R / Bioconductor packages for gene and genome annotation

Transcription

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

2 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?...

3 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, 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

4 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

5 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 mapped keys (of keys) > # org.hs.eggo has mapped keys (of 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()

6 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 <- org.hs.eggenename Lkeys and Rkeys > head(lkeys(map)) [1] "1" "10" "100" [4] "1000" "10000" " " > map[["1000"]] [1] "cadherin 2, type 1, N-cadherin (neuronal)"

7 Manipulating maps Subset (numeric or character) > submap <- map[c(1, 3)] > 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 adenosine deaminase Reverse (!) > revmap(map)[["adenosine deaminase"]] [1] "100"

8 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"

9 Advanced examples I Between-table joins ENTREZ id 1 has 3 GO terms associated with it The second term has GO id GO: This term is described in detail in the GO.db ontology annotation package > go <- org.hs.eggo[["1"]] > length(go) [1] 3 > (goid <- go[[2]][["goid"]]) [1] "GO: "

10 Advanced examples II > library(go.db) > GOTERM[[goid]] GOID: GO: 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

11 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

12 Advanced examples IV > conn <- org.hs.eg_dbconn() > 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 <- "SELECT gene_id, path_id FROM genes,kegg + WHERE genes._id=kegg._id + LIMIT 3;" > dbgetquery(conn, sql) gene_id path_id

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

14 biomart 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

15 biomart II > library(biomart) > listmarts() > mart0 <- usemart("ensembl") > listdatasets(mart0) > mart <- usemart("ensembl", "hsapiens_gene_ensembl") > listfilters(mart) > getgene(id = "100", type = "entrezgene", + mart = mart) > getgene(id = "1939_at", type = "affy_hg_u133_plus_2", + mart = mart)

16 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

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

18 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