Contents

1 Introduction

InterMine is a powerful open source data warehouse system integrating diverse biological data sets (e.g. genomic, expression and protein data) for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. A selected list of databases powered by InterMine is shown in Table 1:

Database Organism Data
FlyMine Drosophila Genes, homology, proteins, interactions, gene ontology, expression, regulation, phenotypes, pathways, diseases, resources, publications
HumanMine H. sapiens Genomics, SNPs, GWAS, proteins, gene ontology, pathways, gene expression, interactions, publications, disease, orthologues, alleles
MouseMine M. musculus Genomics, proteins, gene ontology, expression, interactions, pathways, phenotypes, diseases, homology, publications
RatMine R. norvegicus Disease, gene ontology, genomics, interactions, phenotype, pathway, proteins, publication QTL, SNP
WormMine C. elegans Genes, alleles, homology, go annotation, phenotypes, strains
YeastMine S. cerevisiae Genomics, proteins, gene ontology, comparative genomics, phenotypes, interactions, literature, pathways, gene expression
ZebrafishMine D. rerio Genes, constructs, disease, gene ontology, genotypes, homology, morpholinos, phenotypes
TargetMine H. sapiens, M. musculus Genes, protein structures, chemical compounds, protein domains, gene function, pathways, interactions, disease, drug targets
MitoMiner H. sapiens, M. musculus, R. norvegicus, D. rerio, S. cerevisiae, S. pombe Genes, homology, localisation evidence, Mitochondrial reference gene lists, phenotypes, diseases, expression, interactions, pathways, exome
IndigoMine Archae Genomics
ThaleMine A. thaliana Genomics, proteins, domains, homology, gene ontology, interactions, expression, publications, pathways, GeneRIF, stocks, phenotypes, alleles, insertions, TAIR
MedicMine Medicago truncatula Genomics, pathways, gene ontology, publications, proteins, homology
PhytoMine over 50 plant genomes Genes, proteins, expression, transcripts, homology

Please see the InterMine home page for a full list of available InterMines.

InterMine includes an attractive, user-friendly web interface that works ‘out of the box’ and a powerful, scriptable web-service API to allow programmatic access to your data. This R package provides an interface with the InterMine-powered databases through Web services.

2 Jumpstart: How to build queries using InterMineR

Let’s start with a simple task - find the homologues of gene ABO.

2.1 Select a database

First, we look at what databases are available.

library(InterMineR)
listMines()
##                                                BMAP 
##                "https://bmap.jgi.doe.gov/bmapmine/" 
##                                            BeanMine 
##             "https://mines.legumeinfo.org/beanmine" 
##                                          BovineMine 
##                "http://bovinegenome.org/bovinemine" 
##                                             CHOmine 
##                "https://chomine.boku.ac.at/chomine" 
##                                        ChickpeaMine 
##         "https://mines.legumeinfo.org/chickpeamine" 
##                                          CowpeaMine 
##           "https://mines.legumeinfo.org/cowpeamine" 
##                                             FlyMine 
##                    "http://www.flymine.org/flymine" 
##                                           GrapeMine 
##          "http://urgi.versailles.inra.fr/GrapeMine" 
##                                           HumanMine 
##                "http://www.humanmine.org/humanmine" 
##                                     HymenopteraMine 
##      "http://hymenopteragenome.org/hymenopteramine" 
##                                          IndigoMine 
##               "http://www.cbrc.kaust.edu.sa/indigo" 
##                                          LegumeMine 
##           "https://mines.legumeinfo.org/legumemine" 
##                                           MaizeMine 
## "http://maizemine.rnet.missouri.edu:8080/maizemine" 
##                                           MedicMine 
##               "http://medicmine.jcvi.org/medicmine" 
##                                           MitoMiner 
##    "http://mitominer.mrc-mbu.cam.ac.uk/release-4.0" 
##                                             ModMine 
##         "http://intermine.modencode.org/release-33" 
##                                           MouseMine 
##                "http://www.mousemine.org/mousemine" 
##                                             OakMine 
##      "https://urgi.versailles.inra.fr/OakMine_PM1N" 
##                                          PeanutMine 
##           "https://mines.legumeinfo.org/peanutmine" 
##                                           PhytoMine 
##          "https://phytozome.jgi.doe.gov/phytomine/" 
##                                            PlanMine 
##               "http://planmine.mpi-cbg.de/planmine" 
##                                             RatMine 
##                    "http://ratmine.mcw.edu/ratmine" 
##                                             RepetDB 
##            "http://urgi.versailles.inra.fr/repetdb" 
##                                             SoyMine 
##              "https://mines.legumeinfo.org/soymine" 
##                                          TargetMine 
##     "http://targetmine.mizuguchilab.org/targetmine" 
##                                           TetraMine 
##              "http://adenine.bradley.edu/tetramine" 
##                                           ThaleMine 
##                "https://apps.araport.org/thalemine" 
##                                           WheatMine 
##         "https://urgi.versailles.inra.fr/WheatMine" 
##                                            WormMine 
##     "http://intermine.wormbase.org/tools/wormmine/" 
##                                             XenMine 
##                    "http://www.xenmine.org/xenmine" 
##                                           YeastMine 
##       "https://yeastmine.yeastgenome.org/yeastmine" 
##                                       ZebrafishMine 
##                          "http://zebrafishmine.org"

Since we would like to query human genes, we select HumanMine.

# load HumaMine
im <- initInterMine(mine=listMines()["HumanMine"])
im
## $mine
##                            HumanMine 
## "http://www.humanmine.org/humanmine" 
## 
## $token
## [1] ""

2.2 Obtain a prebuilt query

Both in InterMine database website and in InterMineR, you are able to build custom queries. However, to facilitate the retrieval of information from InterMine databases, a variety of pre-built queries, called templates, have also been made available. Templates are queries that have already been created with a fixed set of output columns and one or more constraints.

