metadata and annotation with bioconductor
DESCRIPTION
Metadata and Annotation with Bioconductor. Static vs. Dynamic Annotation. Static Annotation: Bioconductor packages containing annotation information that are installed locally on a computer well-defined structure reproducible analyses no need for network connection. Dynamic Annotation: - PowerPoint PPT PresentationTRANSCRIPT
Metadata and Annotation with Bioconductor
Static vs. Dynamic Annotation
Static Annotation:• Bioconductor packages containing annotation
information that are installed locally on a computer
• well-defined structure• reproducible analyses• no need for network connection
Dynamic Annotation:• stored in a remote database• more frequent updates possibly different
result when repeating analyses• more information• one needs to know about the structure of the
database, the API of the webservice etc.
• EntrezGene is a catalog of genetic loci that connects curated sequence information to official nomenclature. It replaced LocusLink.
• UniGene defines sequence clusters. UniGene focuses on protein-coding genes of the nuclear genome (excluding rRNA and mitochondrial sequences).
• RefSeq is a non-redundant set of transcripts and proteins of known genes for many species, including human, mouse and rat.
• Enzyme Commission (EC) numbers are assigned to different enzymes and linked to genes through EntrezGene.
Available Metadata
• Gene Ontology (GO) is a structured vocabulary of terms describing gene products according to molecular function, biological process, or cellular component
• PubMed is a service of the U.S. National Library of Medicine. PubMed provides a rich resource of data and tools for papers in journals related to medicine and health. While large, the data source is not comprehensive, and not all papers have been abstracted
Available Metadata
• OMIM Online Mendelian Inheritance in Man is a catalog of human genes and genetic disorders.
• NetAffx Affymetrix’ NetAffx Analysis Center provides annotation resources for Affymetrix GeneChip technology.
• KEGG Kyoto Encyclopedia of Genes and Genomes; a collection of data resources including a rich collection of pathway data.
• IntAct Protein Interaction data, mainly derived from experiments.
• Pfam Pfam is a large collection of multiple sequence alignments and hidden Markov models covering manycommon protein domains and families.
Available Metadata
• Chromosomal Location Genes are identified with chromosomes, and where appropriate with strand.
• Data Archives The NCBI coordinates the Gene Expression Omnibus (GEO); TIGR provides the Resourcerer database, and the EBI runs ArrayExpress.
Available Metadata
• An early design decision was to provide metadata on a per chip-type basis (e.g. hgu133a, hgu95av2)
• Each annotation package contains objects that provide mappings between identifiers (genes, probes, …) and different types of annotation data
• One can list the content of a package:
> library("hgu133a")> ls("package:hgu133a")[1] "hgu133a" "hgu133aACCNUM"[3] "hgu133aCHR" "hgu133aCHRLENGTHS"[5] "hgu133aCHRLOC" "hgu133aENTREZID"[7] "hgu133aENZYME" "hgu133aENZYME2PROBE"[9] "hgu133aGENENAME" "hgu133aGO"[11] "hgu133aGO2ALLPROBES" "hgu133aGO2PROBE"[13] "hgu133aLOCUSID" "hgu133aMAP"[15] "hgu133aMAPCOUNTS" "hgu133aOMIM"[17] "hgu133aORGANISM" "hgu133aPATH"[19] "hgu133aPATH2PROBE" "hgu133aPFAM"[21] "hgu133aPMID" "hgu133aPMID2PROBE"[23] "hgu133aPROSITE" "hgu133aQC"[25] "hgu133aREFSEQ" "hgu133aSUMFUNC_DEPRECATED"[27] "hgu133aSYMBOL" "hgu133aUNIGENE"
Annotation Packages
A little bit of history...A little bit of history...(the pre-SQL era)
before: hgu95av2 now: hgu95av2.db
• Objects in annotation packages used to be environments, hash tables for mapping now things are stored in SQLite DB• Mapping only from one identifier to another, hard to reverse• quite unflexible• The user interface still supports many of the old environment-
specific interactions:
You can access the data directly using any of the standard subsetting or extraction tools for environments: get, mget, $ and [[.
