotationDbi
◮ Consistent across analyses
◮ Reliably accessible; obtain with biocLite
◮ Careful secondary curation at Bioconductor
biomaRt
◮ Updated continuously
◮ Careful secondary curation at EBI

Using AnnotationDbi packages
> library(org.Hs.eg.db)
> org.Hs.eg()
> # Quality control information for org.Hs.eg:
> #
> # This package has the following mappings:
> # ...
> # org.Hs.egGENENAME has 40596 mapped keys (of 40596 keys)
> # org.Hs.egGO has 17593 mapped keys (of 40596 keys)
> # ...
> ls("package:org.Hs.eg.db")
> # [1] "org.Hs.eg" "org.Hs.egACCNUM"
> # [3] "org.Hs.egACCNUM2EG" "org.Hs.egALIAS2EG"
> # ...
> org.Hs.eg_dbInfo()

Basic structure: bi-maps with Lkeys and Rkeys
Bi-maps, e.g., from ENTREZ id to GENENAME (and reverse)
> org.Hs.egGENENAME
GENENAME map for Human (object of class "AnnDbBimap")
> map head(Lkeys(map))
[1] "1" "10" "100"
[4] "1000" "10000" "100008586"
> map[["1000"]]
[1] "cadherin 2, type 1, N-cadherin (neuronal)"

Manipulating maps
Subset (numeric or character)
> submap submap
GENENAME submap for Human (object of class "AnnDbBimap")
As data frame or list
> toTable(submap)
gene_id
gene_name
1 1 alpha-1-B glycoprotein
2 100 adenosine deaminase
Reverse (!)
> revmap(map)[["adenosine deaminase"]]
[1] "100"

Common issues
Not all maps are precisely as described above
◮ Chip packages: ‘Lkey’ is probeset id; pathway packages:
‘Lkey’ is pathway id
◮ Some maps are not reversible, e.g., org.Hs.egCHRLOC
Symbol to ENTREZ
> org.Hs.egSYMBOL[["10316"]]
[1] "NMUR1"
> revmap(org.Hs.egALIAS2EG)[["10316"]]
[1] "(FM-3)" "FM-3" "FM3" "GPC-R" "GPR66"
[6] "NMU1R" "NMUR1"

Advanced examples I
Between-table joins
◮ ENTREZ id 1 has 3 GO terms associated with it
◮ The second term has GO id GO:0005576
◮ This term is described in detail in the GO.db ontology
annotation package
> go length(go)
[1] 3
> (goid

Advanced examples II
> library(GO.db)
> GOTERM[[goid]]
GOID: GO:0005576
Term: extracellular region
Ontology: CC
Definition: The space external to the
outermost structure of a cell. For cells
without external protective or external
encapsulating structures this refers to
space outside of the plasma membrane.
This term covers the host cell
environment outside an intracellular
parasite.
Synonym: extracellular

Advanced examples III
Direct SQL queries
◮ AnnotationDbi stores information as sqlite tables
◮ Use org.Hs.eg_dbschema() to discover table structure
◮ Get the data base connection with org.Hs.eg_dbconn()
◮ Compose and evaluate a SQL statement with the interface
provided by the DBI package

Advanced examples IV
> conn dbGetQuery(conn, "SELECT * FROM gene_info LIMIT 3;")
_id
gene_name symbol
1 1 alpha-1-B glycoprotein A1BG
2 2 alpha-2-macroglobulin A2M
3 3 alpha-2-macroglobulin pseudogene A2MP
> ## join
> sql dbGetQuery(conn, sql)
gene_id path_id
1 2 04610
2 9 00232
3 9 00983

Advanced examples V
Custom annotation packages are ‘easy’ to create
◮ See the SQLForge vignette in AnnotationDbi

iomaRt I
Basic work flow
◮ Discover and select a ‘mart’, e.g., ensembl
◮ Discover and select a ‘dataset’, e.g.,
hsapiens_gene_ensembl
◮ Discover and select a ‘filter’, e.g., entrezgene
◮ Compose a query

iomaRt II
> library(biomaRt)
> listMarts()
> mart0 listDatasets(mart0)
> mart listFilters(mart)
> getGene(id = "100", type = "entrezgene",
+ mart = mart)
> getGene(id = "1939_at", type = "affy_hg_u133_plus_2",
+ mart = mart)

Intermediate and advanced biomaRt
Complex queries
> getBM(
+ attributes=c("affy_hg_u95av2", "hgnc_symbol",
+ "chromosome_name", "band"),
+ filters="affy_hg_u95av2",
+ values=c("1939_at", "1503_at", "1454_at"),
+ mart=mart)
Marts can be installed locally
◮ Non-public data
◮ Reliable connectivity

Additional packages
◮ GenomeGraphs for (very) pretty display of annotation and
expression data
◮ rtracklayer for exporting expression and genomic coordinates
for visualization in web browsers

Summary
AnnotationDbi
◮ Curated, reliable organismal, chip, and pathway annotations
◮ Accessible on the desktop
◮ Advanced users can query with SQL, and create their own
data bases.
biomaRt
◮ Curated, diverse annotations
◮ Accessible via the internet
◮ Advanced users can install their own biomaRt for private and
reliable access.
Other packages
◮ GenomeGraphs for visualization, rtracklayer for export to web
browsers