# Get template (collection of pre-defined queries)
template = getTemplates(im)
head(template)
##                         name
## 1 Tissue_Expression_illumina
## 2          humDisGeneOrthol2
## 3              PhenotypeGene
## 4    Protein_complex_details
## 5                disExprGene
## 6       protein_interactions
##                                                  title
## 1       Tissue --> Gene Expression (Illumina body map)
## 2    Human Disease --> Human Gene + Orthologue Gene(s)
## 3 Mouse Phenotype -->  Mouse Genes + Orthologous genes
## 4                          Protein --> Protein Complex
## 5                         Disease Expression --> Genes
## 6                             Protein --> Interactions

We would like to find templates involving genes.

# Get gene-related templates
template[grep("gene", template$name, ignore.case=TRUE),]
##                               name
## 2                humDisGeneOrthol2
## 3                    PhenotypeGene
## 5                      disExprGene
## 7               Gene_Interactions2
## 8               Protein_Gene_Ortho
## 9                      GOterm_Gene
## 11           Gene_Alleles_Disease2
## 13      ChromRegion_GenesTransExon
## 16                     GeneExpress
## 17                  Disease_Genes2
## 18                   Gene_Location
## 19    Protein_GeneChromosomeLength
## 20                Gene_Identifiers
## 22                    Gene_Pathway
## 23            gene_complex_details
## 26                    PathwayGenes
## 28                    Gene_Protein
## 29          Gene_OverlapppingGenes
## 32            Gene_To_Publications
## 33 Gene_Interactions_forReportPage
## 35                Gene_Disease_HPO
## 36                         Gene_GO
## 38       GeneInteractorsExpression
## 39     Gene_particularGoannotation
## 40   Gene_TissueExpressionIllumina
## 41             Gene_HPOphenotype_2
## 42             domain_protein_gene
## 43            Gene_Expression_GTex
## 44             Pathway_ProteinGene
## 47           GeneHPOparent_Genes_2
## 48              Gene_proteindomain
## 49                     Gene_inGWAS
## 50               geneGWAS_reportPg
## 51             geneInteractiongene
## 52                   Gene_Disease2
## 53         Term_inGWASoptionalGene
## 54    Gene_proteinAtlasExpression2
## 55                  GeneOrthAllele
## 59                       Gene_Orth
## 60               ChromRegion_Genes
## 62       GenePathway_interactions2
## 63                 Gene_AllelePhen
##                                                                          title
## 2                            Human Disease --> Human Gene + Orthologue Gene(s)
## 3                         Mouse Phenotype -->  Mouse Genes + Orthologous genes
## 5                                                 Disease Expression --> Genes
## 7                                                        Gene --> Interactions
## 8                                             Protein --> Gene and Orthologues
## 9                                                            GO term --> Genes
## 11                                                Gene --> Alleles and Disease
## 13                    Chromosomal Location --> All Genes + Transcripts + Exons
## 16       Gene --> Gene Expression  (Tissue, Disease; Array Express, E-MTAB-62)
## 17                                                         Disease --> Gene(s)
## 18                                              Gene --> Chromosomal location.
## 19                                                           Protein --> Gene.
## 20                                                   Gene --> All identifiers.
## 22                                                            Gene --> Pathway
## 23                                                    Gene --> protein complex
## 26                                                           Pathway --> Genes
## 28                                                          Gene --> Proteins.
## 29                                                 Gene --> Overlapping genes.
## 32                                                      Gene --> Publications.
## 33                                  Gene --> Physical and Genetic Interactions
## 35                Gene --> Disease + HPO annotations (Human Phenotype Ontology
## 36                                                          Gene --> GO terms.
## 38 Gene + Tissue Expression  --> Interactors that are expressed in that tissue
## 39                                         Gene + GO term --> Genes by GO term
## 40                              Gene --> Tissue Expression (Illumina body map)
## 41                           Gene -> HPO annotation (Human Phenotype Ontology)
## 42                                        Protein Domain --> Protein and Genes
## 43                                      Gene --> Tissue Expression (GTex data)
## 44                                                Pathway --> Protein and Gene
## 47                                   Gene + HPO Phenotype parent term -> Genes
## 48                                                  Gene --> Protein + Domains
## 49                                                           Gene --> GWAS hit
## 50                                                    Gene Report --> GWAS hit
## 51                                           Gene A --> Interaction <-- Gene B
## 52                                                     Gene --> Disease (OMIM)
## 53                            GWAS term --> SNP + Associated gene if available
## 54                                        Gene --> Protein tissue Localisation
## 55                              Gene (Hum OR Rat) --> Mouse Allele (Phenotype)
## 59                                                        Gene --> Orthologues
## 60                                                            Region --> Genes
## 62                                             Gene + Pathway --> Interactions
## 63                                           Mouse Gene --> Allele [Phenotype]

The template Gene_Orth seems to be what we want. Let’s look at this template in more detail.

# Query for gene orthologs
queryGeneOrth = getTemplateQuery(
  im = im, 
  name = "Gene_Orth"
)
queryGeneOrth
## $model
##      name 
## "genomic" 
## 
## $title
## [1] "Gene --> Orthologues"
## 
## $description
## [1] "For a given Gene (or List of Genes) in named organism (default: Human) returns the orthologues in a different organisms. [keywords: homologue, homolog, paralogue, paralogue, ortholog]"
## 
## $select
## [1] "Gene.primaryIdentifier"                      
## [2] "Gene.symbol"                                 
## [3] "Gene.homologues.homologue.primaryIdentifier" 
## [4] "Gene.homologues.homologue.symbol"            
## [5] "Gene.homologues.homologue.organism.shortName"
## 
## $name
## [1] "Gene_Orth"
## 
## $comment
## [1] ""
## 
## $orderBy
## $orderBy[[1]]
## Gene.symbol 
##       "ASC" 
## 
## 
## $where
## $where[[1]]
## $where[[1]]$path
## [1] "Gene"
## 
## $where[[1]]$op
## [1] "LOOKUP"
## 
## $where[[1]]$code
## [1] "A"
## 
## $where[[1]]$editable
## [1] TRUE
## 
## $where[[1]]$switchable
## [1] FALSE
## 
## $where[[1]]$switched
## [1] "LOCKED"
## 
## $where[[1]]$value
## [1] "PPARG"
## 
## $where[[1]]$extraValue
## [1] "H. sapiens"

There are three essential members in a query - SELECT, WHERE and constraintLogic.