> get("201473_at", hgu133aSYMBOL)[1] "JUNB"> mget(c("201473_at","201476_s_at"), hgu133aSYMBOL)$`201473_at`[1] "JUNB"$`201476_s_at`[1] "RRM1"> hgu133aSYMBOL$"201473_at"[1] "JUNB"> hgu133aSYMBOL[["201473_at"]][1] "JUNB"
Annotation Packages
Suppose we are interested in the gene BAD.
> gsyms <- unlist(as.list(hgu133aSYMBOL))> whBAD <- grep("^BAD$", gsyms)> gsyms[whBAD]1861_at 209364_at"BAD" "BAD"> hgu133aGENENAME$"1861_at"[1] "BCL2-antagonist of cell death"
Working with Metadata
Find the pathways that BAD is associated with.
> BADpath <- hgu133aPATH$"1861_at"> kegg <- mget(BADpath, KEGGPATHID2NAME)> unlist(kegg)01510"Neurodegenerative Disorders"04012"ErbB signaling pathway"04210"Apoptosis"04370
…"Colorectal cancer"05212"Pancreatic cancer"05213"Endometrial cancer"05215
Working with Metadata
We can get the GeneChip probes and the unique EntrezGene loci in each of these pathways. First, we obtain the Affymetrix IDs
> allProbes <- mget(BADpath, hgu133aPATH2PROBE)> length(allProbes)[1] 15> allProbes[[1]][1:10][1] "206679_at" "209462_at" "203381_s_at" "203382_s_at"[5] "212874_at" "212883_at" "212884_x_at" "200602_at"[9] "211277_x_at" "214953_s_at"
> sapply(allProbes, length)01510 04012 04210 04370 04510 04910 05030 05210 05212 0521385 169 162 137 413 243 39 167 156 11105215 05218 05220 05221 05223194 137 160 117 110
Working with Metadata
And then we can map these to their Entrez Gene values.
> getEG = function(x) unique(unlist(mget(x, hgu133aENTREZID)))> allEG = sapply(allProbes, getEG)> sapply(allEG, length)01510 04012 04210 04370 04510 04910 05030 05210 05212 0521337 84 81 67 187 130 18 82 72 5105215 05218 05220 05221 0522385 68 74 53 53
Working with Metadata
Data in the new .db annotation packages is stored in SQLite databases
much more efficient and flexible
old environment-style access provided by objects of class Bimap (package AnnotationDbi)
leftobject
rightobject
leftobject
rightobject
leftobject
rightobject
.db Packages
Data in the new .db annotation packages is stored in SQLite databases
much more efficient and flexible
old environment-style access provided by objects of class Bimap (package AnnotationDbi)
leftobject
rightobject
leftobject
rightobject
leftobject
rightobject
bipartite graph
name
attr1 = value1attr2 0 value2
.db Packages
• collection of classes and methods for database interaction
• they abstract the particular implementations of common standard operations on different types of databases
• resultSet: operations are performed on the database, the user controls how much information is returned
dbSendQuery create result set
dbGetQuery get all results
dbGetQuery(connection, sql query)
DBI
Notice that there are a few more entries here. They give you access to a connection to the database.
