Transcript Folie 1 - unimi.it

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.
Available Metadata
• 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.
Annotation Packages
• 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"
A little bit of history...
(the pre-SQL era)
before: hgu95av2
now: hgu95av2.db
Annotation Packages
• 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 environmentspecific 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"
Working with Metadata
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 05213
85 169 162 137 413 243 39 167 156 111
05215 05218 05220 05221 05223
194 137 160 117 110
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 05213
37 84 81 67 187 130 18 82 72 51
05215 05218 05220 05221 05223
85 68 74 53 53
.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)
left
object
right
object
left
object
right
object
left
object
right
object
.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)
left
object
left
object
right
object
name
left
object
right
object
right
object
 bipartite graph
attr1 = value1
attr2 0 value2
DBI
• 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)
.db Packages
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"
> con <- hgu133a_dbconn()
> q1 <- "select symbol from gene_info“
> head(dbGetQuery(con ,q1))
symbol
1
A2M
2
NAT1
3
NAT2
4 SERPINA3
extract information from a database table as data.frame
> toTable(hgu133aSYMBOL)[1:3,]
probe_id symbol
1 217757_at
A2M
2 214440_at
NAT1
3 206797_at
NAT2
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"
nhit: number of hits for every left key in a Bimap object
> 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
3
4
1
2
1
1
19
15
5
Metadata about Metadata
<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()
> 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_DB
DB schema version: 1.0
Organism: Homo sapiens
Date for NCBI data: 2008-Apr2
Date for GO data: 200803
Date for KEGG data: 2008-Apr1
Date for Golden Path data: 2006-Apr14
Date for IPI data: 2008-Mar19
Date for Ensembl data: 2007-Oct24
Annotating a Genome
Bioconductor also provides some comprehensive annotations for
whole 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 a
different set of primary keys is used (e.g. for yeast we use the
systematic 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"
„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
• 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)
Exploring 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$"04010"
[1] "MAPK signaling pathway„
We find that it is involved in 9 pathways. For mice, the MAPK
signaling pathway contains
> mm <- KEGGPATHID2EXTID$mmu04010
> length(mm)
[1] 253
> mm[1:10]
[1] "102626" "109689" "109880" "109905" "110157" "110651"
[7] "114713" "11479" "11651" "11652"
Dynamic Annotation
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.
The annotate Package
• 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: 1358459
Authors: MP Scott
Journal: Cell
Date: Nov 1992
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.
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: A Comparative Mapping Resource for Grains
Gramene is a curated, open-source, Web-accessible data
resource for comparative genome analysis in the grasses.
http://www.gramene.org
Other databases with BioMart interfaces
• dbSNP (via Ensembl)
• HapMap
• Sequence Mart: Ensembl genome sequences
BioMart user interfaces
MartShell
MartShell 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
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"
[4] "www.biomart.org"
"www.biomart.org"
[7] "www.biomart.org"
"www.biomart.org"
"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 symbol
1
202763_at CASP3
2 210708_x_at CASP10
7 211464_x_at CASP6
description
1 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_id
1
4 q35.1
-1
185785845
185807623 ENSG00000164305
2
2 q33.1
1
201756100
201802372 ENSG00000003400
7
4
q25
-1
110829234
110844078 ENSG00000138794
ensembl_transcript_id
1
ENST00000308394
2
ENST00000272879
7
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 symbol
1 100
ADA
description
1 Adenosine deaminase (EC 3.5.4.4) (Adenosine aminohydrolase).
[Source:Uniprot/SWISSPROT;Acc:P00813]
chromosome
band strand chromosome_start chromosome_end ensembl_gene_id
1
20 q13.12
-1
42681577
42713797 ENSG00000196839
ensembl_transcript_id
1
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
"AGTCCCAGCTCAGAGCCGCAACCTGCACAGCCATGCCCGGGCAAGAACTCAGGACGGTGAATGGCTCTCAG
ATGCTCCTGGTGTTGCTGGTGCTCTCGTGGCTGCCGCATGGGGGCGCCCTGTCTCTGGCCGAGGCGAGCCGC
GCAAGTTTCCCGGGACCCTCAGAGTTGCACTCCGAAGACTCCAGATTCCGAGAGTTGCGGAAACGCTACGAG
GACCTGCTAACCAGGCTGCGGGCCAACCAGAGCTGGGAAGATTCGAACACCGACCTCGTCCCGGCCCCTGCA
GTCCGGATACTCACGCCAGAAGGTAAGTGAAATCTTAGAGATCCCCTCCCACCCCCCAAGCAGCCCCCATAT
CTAATCAGGGATTCCTCATCTTGAAAAGCCCAGACCTACCTGCGTATCTCTCGGGCCGCCCTTCCCGAGGGG
CTCCCCGAGGCCTCCCGCCTTCACCGGGCTCTGTTCCGGCTGTCCCCGACGGCGTCAAGGTCGTGGGACGTG
ACACGACCGCTGCGGCGTCAGCTCAGCCTTGCAAGACCCCAGGCGCCCGCGCTGCACCTGCGACTGTCGCCG
CCGCCGTCGCAGTCGGACCAACTGCTGGCAGAATCTTCGTCCGCACGGCCCCAGCTGGAGTTGCACTTGCGG
CCGCAAGCCGCCAGGGGGCGCCGCAGAGCGCGTGCGCGCAACGGGGACCACTGTCCGCTCGGGCCCGGGCGT
TGCTGCCGTCTGCACACGGTCCGCGCGTCGCTGGAAGACCTGGGCTGGGCCGATTGGGTGCTGTCGCCACGG
GAGGTGCAAGTGACCATGTGCATCGGCGCGTGCCCGAGCCAGTTCCGGGCGGCAAACATG....
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)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
tsc
TSC1723456
TSC1421398
TSC1421399
TSC1421400
TSC1421401
TSC1421402
TSC1737607
refsnp_id allele chrom_start chrom_strand
rs3969741
C/A
148394
1
rs4046274
C/A
148394
1
rs4046275
A/G
148411
1
rs13291
C/T
148462
1
rs4046276
C/T
148462
1
rs4483971
C/T
148462
1
rs17355217
C/T
148462
1
rs12019378
T/G
148471
1
rs4046277
G/A
148499
1
rs11136408
G/A
148525
1
rs4046278
G/A
148533
1
rs17419210
C/T
148533
-1
rs28735600
G/A
148533
1
rs3965587
C/T
148535
1
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_2
1
BRCA2
208368_s_at
> getFeature(chromosome = 1, start = 2800000, end = 3200000, type = "entrezgene",
+
mart = mart)
ensembl_transcript_id chromosome_name start_position end_position entrezgene
1
ENST00000378404
1
2927907
2929327
140625
2
ENST00000304706
1
2927907
2929327
140625
3
ENST00000321336
1
2970496
2974193
440556
4
ENST00000378398
1
2975621
3345045
63976
5
ENST00000378398
1
2975621
3345045
647868
6
ENST00000270722
1
2975621
3345045
63976
7
ENST00000270722
1
2975621
3345045
647868
8
ENST00000378391
1
2975621
3345045
63976
9
ENST00000378391
1
2975621
3345045
647868
10
ENST00000378389
1
2975621
3345045
NA
11
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)...
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