  1. SELECT
    1. The SELECT (or view) represents the output columns in the query output.
    2. Columns of a view are usually of the form “A.B”, where B is the child of A. For example in the column Gene.symbol, symbol is the child of Gene. Columns could also be in cascade form “A.B.C”. For example, in the column Gene.locations.start, locations is the child of Gene and start is the child of locations.
  2. WHERE
    1. The WHERE statement is a collection of constraints.
    2. Query constraints include a list of the following columns:
      1. path
        1. in the same format as view columns
      2. op
        1. the constraint operator
        2. Valid values: “=”, “!=”, “LOOKUP”, “ONE OF”, “NONE OF”, “>”, “<”, “>=”, “<=”, “LIKE”
      3. value 1. the constraint value
      4. code
        1. Ignore
        2. The logic code for the constraint (e.g. A, B or C).
        3. Only used in the constrainLogic (discussed below
      5. extraValue
        1. optional, required for LOOKUP constraints
        2. Short name of organism, e.g. H. sapiens
        1. Editable
          1. Ignore
          2. Used to determine if user is allowed to edit this constraint. Only for the UI.
      6. Switchable
        1. Ignore
        2. Used to determine if user is allowed to disable this constraint.
          Only for the UI.
      7. Switched
        1. Ignore
        2. Used to determine if user has enabled this constraint. Only for the UI.
  3. constraintLogic
    1. Constraint Logic, if not explicitly given, is “AND” operation, e.g., “A and B”, where A and B are the codes in the constraints.

2.2.1 Look at the data model

What does ‘Gene.symbol’ mean? What is ‘Gene.homologues.homologue.symbol’?

Let’s take a look at the data model.

model <- getModel(im)
head(model)
##     type            child_name child_type
## 1 Allele             Alternate           
## 2 Allele Clinical Significance           
## 3 Allele                    Id           
## 4 Allele                  Name           
## 5 Allele    Primary Identifier           
## 6 Allele             Reference

Let’s look at the children of the Gene data type.

model[which(model$type=="Gene"),]
##     type                 child_name             child_type
## 924 Gene          Brief Description                       
## 925 Gene       Cytological Location                       
## 926 Gene                Description                       
## 927 Gene                         Id                       
## 928 Gene                     Length                       
## 929 Gene                       Name                       
## 930 Gene         Primary Identifier                       
## 931 Gene                      Score                       
## 932 Gene                 Score Type                       
## 933 Gene       Secondary Identifier                       
## 934 Gene                     Symbol                       
## 935 Gene                    alleles                 Allele
## 936 Gene            atlasExpression        AtlasExpression
## 937 Gene                       CDSs                    CDS
## 938 Gene                 chromosome             Chromosome
## 939 Gene            crossReferences         CrossReference
## 940 Gene                   dataSets                DataSet
## 941 Gene                   diseases                Disease
## 942 Gene                      exons                   Exon
## 943 Gene               goAnnotation           GOAnnotation
## 944 Gene            flankingRegions     GeneFlankingRegion
## 945 Gene                 homologues              Homologue
## 946 Gene               interactions            Interaction
## 947 Gene downstreamIntergenicRegion       IntergenicRegion
## 948 Gene   upstreamIntergenicRegion       IntergenicRegion
## 949 Gene                    introns                 Intron
## 950 Gene         chromosomeLocation               Location
## 951 Gene            locatedFeatures               Location
## 952 Gene                  locations               Location
## 953 Gene        ontologyAnnotations     OntologyAnnotation
## 954 Gene                   organism               Organism
## 955 Gene                   pathways                Pathway
## 956 Gene                  probeSets               ProbeSet
## 957 Gene                   proteins                Protein
## 958 Gene     proteinAtlasExpression ProteinAtlasExpression
## 959 Gene               publications            Publication
## 960 Gene              rnaSeqResults           RNASeqResult
## 961 Gene          regulatoryRegions       RegulatoryRegion
## 962 Gene                       SNPs                    SNP
## 963 Gene       sequenceOntologyTerm                 SOTerm
## 964 Gene                   sequence               Sequence
## 965 Gene              childFeatures        SequenceFeature
## 966 Gene        overlappingFeatures        SequenceFeature
## 967 Gene                     strain                 Strain
## 968 Gene                   synonyms                Synonym
## 969 Gene                transcripts             Transcript
## 970 Gene                       UTRs                    UTR

Gene has a field called ‘symbol’ (hence the column Gene.symbol). Gene also has a child called homologues, which is of the Homologue data type.

model[which(model$type=="Homologue"),]
##           type      child_name         child_type
## 1091 Homologue              Id                   
## 1092 Homologue            Type                   
## 1093 Homologue crossReferences     CrossReference
## 1094 Homologue        dataSets            DataSet
## 1095 Homologue            gene               Gene
## 1096 Homologue       homologue               Gene
## 1097 Homologue        evidence OrthologueEvidence

Homologue has a child called ‘gene’ which is of the type ‘Gene’, which we saw above has a field called ‘symbol’ (hence the column Gene.homologues.homologue.symbol).