> library("hgu133a.db")> ls("package:hgu133a.db")[1] "hgu133aACCNUM" "hgu133aALIAS2PROBE"[3] "hgu133aCHR" "hgu133aCHRLENGTHS"[5] "hgu133aCHRLOC" "hgu133aENTREZID"[7] "hgu133aENZYME" "hgu133aENZYME2PROBE"[9] "hgu133aGENENAME" "hgu133aGO"[11] "hgu133aGO2ALLPROBES" "hgu133aGO2PROBE"[13] "hgu133aMAP" "hgu133aMAPCOUNTS"[15] "hgu133aOMIM" "hgu133aORGANISM"[17] "hgu133aPATH" "hgu133aPATH2PROBE"[19] "hgu133aPFAM" "hgu133aPMID"[21] "hgu133aPMID2PROBE" "hgu133aPROSITE"[23] "hgu133aREFSEQ" "hgu133aSYMBOL"[25] "hgu133aUNIGENE" "hgu133a_dbInfo"[27] "hgu133a_dbconn" "hgu133a_dbfile"[29] "hgu133a_dbschema"
.db Packages
> con <- hgu133a_dbconn()> q1 <- "select symbol from gene_info“> head(dbGetQuery(con ,q1)) symbol1 A2M2 NAT13 NAT24 SERPINA3
> toTable(hgu133aSYMBOL)[1:3,] probe_id symbol1 217757_at A2M2 214440_at NAT13 206797_at NAT2
extract information from a database table as data.frame
reverse mapping
> revmap(hgu133aSYMBOL)$BAD
[1] "1861_at" "209364_at"
Lkeys, Rkeys: Get left and right keys of a Bimap object
> head(Lkeys(hgu133aSYMBOL))[1] "1007_s_at" "1053_at" "117_at" "121_at" "1255_g_at" "1294_at"
> head(Rkeys(hgu133aSYMBOL))[1] "A2M" "NAT1" "NAT2" "SERPINA3" "AADAC" "AAMP"
> table(nhit(revmap(hgu133aSYMBOL)))
1 2 3 4 5 6 7 8 9 10 11 12 13 18 19 8101 2814 1273 475 205 77 19 15 5 3 4 1 2 1 1
nhit: number of hits for every left key in a Bimap object
<package>_dbschema()
database schemata of the package
e.g. hgu133a_dbschema()
<package>()
summary of tables, number of mapped elements, etc.
e.g. hgu133a()
<package>_dbInfo()
meta information about origin of the data, chip type, etc
e.g. hgu133a_dbInfo()
Metadata about Metadata
> hgu133a()Quality control information for hgu133a:
This package has the following mappings:
hgu133aACCNUM has 22283 mapped keys (of 22283 keys)hgu133aALIAS2PROBE has 51017 mapped keys (of 51017 keys)
…
hgu133aSYMBOL has 21382 mapped keys (of 22283 keys)hgu133aUNIGENE has 21291 mapped keys (of 22283 keys)
Additional Information about this package:
DB schema: HUMANCHIP_DBDB schema version: 1.0Organism: Homo sapiensDate for NCBI data: 2008-Apr2Date for GO data: 200803Date for KEGG data: 2008-Apr1Date for Golden Path data: 2006-Apr14Date for IPI data: 2008-Mar19Date for Ensembl data: 2007-Oct24
Bioconductor also provides some comprehensive annotations forwhole genomes (e.g. S. cerevisae). They follow a naming convention like: org.Hs.eg.db. Currently we are trying to support all widely used model organisms.
These packages are like the chip annotation packages, except adifferent set of primary keys is used (e.g. for yeast we use thesystematic names such as YBL088C)
> library("YEAST.db")> ls("package:YEAST.db")[1:12][1] "YEAST" "YEASTALIAS"[3] "YEASTCHR" "YEASTCHRLENGTHS"[5] "YEASTCHRLOC" "YEASTCOMMON2SYSTEMATIC"[7] "YEASTDESCRIPTION" "YEASTENZYME"[9] "YEASTENZYME2PROBE" "YEASTGENENAME"[11] "YEASTGO" "YEASTGO2ALLPROBES"
Annotating a Genome
„old-style“ vs SQL
example from GO: number of terms in the three different ontologies
BP CC MF
14598 2065 8268
old style:
> system.time(goCats <- unlist(eapply(GOTERM, Ontology)))User System Ellapsed 70.75 0.12 88.48> gCnums <- table(goCats)[c("BP","CC", "MF")]
SQL:
> system.time(goCats <- dbGetQuery(GO_dbconn(), "select ontology from go_term")) User System Ellapsed 0.07 0.00 0.07
• KEGG provides mappings from genes to pathways
• We provide these in the package KEGG.db, you can also query the site directly using KEGGSOAP or other software.
• One problem with the KEGG is that the data is not in a form that is amenable to computation.