2.3 Run a Query

Let’s now run our template.

resGeneOrth <- runQuery(im, queryGeneOrth)
head(resGeneOrth)
##   Gene.primaryIdentifier Gene.symbol
## 1                   5468       PPARG
## 2                   5468       PPARG
## 3                   5468       PPARG
## 4                   5468       PPARG
## 5                   5468       PPARG
## 6                   5468       PPARG
##   Gene.homologues.homologue.primaryIdentifier
## 1                                       10062
## 2                                        5465
## 3                                        5467
## 4                                        5914
## 5                                        5915
## 6                                        5916
##   Gene.homologues.homologue.symbol
## 1                            NR1H3
## 2                            PPARA
## 3                            PPARD
## 4                             RARA
## 5                             RARB
## 6                             RARG
##   Gene.homologues.homologue.organism.shortName
## 1                                   H. sapiens
## 2                                   H. sapiens
## 3                                   H. sapiens
## 4                                   H. sapiens
## 5                                   H. sapiens
## 6                                   H. sapiens

3 Modify a Query

3.1 Edit a constraint

Let’s modify the query to find the orthologues of the gene ABO. We want to change the ‘value’ attribute from PPARG to ABO.

There are two ways to build a query in InterMineR.

  1. We can either build a query as a list object with newQuery function, and assign all input values (selection of retrieved data type, constraints, etc.) as items of that list,

  2. Or we can build the query as an InterMineR-class object with the functions setConstraint, which allows us to generate a new or modify an existing list of constraints, and setQuery, which generates the query as a InterMineR-class object.

setConstraints and setQuery functions are designed to facilitate the generation of queries for InterMine instances and avoid using multiple iterative loops, especially when it is required to include multiple constraints or constraint values (e.g. genes, organisms) in your query.

# modify directly the value of the first constraint from the list query
queryGeneOrth$where[[1]][["value"]] <- "ABO"

# or modify the value of the first constraint from the list query with setConstraints
queryGeneOrth$where = setConstraints(
  modifyQueryConstraints = queryGeneOrth,
  m.index = 1,
  values = list("ABO")
)

queryGeneOrth$where
## [[1]]
## [[1]]$path
## [1] "Gene"
## 
## [[1]]$op
## [1] "LOOKUP"
## 
## [[1]]$code
## [1] "A"
## 
## [[1]]$editable
## [1] TRUE
## 
## [[1]]$switchable
## [1] FALSE
## 
## [[1]]$switched
## [1] "LOCKED"
## 
## [[1]]$value
## [1] "ABO"
## 
## [[1]]$extraValue
## [1] "H. sapiens"

Note the value is now equal to ‘ABO’. Let’s rerun our query with the new constraint.

resGeneOrth <- runQuery(im, queryGeneOrth)
head(resGeneOrth)
##   Gene.primaryIdentifier Gene.symbol
## 1                     28         ABO
## 2                     28         ABO
## 3                     28         ABO
## 4                     28         ABO
## 5                     28         ABO
## 6                     28         ABO
##   Gene.homologues.homologue.primaryIdentifier
## 1                                      127550
## 2                                       26301
## 3                                      360203
## 4                                 MGI:2135738
## 5                                 RGD:1311152
## 6                                 RGD:2307241
##   Gene.homologues.homologue.symbol
## 1                          A3GALT2
## 2                            GBGT1
## 3                           GLT6D1
## 4                              Abo
## 5                             Abo2
## 6                              Abo
##   Gene.homologues.homologue.organism.shortName
## 1                                   H. sapiens
## 2                                   H. sapiens
## 3                                   H. sapiens
## 4                                  M. musculus
## 5                                R. norvegicus
## 6                                R. norvegicus

Now we are seeing orthologues for the ABO gene. Let’s add the organism to the view to make sure we are looking at the desired gene.

3.2 Add a new constraint

You can also add additional filters. Let’s exclude all homologues where organism is H. sapiens.

There are four parts of a constraint to add:

  1. path
    1. I got the path from the output columns but I could have figured out it from the data model.
  2. op
    1. Valid values: “=”, “!=”, “LOOKUP”, “ONE OF”, “NONE OF”, “>”, “<”, “>=”, “<=”, “LIKE”
  3. value
    1. What value I am filtering on.
  4. code
    1. Must be a letter not in use by the query already. Looking at the query output above we can see we only have one constraint, labelled ‘A’. Let’s use ‘B’ for our code.
newConstraint <- list(
  path=c("Gene.homologues.homologue.organism.shortName"),
  op=c("!="), 
  value=c("H. sapiens"), 
  code=c("B")
)

queryGeneOrth$where[[2]] <- newConstraint
queryGeneOrth$where
## [[1]]
## [[1]]$path
## [1] "Gene"
## 
## [[1]]$op
## [1] "LOOKUP"
## 
## [[1]]$code
## [1] "A"
## 
## [[1]]$editable
## [1] TRUE
## 
## [[1]]$switchable
## [1] FALSE
## 
## [[1]]$switched
## [1] "LOCKED"
## 
## [[1]]$value
## [1] "ABO"
## 
## [[1]]$extraValue
## [1] "H. sapiens"
## 
## 
## [[2]]
## [[2]]$path
## [1] "Gene.homologues.homologue.organism.shortName"
## 
## [[2]]$op
## [1] "!="
## 
## [[2]]$value
## [1] "H. sapiens"
## 
## [[2]]$code
## [1] "B"

Our new filter has been added successfully. Rerun the query and you see you only have non-Homo sapiens orthologues.

resGeneOrth <- runQuery(im, queryGeneOrth)
resGeneOrth
##    Gene.primaryIdentifier Gene.symbol
## 1                      28         ABO
## 2                      28         ABO
## 3                      28         ABO
## 4                      28         ABO
## 5                      28         ABO
## 6                      28         ABO
## 7                      28         ABO
## 8                      28         ABO
## 9                      28         ABO
## 10                     28         ABO
## 11                     28         ABO
## 12                     28         ABO
##    Gene.homologues.homologue.primaryIdentifier
## 1                                  MGI:2135738
## 2                                  RGD:1311152
## 3                                  RGD:2307241
## 4                                   RGD:628609
## 5                            ZDB-GENE-031204-4
## 6                         ZDB-GENE-040426-1117
## 7                           ZDB-GENE-040912-46
## 8                           ZDB-GENE-060531-15
## 9                           ZDB-GENE-060531-59
## 10                          ZDB-GENE-060531-70
## 11                          ZDB-GENE-060531-71
## 12                          ZDB-GENE-081104-23
##    Gene.homologues.homologue.symbol
## 1                               Abo
## 2                              Abo2
## 3                               Abo
## 4                              Abo3
## 5                                  
## 6                                  
## 7                                  
## 8                                  
## 9                                  
## 10                                 
## 11                                 
## 12                                 
##    Gene.homologues.homologue.organism.shortName
## 1                                   M. musculus
## 2                                 R. norvegicus
## 3                                 R. norvegicus
## 4                                 R. norvegicus
## 5                                      D. rerio
## 6                                      D. rerio
## 7                                      D. rerio
## 8                                      D. rerio
## 9                                      D. rerio
## 10                                     D. rerio
## 11                                     D. rerio
## 12                                     D. rerio

3.3 Add a column

You can also add additional columns to the output. For instance, where do these homologues come from? Let’s add this information.

Let’s see what we know about homologues.

model[which(model$type=="Homologue"),]
##           type      child_name         child_type
## 1091 Homologue              Id                   
## 1092 Homologue            Type                   
## 1093 Homologue crossReferences     CrossReference
## 1094 Homologue        dataSets            DataSet
## 1095 Homologue            gene               Gene
## 1096 Homologue       homologue               Gene
## 1097 Homologue        evidence OrthologueEvidence

The Homologue data type has an ‘dataSets’ reference of type ‘DataSet’.

model[which(model$type=="DataSet"),]
##        type  child_name  child_type
## 648 DataSet Description            
## 649 DataSet          Id            
## 650 DataSet        Name            
## 651 DataSet         URL            
## 652 DataSet     Version            
## 653 DataSet bioEntities   BioEntity
## 654 DataSet  dataSource  DataSource
## 655 DataSet publication Publication

DataSet has a child called name. Add Gene.homologues.dataSets.name to the view. We’ll add it as the last column, we can see from above there are 5 other columns already so we’ll put it as #6:

# use setQuery function which will create an InterMineR-class query
queryGeneOrth.InterMineR = setQuery(
  inheritQuery = queryGeneOrth,
  select = c(queryGeneOrth$select, 
             "Gene.homologues.dataSets.name")
  )

getSelect(queryGeneOrth.InterMineR)
## [1] "Gene.primaryIdentifier"                      
## [2] "Gene.symbol"                                 
## [3] "Gene.homologues.homologue.primaryIdentifier" 
## [4] "Gene.homologues.homologue.symbol"            
## [5] "Gene.homologues.homologue.organism.shortName"
## [6] "Gene.homologues.dataSets.name"
#queryGeneOrth.InterMineR@select

# or assign new column directly to the existing list query
queryGeneOrth$select[[6]] <- "Gene.homologues.dataSets.name"
queryGeneOrth$select
## [1] "Gene.primaryIdentifier"                      
## [2] "Gene.symbol"                                 
## [3] "Gene.homologues.homologue.primaryIdentifier" 
## [4] "Gene.homologues.homologue.symbol"            
## [5] "Gene.homologues.homologue.organism.shortName"
## [6] "Gene.homologues.dataSets.name"
# run queries
resGeneOrth.InterMineR <- runQuery(im, queryGeneOrth.InterMineR)
resGeneOrth <- runQuery(im, queryGeneOrth)

all(resGeneOrth == resGeneOrth.InterMineR)
## [1] TRUE
head(resGeneOrth, 3)
##   Gene.primaryIdentifier Gene.symbol
## 1                     28         ABO
## 2                     28         ABO
## 3                     28         ABO
##   Gene.homologues.homologue.primaryIdentifier
## 1                                 MGI:2135738
## 2                                 RGD:1311152
## 3                                 RGD:2307241
##   Gene.homologues.homologue.symbol
## 1                              Abo
## 2                             Abo2
## 3                              Abo
##   Gene.homologues.homologue.organism.shortName
## 1                                  M. musculus
## 2                                R. norvegicus
## 3                                R. norvegicus
##          Gene.homologues.dataSets.name
## 1 Orthologue and paralogue predictions
## 2 Orthologue and paralogue predictions
## 3 Orthologue and paralogue predictions

NB: adding columns can result in changing the row count.

3.4 Change constraint logic

The constraintLogic, if not given, is ‘A and B’. We would now try to explicitly specify the constraintLogic. A and B corresponds to the ‘code’ for each constraint.

queryGeneOrth$constraintLogic <- "A and B"
queryGeneOrth$constraintLogic
## [1] "A and B"

Run the query again and see no change:

resGeneOrth <- runQuery(im, queryGeneOrth)
resGeneOrth
##    Gene.primaryIdentifier Gene.symbol
## 1                      28         ABO
## 2                      28         ABO
## 3                      28         ABO
## 4                      28         ABO
## 5                      28         ABO
## 6                      28         ABO
## 7                      28         ABO
## 8                      28         ABO
## 9                      28         ABO
## 10                     28         ABO
## 11                     28         ABO
## 12                     28         ABO
##    Gene.homologues.homologue.primaryIdentifier
## 1                                  MGI:2135738
## 2                                  RGD:1311152
## 3                                  RGD:2307241
## 4                                   RGD:628609
## 5                            ZDB-GENE-031204-4
## 6                         ZDB-GENE-040426-1117
## 7                           ZDB-GENE-040912-46
## 8                           ZDB-GENE-060531-15
## 9                           ZDB-GENE-060531-59
## 10                          ZDB-GENE-060531-70
## 11                          ZDB-GENE-060531-71
## 12                          ZDB-GENE-081104-23
##    Gene.homologues.homologue.symbol
## 1                               Abo
## 2                              Abo2
## 3                               Abo
## 4                              Abo3
## 5                                  
## 6                                  
## 7                                  
## 8                                  
## 9                                  
## 10                                 
## 11                                 
## 12                                 
##    Gene.homologues.homologue.organism.shortName
## 1                                   M. musculus
## 2                                 R. norvegicus
## 3                                 R. norvegicus
## 4                                 R. norvegicus
## 5                                      D. rerio
## 6                                      D. rerio
## 7                                      D. rerio
## 8                                      D. rerio
## 9                                      D. rerio
## 10                                     D. rerio
## 11                                     D. rerio
## 12                                     D. rerio
##           Gene.homologues.dataSets.name
## 1  Orthologue and paralogue predictions
## 2  Orthologue and paralogue predictions
## 3  Orthologue and paralogue predictions
## 4  Orthologue and paralogue predictions
## 5  Orthologue and paralogue predictions
## 6  Orthologue and paralogue predictions
## 7  Orthologue and paralogue predictions
## 8  Orthologue and paralogue predictions
## 9  Orthologue and paralogue predictions
## 10 Orthologue and paralogue predictions
## 11 Orthologue and paralogue predictions
## 12 Orthologue and paralogue predictions