KEGG
Data in KEGG.db package
KEGGEXTID2PATHID provides mapping from either EntrezGene (for human, mouse and rat) or Open Reading Frame (yeast) to KEGG pathway ID.KEGGPATHID2EXTID contains the mapping in the other direction.
KEGGPATHID2NAME provides mapping from KEGG pathway ID to a textual description of the pathway. Only the numeric part of the KEGG pathway identifiers is used (not the three letter species codes)
KEGG
Consider pathway 00362.
> KEGGPATHID2NAME$"00362"[1] "Benzoate degradation via hydroxylation„
Species specific mapping from pathway to genes is indicated by glueing together three letter species code, e. g. texttthsa, and numeric pathway code.
> KEGGPATHID2EXTID$hsa00362[1] "10449" "30" "3032" "59344" "83875"> KEGGPATHID2EXTID$sce00362[1] "YIL160C" "YKR009C"
Exploring KEGG
PAK1 has EntrezGene ID 5058 in humans
> KEGGEXTID2PATHID$"5058"[1] "hsa04010" "hsa04012" "hsa04360" "hsa04510" "hsa04650"[6] "hsa04660" "hsa04810" "hsa05120" "hsa05211"> KEGGPATHID2NAME$"04010"[1] "MAPK signaling pathway„
We find that it is involved in 9 pathways. For mice, the MAPKsignaling pathway contains
> mm <- KEGGPATHID2EXTID$mmu04010> length(mm)[1] 253> mm[1:10][1] "102626" "109689" "109880" "109905" "110157" "110651"[7] "114713" "11479" "11651" "11652"
Exploring KEGG
The annotate package
• functions for harvesting of curated persistent data sources
• functions for simple HTTP queries to web service providers
• interface code that provides common calling sequences for the assay based metadata packages such as getSEQ
• perform web queries to NCBI to extract the nucleotide sequence corresponding to a GenBank accession number.
> gsq <- getSEQ("M22490")> substring(gsq,1,40)[1] "GGCAGAGGAGGAGGGAGGGAGGGAAGGAGCGCGGAGCCCG"M22490: mapped to locus HUMBMP2B; Human bone morphogenetic
protein-2B (BMP-2B) mRNA.
Dynamic Annotation
• other interface functions include getGO, getSYMBOL, getPMID, and getLL
• functions whose names start with pm work with lists of PubMed identifiers for journal articles.
> hgu133aSYMBOL$"209905_at"[1] "HOXA9"> pm.getabst("209905_at", "hgu133a")$`209905_at`$`209905_at`[[1]]An object of class 'pubMedAbst':Title: Vertebrate homeobox gene nomenclature.PMID: 1358459Authors: MP ScottJournal: CellDate: Nov 1992
The annotate Package
BioMart
Generic data management system, collaboration between EBI and CSHL
Several query interfaces and administration tools
Conduct fast and powerful queries using:
website
webservice
graphical or text-oriented applications
software libraries written in Perl and Java.
http://www.ebi.ac.uk/biomart/
Ensembl
Joint project between EMBL-EBI and the Sanger Institute
Produces and maintains automatic annotation on selected eukaryotic genomes.
http://www.ensembl.org
Ensembl martview
Ensembl martview
VEGA
The Vertebrate Genome Annotation (VEGA) database is a central repository for high quality, frequently updated, manual annotation of vertebrate finished genome sequence.
Current release:• Human• Mouse• Zebrafish• Dog
http://vega.sanger.ac.uk
WormBase
WormBase is the repository of mapping, sequencing and phenotypic information for C. elegans (and some other nematodes).
http://www.wormbase.org
WormMart
GrameneMart
Gramene is a curated, open-source, Web-accessible data resource for comparative genome analysis in the grasses.
http://www.gramene.org
Gramene: A Comparative Mapping Resource for Grains
Other databases with BioMart interfaces • dbSNP (via Ensembl)• HapMap• Sequence Mart: Ensembl genome sequences
BioMart user interfaces
MartShellMartShell is a command line BioMart user interface based on a structured query language: Mart Query Language (MQL)
BioMart user interfaces
Martview Web based user interface for BioMart, provides functionality for remote users to query all databases hosted by the EBI's public BioMart server.