Change to be ‘A or B’ and see how the results change.

4 Recipes

4.1 Obtain the gene ontology (GO) terms associated with gene ABO

  • Start with the template Gene GO
queryGeneGO <- getTemplateQuery(im, "Gene_GO")
queryGeneGO
## $model
##      name 
## "genomic" 
## 
## $title
## [1] "Gene --> GO terms."
## 
## $description
## [1] "Search for GO annotations for a particular gene (or List of Genes)."
## 
## $select
## [1] "Gene.primaryIdentifier"                           
## [2] "Gene.symbol"                                      
## [3] "Gene.goAnnotation.ontologyTerm.identifier"        
## [4] "Gene.goAnnotation.ontologyTerm.name"              
## [5] "Gene.goAnnotation.ontologyTerm.namespace"         
## [6] "Gene.goAnnotation.evidence.code.code"             
## [7] "Gene.goAnnotation.ontologyTerm.parents.identifier"
## [8] "Gene.goAnnotation.ontologyTerm.parents.name"      
## [9] "Gene.goAnnotation.qualifier"                      
## 
## $name
## [1] "Gene_GO"
## 
## $comment
## [1] "Added 15NOV2010: ML"
## 
## $orderBy
## $orderBy[[1]]
## Gene.primaryIdentifier 
##                  "ASC" 
## 
## 
## $where
## $where[[1]]
## $where[[1]]$path
## [1] "Gene"
## 
## $where[[1]]$op
## [1] "LOOKUP"
## 
## $where[[1]]$code
## [1] "A"
## 
## $where[[1]]$editable
## [1] TRUE
## 
## $where[[1]]$switchable
## [1] FALSE
## 
## $where[[1]]$switched
## [1] "LOCKED"
## 
## $where[[1]]$value
## [1] "PPARG"
## 
## $where[[1]]$extraValue
## [1] "H. sapiens"
  • Modify the view to display a compact view
queryGeneGO$select <- queryGeneGO$select[2:5]
queryGeneGO$select
## [1] "Gene.symbol"                              
## [2] "Gene.goAnnotation.ontologyTerm.identifier"
## [3] "Gene.goAnnotation.ontologyTerm.name"      
## [4] "Gene.goAnnotation.ontologyTerm.namespace"
  • Modify the constraints to look for gene ABO.
queryGeneGO$where[[1]][["value"]] <- "ABO"
queryGeneGO$where
## [[1]]
## [[1]]$path
## [1] "Gene"
## 
## [[1]]$op
## [1] "LOOKUP"
## 
## [[1]]$code
## [1] "A"
## 
## [[1]]$editable
## [1] TRUE
## 
## [[1]]$switchable
## [1] FALSE
## 
## [[1]]$switched
## [1] "LOCKED"
## 
## [[1]]$value
## [1] "ABO"
## 
## [[1]]$extraValue
## [1] "H. sapiens"
  • Run the query
resGeneGO <- runQuery(im, queryGeneGO )
head(resGeneGO)
##   Gene.symbol Gene.goAnnotation.ontologyTerm.identifier
## 1         ABO                                GO:0000166
## 2         ABO                                GO:0003823
## 3         ABO                                GO:0004380
## 4         ABO                                GO:0004381
## 5         ABO                                GO:0005576
## 6         ABO                                GO:0005794
##                                                Gene.goAnnotation.ontologyTerm.name
## 1                                                               nucleotide binding
## 2                                                                  antigen binding
## 3 glycoprotein-fucosylgalactoside alpha-N-acetylgalactosaminyltransferase activity
## 4                        fucosylgalactoside 3-alpha-galactosyltransferase activity
## 5                                                             extracellular region
## 6                                                                  Golgi apparatus
##   Gene.goAnnotation.ontologyTerm.namespace
## 1                       molecular_function
## 2                       molecular_function
## 3                       molecular_function
## 4                       molecular_function
## 5                       cellular_component
## 6                       cellular_component

4.2 Obtain the genes associated with gene ontology (GO) term ‘metal ion binding’

  • Start with the template Gene GO
queryGOGene <- getTemplateQuery(im, "GOterm_Gene")
queryGOGene
## $model
##      name 
## "genomic" 
## 
## $title
## [1] "GO term --> Genes"
## 
## $description
## [1] "Search for Genes in a specified organism that are associated with a particular Gene Ontology (GO) annotation."
## 
## $select
## [1] "Gene.primaryIdentifier"                   
## [2] "Gene.symbol"                              
## [3] "Gene.name"                                
## [4] "Gene.goAnnotation.ontologyTerm.identifier"
## [5] "Gene.goAnnotation.ontologyTerm.name"      
## [6] "Gene.organism.shortName"                  
## 
## $constraintLogic
## [1] "A and B"
## 
## $name
## [1] "GOterm_Gene"
## 
## $comment
## [1] "Added 26OCT2010: ML"
## 
## $orderBy
## $orderBy[[1]]
## Gene.symbol 
##       "ASC" 
## 
## 
## $where
## $where[[1]]
## $where[[1]]$path
## [1] "Gene.goAnnotation.ontologyTerm.name"
## 
## $where[[1]]$op
## [1] "LIKE"
## 
## $where[[1]]$code
## [1] "A"
## 
## $where[[1]]$editable
## [1] TRUE
## 
## $where[[1]]$switchable
## [1] FALSE
## 
## $where[[1]]$switched
## [1] "LOCKED"
## 
## $where[[1]]$value
## [1] "DNA binding"
## 
## 
## $where[[2]]
## $where[[2]]$path
## [1] "Gene.organism.shortName"
## 
## $where[[2]]$op
## [1] "="
## 
## $where[[2]]$code
## [1] "B"
## 
## $where[[2]]$editable
## [1] TRUE
## 
## $where[[2]]$switchable
## [1] FALSE
## 
## $where[[2]]$switched
## [1] "LOCKED"
## 
## $where[[2]]$value
## [1] "H. sapiens"
  • Modify the view to display a compact view
queryGOGene$select <- queryGOGene$select[2:5]
queryGOGene$select
## [1] "Gene.symbol"                              
## [2] "Gene.name"                                
## [3] "Gene.goAnnotation.ontologyTerm.identifier"
## [4] "Gene.goAnnotation.ontologyTerm.name"
  • Modify the constraints to look for GO term ‘metal ion binding’
queryGOGene$where[[1]]$value = "metal ion binding"
queryGOGene$where
## [[1]]
## [[1]]$path
## [1] "Gene.goAnnotation.ontologyTerm.name"
## 
## [[1]]$op
## [1] "LIKE"
## 
## [[1]]$code
## [1] "A"
## 
## [[1]]$editable
## [1] TRUE
## 
## [[1]]$switchable
## [1] FALSE
## 
## [[1]]$switched
## [1] "LOCKED"
## 
## [[1]]$value
## [1] "metal ion binding"
## 
## 
## [[2]]
## [[2]]$path
## [1] "Gene.organism.shortName"
## 
## [[2]]$op
## [1] "="
## 
## [[2]]$code
## [1] "B"
## 
## [[2]]$editable
## [1] TRUE
## 
## [[2]]$switchable
## [1] FALSE
## 
## [[2]]$switched
## [1] "LOCKED"
## 
## [[2]]$value
## [1] "H. sapiens"
  • Run the query
resGOGene <- runQuery(im, queryGOGene )
head(resGOGene)
##   Gene.symbol                                  Gene.name
## 1     A3GALT2          alpha 1,3-galactosyltransferase 2
## 2        AARS                     alanyl-tRNA synthetase
## 3       AARS2    alanyl-tRNA synthetase 2, mitochondrial
## 4      AARSD1 alanyl-tRNA synthetase domain containing 1
## 5        ABAT           4-aminobutyrate aminotransferase
## 6       ABCG5  ATP binding cassette subfamily G member 5
##   Gene.goAnnotation.ontologyTerm.identifier
## 1                                GO:0046872
## 2                                GO:0046872
## 3                                GO:0046872
## 4                                GO:0046872
## 5                                GO:0046872
## 6                                GO:0046872
##   Gene.goAnnotation.ontologyTerm.name
## 1                   metal ion binding
## 2                   metal ion binding
## 3                   metal ion binding
## 4                   metal ion binding
## 5                   metal ion binding
## 6                   metal ion binding

4.3 Find and plot the genes within 50000 base pairs of gene ABCA6

  • Start with the Gene_Location template, update to search for ABCA6 gene.
queryGeneLoc = getTemplateQuery(im, "Gene_Location")
queryGeneLoc$where[[2]][["value"]] = "ABCA6"
resGeneLoc= runQuery(im, queryGeneLoc)

resGeneLoc
##   Gene.primaryIdentifier Gene.secondaryIdentifier Gene.symbol
## 1                  23460          ENSG00000154262       ABCA6
##                                   Gene.name
## 1 ATP binding cassette subfamily A member 6
##   Gene.chromosome.primaryIdentifier Gene.locations.start Gene.locations.end
## 1                                17             69062044           69141927
##   Gene.locations.strand
## 1                    -1

We’re going to use the output (gene location) as input for the next query.

  • Define a new query
# set constraints
constraints = setConstraints(
  paths = c(
    "Gene.chromosome.primaryIdentifier",
    "Gene.locations.start",
    "Gene.locations.end",
    "Gene.organism.name"
  ),
  operators = c(
    "=",
    ">=",
    "<=",
    "="
  ),
  values = list(
    resGeneLoc[1, "Gene.chromosome.primaryIdentifier"],
    as.character(as.numeric(resGeneLoc[1, "Gene.locations.start"])-50000),
    as.character(as.numeric(resGeneLoc[1, "Gene.locations.end"])+50000),
    "Homo sapiens"
  )
)

# set InterMineR-class query
queryNeighborGene = setQuery(
  select = c("Gene.primaryIdentifier", 
             "Gene.symbol",
             "Gene.chromosome.primaryIdentifier",
             "Gene.locations.start", 
             "Gene.locations.end", 
             "Gene.locations.strand"),
  where = constraints
)

summary(queryNeighborGene)
##                                path op        value code
## 1 Gene.chromosome.primaryIdentifier  =           17    A
## 2              Gene.locations.start >=     69012044    B
## 3                Gene.locations.end <=     69191927    C
## 4                Gene.organism.name  = Homo sapiens    D
  • Run the query
resNeighborGene <- runQuery(im, queryNeighborGene)
resNeighborGene
##   Gene.primaryIdentifier Gene.symbol Gene.chromosome.primaryIdentifier
## 1              100616316    MIR4524A                                17
## 2              100847008    MIR4524B                                17
## 3                  23460       ABCA6                                17
##   Gene.locations.start Gene.locations.end Gene.locations.strand
## 1             69099564           69099632                    -1
## 2             69099542           69099656                     1
## 3             69062044           69141927                    -1
  • Plot the genes
resNeighborGene$Gene.locations.strand[which(resNeighborGene$Gene.locations.strand==1)]="+"