MartExplorer
Perl and Java libraries
biomaRt interface to R/Bioconductor
The biomaRt package
Developed by Steffen Durinck (started Feb 2005)
Two main sets of functions:
1. Tailored towards Ensembl, shortcuts for FAQs (frequently asked queries): getGene, getGO, getOMIM...
2. Generic queries, modeled after MQL (Mart query language), can be used with any BioMart dataset
Two communication protocols
1. Direct MySQL queries to BioMart database servers
2. HTTP queries to BioMart webservices
more stable (across database releases); self-reflective; less firewall problems
Getting started> library(biomaRt)> listMarts()
$biomart[1] "dicty" "ensembl" "snp" "vega" "uniprot" "msd" "wormbase"
$version[1] "DICTYBASE (NORTHWESTERN)" "ENSEMBL 38 (SANGER)" [3] "SNP 38 (SANGER)" "VEGA 38 (SANGER)" [5] "UNIPROT 4-5 (EBI)" "MSD 4 (EBI)" [7] "WORMBASE CURRENT (CSHL)"
$host[1] "www.dictybase.org" "www.biomart.org" "www.biomart.org" [4] "www.biomart.org" "www.biomart.org" "www.biomart.org" [7] "www.biomart.org"
$path[1] "" "/biomart/martservice" "/biomart/martservice"[4] "/biomart/martservice" "/biomart/martservice" "/biomart/martservice"[7] "/biomart/martservice"
Gene annotation
The function getGene allows you to get gene annotation for many types of identifiers
Supported identifiers are: Affymetrix Genechip Probeset ID
RefSeq
Entrez-Gene
EMBL
HUGO
Ensembl
soon Agilent identifiers will also be available
getGene> mart <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")> myProbes <- c("210708_x_at", "202763_at", "211464_x_at")> z <- getGene(id = myProbes, array = "affy_hg_u133_plus_2", mart = mart)
ID symbol1 202763_at CASP32 210708_x_at CASP107 211464_x_at CASP6 description1 Caspase-3 precursor (EC 3.4.22.-) (CASP-3) (Apopain) ...2 Caspase-10 precursor (EC 3.4.22.-) (CASP-10) (ICE-like apoptotic pro..7 Caspase-6 precursor (EC 3.4.22.-) (CASP-6) (Apoptotic protease Mch-2)...
chromosome band strand chromosome_start chromosome_end ensembl_gene_id1 4 q35.1 -1 185785845 185807623 ENSG000001643052 2 q33.1 1 201756100 201802372 ENSG000000034007 4 q25 -1 110829234 110844078 ENSG00000138794
ensembl_transcript_id1 ENST000003083942 ENST000002728797 ENST00000265164
Gene annotation
Note:
Ensembl does an independent mapping of affy probe sequences to genomes. If there is no clear match then that probe is not assigned to a gene.
Gene annotation
getGene returns a dataframe Gene symbol Description Chromosome name Band Start position End position BioMartID
getGene
> getGene(id = 100, type = "entrezgene", mart = mart)
ID symbol1 100 ADA description1 Adenosine deaminase (EC 3.5.4.4) (Adenosine aminohydrolase). [Source:Uniprot/SWISSPROT;Acc:P00813]
chromosome band strand chromosome_start chromosome_end ensembl_gene_id1 20 q13.12 -1 42681577 42713797 ENSG00000196839
ensembl_transcript_id1 ENST00000372874
Other functions
getGO: GO id, GO term, evidence code getOMIM (Online Mendelian Inheritance in Man, a
catalogue of human genes and genetic disorders): OMIM id, Disease, BioMart id
getINTERPRO (an integrated resource of protein families, domains and functional sites): Interpro id, description
getSequence getSNP getHomolog
getSequence
> seq <- getSequence(species="hsapiens", chromosome = 19, start = 18357968, end = 18360987, mart = mart)
chromosome [1] "19" start[1] 18357968 end[1] 18360987 sequence "AGTCCCAGCTCAGAGCCGCAACCTGCACAGCCATGCCCGGGCAAGAACTCAGGACGGTGAATGGCTCTCAGATGCTCCTGGTGTTGCTGGTGCTCTCGTGGCTGCCGCATGGGGGCGCCCTGTCTCTGGCCGAGGCGAGCCGCGCAAGTTTCCCGGGACCCTCAGAGTTGCACTCCGAAGACTCCAGATTCCGAGAGTTGCGGAAACGCTACGAGGACCTGCTAACCAGGCTGCGGGCCAACCAGAGCTGGGAAGATTCGAACACCGACCTCGTCCCGGCCCCTGCAGTCCGGATACTCACGCCAGAAGGTAAGTGAAATCTTAGAGATCCCCTCCCACCCCCCAAGCAGCCCCCATATCTAATCAGGGATTCCTCATCTTGAAAAGCCCAGACCTACCTGCGTATCTCTCGGGCCGCCCTTCCCGAGGGGCTCCCCGAGGCCTCCCGCCTTCACCGGGCTCTGTTCCGGCTGTCCCCGACGGCGTCAAGGTCGTGGGACGTGACACGACCGCTGCGGCGTCAGCTCAGCCTTGCAAGACCCCAGGCGCCCGCGCTGCACCTGCGACTGTCGCCGCCGCCGTCGCAGTCGGACCAACTGCTGGCAGAATCTTCGTCCGCACGGCCCCAGCTGGAGTTGCACTTGCGGCCGCAAGCCGCCAGGGGGCGCCGCAGAGCGCGTGCGCGCAACGGGGACCACTGTCCGCTCGGGCCCGGGCGTTGCTGCCGTCTGCACACGGTCCGCGCGTCGCTGGAAGACCTGGGCTGGGCCGATTGGGTGCTGTCGCCACGGGAGGTGCAAGTGACCATGTGCATCGGCGCGTGCCCGAGCCAGTTCCGGGCGGCAAACATG....
SNP
Single Nucleotide Polymorphisms (SNPs) are common DNA sequence variations among individuals.
e.g. AAGGCTAA and ATGGCTAA
biomaRt uses the SNP mart of Ensembl which is obtained from dbSNP
getSNP
> getSNP(chromosome = 8, start = 148350, end = 148612, mart = mart)
tsc refsnp_id allele chrom_start chrom_strand1 TSC1723456 rs3969741 C/A 148394 12 TSC1421398 rs4046274 C/A 148394 13 TSC1421399 rs4046275 A/G 148411 14 rs13291 C/T 148462 15 TSC1421400 rs4046276 C/T 148462 16 rs4483971 C/T 148462 17 rs17355217 C/T 148462 18 rs12019378 T/G 148471 19 TSC1421401 rs4046277 G/A 148499 110 rs11136408 G/A 148525 111 TSC1421402 rs4046278 G/A 148533 112 rs17419210 C/T 148533 -113 rs28735600 G/A 148533 114 TSC1737607 rs3965587 C/T 148535 115 rs4378731 G/A 148601 1
Homology mapping
The getHomolog function enables mapping of many types of identifiers from one species to the same or another type of identifier in another species.
getHomolog
> from.mart = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
> to.mart = useMart("ensembl", dataset = "mmusculus_gene_ensembl")
> getHomolog(id = 2, from.type = "entrezgene", to.type = "refseq",
+ from.mart = from.mart, to.mart = to.mart)
V1 V2 V3
1 ENSMUSG00000030111 ENSMUST00000032203 NM_175628
2 ENSMUSG00000059908 ENSMUST00000032228 NM_008645
3 ENSMUSG00000030131 ENSMUST00000081777 NM_008646
4 ENSMUSG00000071204 ENSMUST00000078431 NM_001013775
5 ENSMUSG00000030113 ENSMUST00000032206
6 ENSMUSG00000030359 ENSMUST00000032510 NM_007376
Find (microarray) probes of interest
getFeature function
Filter on: • gene location• symbol• OMIM• GO
getFeature> getFeature(symbol = "BRCA2", array = "affy_hg_u133_plus_2", mart = mart)
hgnc_symbol affy_hg_u133_plus_21 BRCA2 208368_s_at
> getFeature(chromosome = 1, start = 2800000, end = 3200000, type = "entrezgene", + mart = mart)
ensembl_transcript_id chromosome_name start_position end_position entrezgene1 ENST00000378404 1 2927907 2929327 1406252 ENST00000304706 1 2927907 2929327 1406253 ENST00000321336 1 2970496 2974193 4405564 ENST00000378398 1 2975621 3345045 639765 ENST00000378398 1 2975621 3345045 6478686 ENST00000270722 1 2975621 3345045 639767 ENST00000270722 1 2975621 3345045 6478688 ENST00000378391 1 2975621 3345045 639769 ENST00000378391 1 2975621 3345045 64786810 ENST00000378389 1 2975621 3345045 NA11 ENST00000378388 1 2975621 3345045 NA
getFeature
Select all RefSeq id’s involved in diabetes mellitus:
>getFeature( OMIM="diabetes mellitus",
type="refseq",
species="hsapiens",
mart=mart)
Ensembl Cross-references Powerful function to map between all possible
cross-references in Ensembl Can for example be used to map between
different Affymetrix arrays
Ensembl Cross-references getPossibleXrefs
Retrieves all possible cross-references
> xref <- getPossibleXrefs(mart = mart)
> xref[1:10, ]
species xref
[1,] "agambiae" "embl"
[2,] "agambiae" "pdb"
[3,] "agambiae" "prediction_sptrembl"
[4,] "agambiae" "protein_id"
[5,] "agambiae" "uniprot_accession"
[6,] "agambiae" "uniprot_id"
Ensembl Cross-references
>xref = getXref(id="1939_at",
from.species="hsapiens",
to.species = "mmusculus",
from.xref = "affy_hg_u95av2",
to.xref = "affy_mouse430_2",
mart=mart)
The generic interface:the getBM function
useDataset> library(biomaRt)
> mart <- useMart("ensembl")
> listDatasets(mart)
dataset version
1 rnorvegicus_gene_ensembl RGSC3.4
2 scerevisiae_gene_ensembl SGD1
3 celegans_gene_ensembl CEL150
4 cintestinalis_gene_ensembl JGI2
5 ptroglodytes_gene_ensembl CHIMP1A
6 frubripes_gene_ensembl FUGU4
7 agambiae_gene_ensembl AgamP3
8 hsapiens_gene_ensembl NCBI36
9 ggallus_gene_ensembl WASHUC1
10 xtropicalis_gene_ensembl JGI4.1
11 drerio_gene_ensembl ZFISH5
....(more)...
> mart <- useDataset(dataset = "hsapiens_gene_ensembl", mart = mart)
getBM> getBM(attributes = c("affy_hg_u95av2", "hgnc_symbol"),
filter = "affy_hg_u95av2",
values = c("1939_at", "1000_at"),
mart = mart)
affy_hg_u95av2 hgnc_symbol
1 1000_at MAPK3
3 1939_at TP53
mart – an object describing the database connection and the dataset
attributes – the name of the data you want to obtain
filter – the name of the data by which you want to filter from the dataset
values – values to filter on
Locally installed BioMarts
Main use case currently is to use biomaRt to query public BioMart servers over the internet
But you can also install BioMart server locally, populated with a copy of a public dataset (particular version), or populated with your own data
Versioning is supported by naming convention
Installation
bioMart depends on R packages Rcurl, XML, which require additional system libraries (libcurl, libxml2)
RMySQL package is optional
Platforms on which biomaRt has been installed:
Linux
Mac OS X
Windows
Discussion
Using biomaRt to query public webservices gets you started quickly, is easy and gives you access to a large body of metadata in a uniform way
Need to be online
Online metadata can change behind your back; although there is possibility of connecting to a particular, immutable version of a dataset
Watch this space – implementation of Bioconductor metadata packages is changing and improving! using the familiar packaging and versioning system