resNeighborGene$Gene.locations.strand[which(resNeighborGene$Gene.locations.strand==-1)]="-"

gene.idx = which(nchar(resNeighborGene$Gene.symbol)==0)

resNeighborGene$Gene.symbol[gene.idx]=resNeighborGene$Gene.primaryIdentifier[gene.idx]
require(Gviz)
annTrack = AnnotationTrack(
  start=resNeighborGene$Gene.locations.start,
  end=resNeighborGene$Gene.locations.end,
  strand=resNeighborGene$Gene.locations.strand,
  chromosome=resNeighborGene$Gene.chromosome.primaryIdentifier[1],
  genome="GRCh38", 
  name="around ABCA6",
  id=resNeighborGene$Gene.symbol)

gtr <- GenomeAxisTrack()
itr <- IdeogramTrack(genome="hg38", chromosome="chr17")

plotTracks(list(gtr, itr, annTrack), shape="box", showFeatureId=TRUE, fontcolor="black")

5 System info

sessionInfo()
## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=C                  LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] Gviz_1.26.3          GenomicRanges_1.34.0 GenomeInfoDb_1.18.1 
##  [4] org.Hs.eg.db_3.7.0   GO.db_3.7.0          GeneAnswers_2.24.0  
##  [7] RColorBrewer_1.1-2   Heatplus_2.28.0      MASS_7.3-51.1       
## [10] annotate_1.60.0      XML_3.98-1.16        AnnotationDbi_1.44.0
## [13] IRanges_2.16.0       S4Vectors_0.20.1     Biobase_2.42.0      
## [16] BiocGenerics_0.28.0  RCurl_1.95-4.11      bitops_1.0-6        
## [19] igraph_1.2.2         RSQLite_2.1.1        InterMineR_1.4.1    
## [22] BiocStyle_2.10.0    
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.3-2            rprojroot_1.3-2            
##  [3] biovizBase_1.30.0           htmlTable_1.12             
##  [5] XVector_0.22.0              base64enc_0.1-3            
##  [7] dichromat_2.0-0             rstudioapi_0.8             
##  [9] bit64_0.9-7                 sqldf_0.4-11               
## [11] xml2_1.2.0                  splines_3.5.1              
## [13] knitr_1.20                  Formula_1.2-3              
## [15] jsonlite_1.5                Rsamtools_1.34.0           
## [17] cluster_2.0.7-1             graph_1.60.0               
## [19] BiocManager_1.30.4          compiler_3.5.1             
## [21] httr_1.3.1                  backports_1.1.2            
## [23] assertthat_0.2.0            Matrix_1.2-15              
## [25] lazyeval_0.2.1              acepack_1.4.1              
## [27] htmltools_0.3.6             prettyunits_1.0.2          
## [29] tools_3.5.1                 bindrcpp_0.2.2             
## [31] gtable_0.2.0                glue_1.3.0                 
## [33] GenomeInfoDbData_1.2.0      dplyr_0.7.8                
## [35] Rcpp_1.0.0                  Biostrings_2.50.1          
## [37] RJSONIO_1.3-1.1             rtracklayer_1.42.1         
## [39] xfun_0.4                    stringr_1.3.1              
## [41] proto_1.0.0                 ensembldb_2.6.3            
## [43] zlibbioc_1.28.0             scales_1.0.0               
## [45] BSgenome_1.50.0             VariantAnnotation_1.28.3   
## [47] ProtGenerics_1.14.0         hms_0.4.2                  
## [49] SummarizedExperiment_1.12.0 RBGL_1.58.1                
## [51] AnnotationFilter_1.6.0      yaml_2.2.0                 
## [53] curl_3.2                    memoise_1.1.0              
## [55] gridExtra_2.3               ggplot2_3.1.0              
## [57] downloader_0.4              biomaRt_2.38.0             
## [59] rpart_4.1-13                latticeExtra_0.6-28        
## [61] stringi_1.2.4               checkmate_1.8.5            
## [63] GenomicFeatures_1.34.1      BiocParallel_1.16.2        
## [65] chron_2.3-53                rlang_0.3.0.1              
## [67] pkgconfig_2.0.2             matrixStats_0.54.0         
## [69] evaluate_0.12               lattice_0.20-38            
## [71] purrr_0.2.5                 bindr_0.1.1                
## [73] GenomicAlignments_1.18.0    htmlwidgets_1.3            
## [75] bit_1.1-14                  tidyselect_0.2.5           
## [77] plyr_1.8.4                  magrittr_1.5               
## [79] bookdown_0.8                R6_2.3.0                   
## [81] Hmisc_4.1-1                 DelayedArray_0.8.0         
## [83] DBI_1.0.0                   gsubfn_0.7                 
## [85] pillar_1.3.0                foreign_0.8-71             
## [87] survival_2.43-3             nnet_7.3-12                
## [89] tibble_1.4.2                crayon_1.3.4               
## [91] rmarkdown_1.10              progress_1.2.0             
## [93] data.table_1.11.8           blob_1.1.1                 
## [95] digest_0.6.18               xtable_1.8-3               
## [97] munsell_0.5.0               tcltk_3.5.1

6 Appendix

6.1 Visual way to derive the column name of a query view or the path name in a query constraint from the database webpage


The InterMine model could be accessed from the mine homepage by clicking the tab “QueryBuilder” and selecting the appropriate data type under “Select a Data Type to Begin a Query”:


Here we select Gene as the data type:


We could select Symbol and Chromosome->Primary Identifier by clicking Show on the right of them. Then click “Export XML” at the bottom right corner of the webpage: