Package 'annmap'

Provide access to the Annmap annotation database

Description

Annmap http://annmap.cruk.manchester.ac.uk Is a genome annotation database and genome browser, based on the Google Maps API. The underlying annotation is derived from ENSEMBL (http://www.ensembl.org). Annmap also provides probe to genome mappings for Affymetrix Exon, Gene and Plus2 arrays.

The annmap package makes the data in annmap available for use within R and BioConductor.

Details

Package:	annmap
Type:	Package
Version:	1.0.0
Date:	2011-09-14
License:	GPL-2

Author(s)

Tim Yates
Maintainer: Tim Yates <[email protected]>

References

Yates T, Okoniewski MJ, Miller CJ. X:Map: annotation and visualization of genome structure for Affymetrix exon array analysis. Nucleic Acids Res. 2008 Jan;36(Database issue):D780-6. Epub 2007 Oct 11.
http://nar.oxfordjournals.org/cgi/content/full/gkm779v1

See Also

GenomicRanges

---

annmap 'all' functions

Description

Get all annotations for a given feature. For example, allGenes will return data for all the genes in the genome.

Usage

allArrays( as.vector=FALSE )
  allChromosomes( as.vector=FALSE )
  allDomains( as.vector=FALSE )
  allEstExons( as.vector=FALSE )
  allEstGenes( as.vector=FALSE )
  allEstTranscripts( as.vector=FALSE )
  allExons( as.vector=FALSE )
  allGenes( as.vector=FALSE )
  allPredictionTranscripts( as.vector=FALSE )
  allProbes( as.vector=FALSE )
  allProbesets( as.vector=FALSE )
  allProteins( as.vector=FALSE )
  allSymbols( as.vector=FALSE )
  allSynonyms( as.vector=FALSE )
  allTranscripts( as.vector=FALSE )

Arguments

as.vector

If TRUE returns a vector of database identifiers. If FALSE returns a GRanges object containing detailed annotation.

Value

Returns a vector or GRanges object, as defined by as.vector.

Author(s)

Tim Yates

See Also

annmapTo
annmapDetails
annmapRange
annmapUtils
annmapFilters
GRanges

Examples

if(interactive()) {
    annmapConnect()	
    allChromosomes()
    allChromosomes(as.vector=TRUE)
  }

---

annmap co-ordinate mapping functions

Description

Functions to go between Genomic, Proteomic and Transcriptual co-ordinate systems.

Usage

transcriptCoordsToGenome( transcript.ids, position=1, as.vector=FALSE, check.bounds=TRUE, truncate=TRUE, cds=FALSE )
  genomeToTranscriptCoords( position, transcript.ids, as.vector=FALSE, check.bounds=TRUE, end=c( 'none', 'both', '5', '3' ) )
  proteinCoordsToGenome( protein.ids, position=1, as.vector=FALSE, check.bounds=TRUE, truncate=TRUE )
  genomeToProteinCoords( position, protein.ids, as.vector=FALSE, check.bounds=TRUE )

Arguments

transcript.ids	A vector of transcript.ids (or a RangedData object of transcripts returned from another annmap function)
position	The position of interest (either a genomic position for both of the genomeToXXXX methods, or a protein or transcript sequence position for the other two methods )
as.vector	Should the returned data be in the form of a vector (if TRUE) or a RangedData object (if FALSE)
check.bounds	If TRUE, any postion outside the range of the protein/transcript will cause a warning to be issued and NA returned.
end	Should the UTR be taken in to account when calculating the location, one of ("none", "both", "3" or "5"). Defaults to none.
truncate	If truncate=TRUE, any lengths beyond the end of the transcript or protein will be set to the last residue
cds	If cds=TRUE then only the coding exons (or sub-regions of exons that are coding) are taken in to account.
protein.ids	A vector of protein.ids (or a RangedData object of proteins returned from another annmap function)

Details

The mapping functions need to deal with mappings that fall outside a transcript or protein (or within an intron). When as.vector=FALSE these are identified as NA in the results. Since RangedData objects cannot represent NA or missing values, when as.vector=FALSE, all locations which cannot be mapped are dropped from the result.

Author(s)

Tim Yates

See Also

annmapTo
annmapDetails
annmapAll
annmapRange
annmapFilters

Examples

if(interactive()) {
    # Get the gene for 'tp53'
    gene        = symbolToGene( 'tp53' )
    # And the transcripts for this gene
    transcripts = geneToTranscript( symbolToGene( 'tp53' ) )
    # And the proteins for this transcript
    proteins    = transcriptToProtein( transcripts )

    # get the transcript coords for the transcripts of this gene, at the start of this gene
    genomeToTranscriptCoords( start( gene ), transcripts, as.vector=TRUE )
    #Returns a vector:
    # ENST00000413465 ENST00000359597 ENST00000504290 ENST00000510385 ENST00000504937
    #            1018              NA              NA              NA              NA     
    # ENST00000269305 ENST00000455263 ENST00000420246 ENST00000445888 ENST00000396473
    #              NA              NA              NA              NA              NA     
    # ENST00000545858 ENST00000419024 ENST00000509690 ENST00000514944 ENST00000505014
    #              NA              NA              NA              NA              NA
    # ENST00000414315 ENST00000508793 ENST00000503591 
    #              NA              NA              NA

    # With as.vector=FALSE
    genomeToTranscriptCoords( start( gene ), transcripts )
    # RangedData with 1 row and 1 value column across 1 space
    #             space       ranges | coord.space
    #       <character>    <IRanges> | <character>
    # 1 ENST00000413465 [1018, 1018] |  transcript

    genomeToProteinCoords( start( gene ), proteins, as.vector=TRUE )
    # ENSP00000410739 ENSP00000352610 ENSP00000269305 ENSP00000398846 ENSP00000391127
    #             340              NA              NA              NA              NA
    # ENSP00000391478 ENSP00000379735 ENSP00000437792 ENSP00000402130 ENSP00000425104
    #              NA              NA              NA              NA              NA
    # ENSP00000423862 ENSP00000394195 ENSP00000424104 ENSP00000426252 
    #              NA              NA              NA              NA 

    # With as.vector=FALSE
    genomeToProteinCoords( start( gene ), proteins )
    # RangedData with 1 row and 2 value columns across 1 space
    #             space     ranges |     frame coord.space
    #       <character>  <IRanges> | <numeric> <character>
    # 1 ENSP00000410739 [340, 340] |         0     protein
  }

---

annmap 'details' functions

Description

Get detailed annotations for the specified features.

Usage

arrayDetails( ids, as.data.frame=FALSE )
  chromosomeDetails( ids, as.data.frame=FALSE )
  domainDetails( ids, as.data.frame=FALSE )
  estExonDetails( ids, as.data.frame=FALSE )
  estGeneDetails( ids, as.data.frame=FALSE )
  estTranscriptDetails( ids, as.data.frame=FALSE )
  exonDetails( ids, as.data.frame=FALSE )
  geneDetails( ids, as.data.frame=FALSE )
  predictionTranscriptDetails( ids, as.data.frame=FALSE )
  probeDetails( ids, as.data.frame=FALSE )
  probesetDetails( ids, as.data.frame=FALSE )
  proteinDetails( ids, as.data.frame=FALSE )
  synonymDetails( ids, as.data.frame=FALSE )
  transcriptDetails( ids, as.data.frame=FALSE )

Arguments

ids	Database identifiers for the features of interest
as.data.frame	If FALSE, data will be converted to a GRanges object if possible, otherwise a data.frame

Value

Results in an GRanges object (or a data.frame if TRUE is passed for the second parameter), one \'row\' per feature, containing detailed annotations.

Author(s)

Tim Yates

See Also

annmapTo
annmapAll
annmapRange
annmapUtils
annmapFilters
GRanges

Examples

if(interactive()) {
    annmapConnect()
    geneDetails(symbolToGene("TP53"))
  }

---

annmap 'env' functions

Description

Functions to access internal parameters

Usage

annmapEnv()
  annmapGetParam( key )
  annmapSetParam( ... )

Arguments

...	A list of key-value parameters you wish to set.
key	The key for the value you want to return.

Details

These functions allow some access to annmap\'s configuration data. They are included to help debug database connection issues, and are not normally needed.

On connection, a default arraytype (Affymetrix Exon arrays, where available) is specfied for the probe mappings. arrayType allows a different type of array to be specfied. This included for future compatibility.

Author(s)

Tim Yates Crispin J. Miller

See Also

annmapTo
annmapDetails
annmapAll
annmapRange
annmapFilters

Examples

if(interactive()) {
    annmapEnv()
    annmapGetParam( "debug" )
    annmapConnect()
    annmapSetParam( debug=TRUE)
    annmapConnect()
    annmapSetParam( debug=FALSE)
    annmapDisconnect()
  }

---

annmap 'filter' functions

Description

Functions to filter exon array probeset names by the genome features they correspond to.

Usage

exonic( probesets, exclude=FALSE )
  hasProbes( probesets, num.probes=4, exclude=FALSE )
  hasProbesAtleast( probesets, num.probes=4, exclude=FALSE )
  hasProbesIn( probesets, num.probes=c( 1, 2, 3, 4 ), exclude=FALSE )
  hasProbesBetween( probesets, min.probes=1, max.probes=4, exclude=FALSE, inclusive=TRUE )
  intergenic( probesets, exclude=FALSE )
  intronic( probesets, exclude=FALSE )
  isExonic( probesets )
  isIntergenic( probesets )
  isIntronic( probesets )
  isUnreliable( probesets )
  unreliable( probesets, exclude=FALSE )

Arguments

probesets	A vector of probesets to filter
num.probes	The required number of probes to have in the probeset
exclude	If FALSE, then return a list containing only those probesets matching the filter. If TRUE then return only those that don\'t match the filter
min.probes	Minimum number of probes within a probeset
max.probes	Maximum number of probes within a probeset
inclusive	Whether to include the extremes of the range in the search or not

Details

Probesets are classified according to whether they map to known genes. The function exonic filters for probesets for which all probes match once (and only once) to the genome, and every probe hits an exon. Note that this means that a probeset that hits more than one exon, will be flagged as exonic. All probes in intronic probesets hit the genome once (and once only), and all probes hit a gene - however one or more probes hit an intron. intergenic probesets hit the genome once (and once only) but one or more probes miss a gene compeletely. unreliable probesets comprise those that have at least one probe that does not align to the genome, or one or more probes that align at multiple loci (multiply targeted).

The functions is.exonic, is.intronic and is.intergenic, return a logical vector classifying the supplied probesets.

The functions has.probes, has.probes.in and has.probes.between can be used to filter a set of probesets according to the numbers of probes they contain.

Author(s)

Tim Yates Crispin J. Miller

See Also

annmapTo
annmapDetails
annmapAll
annmapRange
annmapFilters

Examples

if(interactive()){
    annmapConnect()
    ps <- geneToProbeset(symbolToGene("TP53"))
    exonic(ps)
    intronic(ps)
    intergenic(ps)
    unreliable(ps)
    isExonic(ps)
    isIntronic(ps)
    isIntergenic(ps)
    isUnreliable(ps)
    hasProbes(ps)
    hasProbesIn(ps,1:3)
    hasProbesBetween(ps,2,3)
    hasProbesAtleast(ps,4)
  }

---

annmap 'range' functions

Description

Get the features within the specified genome coordinates.

Usage

domainInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
domainInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
domainInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
domainInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
domainInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
domainInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
domainInRange( x, start, end, strand, ..., as.vector=FALSE )

  estExonInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
estExonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
estExonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
estExonInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
estExonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
estExonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
estExonInRange( x, start, end, strand, ..., as.vector=FALSE )

  estGeneInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
estGeneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
estGeneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
estGeneInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
estGeneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
estGeneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
estGeneInRange( x, start, end, strand, ..., as.vector=FALSE )

  estTranscriptInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
estTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
estTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
estTranscriptInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
estTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
estTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
estTranscriptInRange( x, start, end, strand, ..., as.vector=FALSE )

  exonInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
exonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
exonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
exonInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
exonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
exonInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
exonInRange( x, start, end, strand, ..., as.vector=FALSE )

  geneInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
geneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
geneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
geneInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
geneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
geneInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
geneInRange( x, start, end, strand, ..., as.vector=FALSE )

  predictionTranscriptInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
predictionTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
predictionTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
predictionTranscriptInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
predictionTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
predictionTranscriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
predictionTranscriptInRange( x, start, end, strand, ..., as.vector=FALSE )

  probesetInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
probesetInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
probesetInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
probesetInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
probesetInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
probesetInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
probesetInRange( x, start, end, strand, ..., as.vector=FALSE )

  probeInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
probeInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
probeInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
probeInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
probeInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
probeInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
probeInRange( x, start, end, strand, ..., as.vector=FALSE )

  proteinInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
proteinInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
proteinInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
proteinInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
proteinInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
proteinInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
proteinInRange( x, start, end, strand, ..., as.vector=FALSE )

  transcriptInRange( x, ..., as.vector = FALSE )
  ## S4 method for signature 'GRanges'
transcriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'RangedData'
transcriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'character'
transcriptInRange( x, start, end, strand, ..., as.vector=FALSE )
  ## S4 method for signature 'data.frame'
transcriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'NULL'
transcriptInRange( x, as.vector=FALSE )
  ## S4 method for signature 'factor'
transcriptInRange( x, start, end, strand, ..., as.vector=FALSE )

Arguments

as.vector	If TRUE returns a vector of database identifiers. If FALSE returns a GRanges object containing detailed annotation.
x	The name of the chromosome of interest – in the case of the factor or character variants), or a GRanges object or data.frame containing location information. In the case of a data.frame, columns must be named chr or chromosome_name, followed by start, end and strand. RangedData objects must contain a strand in their meta-data. And strand must be 1 or -1 in all cases arart from GRanges where it obviously has to be + or -. All of the NULL variants simply return NULL, in-keeping with the fluent style of the rest of the package.
start	Start of the region
end	End of the region
strand	1 == top stand, -1 == bottom strand
...	The ellipsis is to allow this multi-method style of programming.

Details

Find all the specified features within a given region of the genome. For all functions except probeInRange, features that fall on the boundaries of the region (i.e. are partially overlapping) are returned too. For probeInRange probes that span the start of the range are NOT returned (but those spanning the end of the range are).

The function annmapRangeApply makes it possible to map any of these functions down the rows of a RangedData or GRanges object. The defaults are set up so that it will handle the output of one of the InRange methods here. This makes it easy to nest functions, for example, to find all genes in a given region of the the genome, and then find the exon array probes that map to those genes (see below).

Value

Returns a GRanges object, one \'row\' per feature, containing detailed annotations, or a vector of identifiers, depending on the value of as.vector.

Author(s)

Tim Yates

See Also

annmapTo
annmapDetails
annmapAll
annmapUtils
annmapFilters
RangedData GRanges

Examples

if(interactive()) {
    annmapConnect()

    r = geneInRange( '17', 7510000, 7550000, 1 )

    # Can take equal length vectors as parameters
    geneInRange( c( '17', 'X' ), c( 7510000, 1000000 ), c( 7550000, 1500000 ), c( -1, -1 ) )

    # Or a data.frame
    df = data.frame( chr=c( '17', 'X' ), start=c( 7510000, 1000000 ), end=c( 7550000, 1500000 ), strand=c( -1, -1 ) )
    geneInRange( df )

    # Or RangedData objects
    transcriptInRange( geneDetails( symbolToGene( c( 'tp53', 'ssh' ) ) ) )
  }

---

Seqnames manipulation functions

Description

These functions allow easier manipulation of the seqnames column of a GRanges object

Usage

generalisedNameToNCBI( name, ... )
  generalisedNameToEnsembl( name, ... )
  seqnameMapping( x, mappingFunction, ... )
  seqnamesToNCBI( x )
  seqnamesToEnsembl( x )

Arguments

name	The name to convert.
x	A GRanges object to convert the seqnames of.
mappingFunction	The function to do the mapping of names.
...	Other arguments you may wish to send to a custom mapping function.

Details

These functions allow simple mapping between seqnames of a GRanges object.

The two standard derivations are seqnamesToNCBI and seqnamesToEnsembl. The rules for mapping are:

      Ensembl      NCBI
      1       <=>  chr1
      ...
      22      <=>  chr22
      X       <=>  chrX
      Y       <=>  chrY
      MT      <=>  chrM
  

You can define your own mapping function and pass it as the mappingFunction parameter to seqnameMapping function to do your own custom mapping.

The function seqnamesToNCBI calls seqnameMapping with generalisedNameToNCBI as the mappingFunction. The function seqnamesToEnsembl uses generalisedNameToEnsembl.

Author(s)

Tim Yates

Examples

if(interactive()) {
    annmapConnect()
    seqnamesToNCBI( symbolToGene( c( 'tp53', 'shh' ) ) )
 }

---

annmap 'to' functions

Description

Map between the different levels of annotation in Annmap. For example, given a vector of gene identifiers, geneToExon will return the exons in those genes.

Usage

arrayToProbeset( ids, as.vector=FALSE )
  domainToGene( ids, as.vector=FALSE )
  domainToProbeset( ids, as.vector=FALSE )
  domainToProtein( ids, as.vector=FALSE )
  domainToTranscript( ids, as.vector=FALSE )
  estExonToEstGene( ids, as.vector=FALSE )
  estExonToEstTranscript( ids, as.vector=FALSE )
  estExonToProbeset( ids, as.vector=FALSE )
  estGeneToEstExon( ids, as.vector=FALSE )
  estGeneToEstTranscript( ids, as.vector=FALSE )
  estGeneToProbeset( ids, as.vector=FALSE )
  estTranscriptToEstExon( ids, as.vector=FALSE )
  estTranscriptToEstGene( ids, as.vector=FALSE )
  estTranscriptToProbeset( ids, as.vector=FALSE )
  exonToGene( ids, as.vector=FALSE )
  exonToProbeset( ids, as.vector=FALSE )
  exonToTranscript( ids, as.vector=FALSE )
  geneToDomain( ids, as.vector=FALSE )
  geneToExon( ids, as.vector=FALSE )
  geneToExonProbeset( ids, as.vector=FALSE, probes.min=4 )
  geneToExonProbesetExpr( x, ids, probes.min=4 )
  geneToProbeset( ids, as.vector=FALSE )
  geneToProtein( ids, as.vector=FALSE )
  geneToSymbol( ids )
  geneToSynonym( ids, as.vector=FALSE )
  geneToTranscript( ids, as.vector=FALSE )
  predictionTranscriptToPredictionExon( ids )
  predictionTranscriptToProbeset( ids, as.vector=FALSE )
  probeToHit( ids, as.data.frame=FALSE )
  probeToProbeset( ids, as.vector=FALSE )
  probesetToCdnatranscript( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToDomain( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToEstExon( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToEstGene( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToEstTranscript( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToExon( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToGene( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToHit( ids, as.data.frame=FALSE, rm.unreliable=TRUE )
  probesetToPredictionTranscript( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToProbe( ids, as.vector=FALSE )
  probesetToProtein( ids, as.vector=FALSE, rm.unreliable=TRUE )
  probesetToTranscript( ids, as.vector=FALSE, rm.unreliable=TRUE )
  proteinToDomain( ids, as.vector=FALSE )
  proteinToGene( ids, as.vector=FALSE )
  proteinToProbeset( ids, as.vector=FALSE )
  proteinToTranscript( ids, as.vector=FALSE )
  symbolToEstGene( ids, as.vector=FALSE )
  symbolToEstTranscript( ids, as.vector=FALSE )
  symbolToGene( ids, as.vector=FALSE )
  symbolToTranscript( ids, as.vector=FALSE )
  synonymToEstGene( ids, as.vector=FALSE )
  synonymToEstTranscript( ids, as.vector=FALSE )
  synonymToGene( ids, as.vector=FALSE )
  synonymToTranscript( ids, as.vector=FALSE )
  transcriptToCdnaprobeset( ids, as.vector=FALSE )
  transcriptToDomain( ids, as.vector=FALSE )
  transcriptToExon( ids, as.vector=FALSE )
  transcriptToExonProbeset( ids, as.vector=FALSE, probes.min=4 )
  transcriptToGene( ids, as.vector=FALSE )
  transcriptToProbeset( ids, as.vector=FALSE )
  transcriptToProtein( ids, as.vector=FALSE )
  transcriptToSynonym( ids, as.vector=FALSE )
  transcriptToTranslatedprobes( ids )

Arguments

as.vector	If TRUE returns a vector of database identifiers. If FALSE returns a link{RangedData} object containing detailed annotation.
as.data.frame	Where a vector is inappropriate for the data type, the option to return the data as a plain data.frame in place of a GRanges object is given.
ids	Database identifiers to map from. Can be either a vector of database identifiers, or a GRanges object.
probes.min	How many probes need to match before the probeset is returned.
rm.unreliable	If TRUE, the input probeset list is filtered, and all unreliable probesets are removed.
x	An ExpressionSet object or a matrix containing expression data. If the latter, then the rownames must specify the exon array probeset names.

Details

In most cases, these functions should be self-explantory. However, by default, the mappings involving probes and probesets do some filtering of the data. This means that probesets which have one or more probes that don't match to the genome, or which match to multiple loci, are removed (see unreliable for more details).

The function transcriptToTranslatedprobes returns a list of GRanges objects (one for each transcript) containing each probe that hits that translated transcripts and the relative start and end locations.

Value

Results in an GRanges object, one row per feature, containing detailed annotations, or a vector, as defined by as.vector.

Author(s)

Tim Yates

See Also

annmapDetails
annmapAll
annmapRange
annmapUtils
annmapFilters
link{GRanges}

Examples

if(interactive()) {
    annmapConnect()
    geneToExon(symbolToGene("TP53"))
  }

---

annmap 'utils' functions

Description

Functions to connect to the database and manage the database connections.

Usage

annmapConnect( name, use.webservice=FALSE, quiet.webservice=FALSE )
  annmapDisconnect()
  annmapAddConnection( dsname, species, version,
                       host='localhost',
                       username=as.character( Sys.info()[ 'user' ] ),
                       password='',
                       port='',
                       overwrite=FALSE,
                       testConnect=TRUE )
  arrayType( name=NULL, pick.default=FALSE, silent=FALSE )
  annmapToggleCaching()
  annmapClearCache()
  annmapRangeApply( x, f, filter=c( chr="space", start="start", end="end", strand="strand" ), coerce=c( as.character, as.numeric, as.numeric, as.numeric ), ... )
  strandAsInteger( granges )
  geneToGeneRegionTrack( genes, genome, coalesce.name=NULL, ... )

Arguments

name	The name of the database to connect to, or the array to select.
use.webservice	If TRUE, we will use the annmap webservices rather than a local MySQL installation.
quiet.webservice	If FALSE, there will be output as the webservice calls are processed. Set TRUE to silence these.
dsname	The name of the datasource to add or modify.
species	The species of interest.
version	The version of the database to connect to.
host	The location of the MySQL installation.
username	The username to connect to MySQL.
password	The password required to connect to MySQL.
port	The port MySQL is running on. (Use NA for default)
overwrite	If another connection with this dsname already exists, should it be overwritten?
testConnect	If TRUE, the connection will be attempted before adding it to the databases.txt file.
pick.default	If TRUE, arrayType will choose the first available arraytype for this species.
silent	If TRUE, it will skip telling you which array you have selected.
x	A RangedData object
f	A function to apply to each \'row\' of the RangedData object
filter	Which \'columns\' of the RangedData object does the function need, and what parameters in the function do they map on to?. For example, by default, the field \'space\' gets mapped to the parameter \'chr\'.
coerce	What is the type of each parameter in \'f\'?
...	additional parameters
granges	A GRanges object
genes	The genes you wish to load into a GeneRegionTrack they must all be on the same chromosome.
genome	A valid Gviz genome, ie: 'hg19'.
coalesce.name	If this is a character vector, all genes will be joined into a single track with this name. Otherwise each gene will have its own track.

Details

annmapConnect is used to establish a connection to an instance of the Annmap database, and annmapDisconnect closes the connection.

arrayType is used to specify the array you wish to use for queries based on Affymetrix probesets.

Many of the functions in annmap cache results locally. The function annmapToggleCaching turns this functionality on and off, and annmapClearCache can be used to clear the cache (this is not normally something a user needs to do).

Note that details of how to set up the default databases, connection details, etc. Can be found in the package vignette.

The function strandAsInteger takes a GRanges object and returns an integer vector of strands in the Ensembl style. "+" becomes 1, "-" becomes -1, and "*" becomes NA.

The function geneToGeneRegionTrack takes a list of genes (character vector, GRanges object, etc), and returns a list of GeneRegionTracks which can be plotted in Gviz. There is an example in the cookbook.

Author(s)

Tim Yates Crispin J. Miller

See Also

annmapTo
annmapDetails
annmapAll
annmapRange
annmapFilters

Examples

if(interactive()) {
    annmapConnect()
    annmapToggleCaching()
    annmapToggleCaching()

    annmapRangeApply(symbolToGene("TP53",as.vector=FALSE),probeInRange)

    #NOTE: since the next function empties out the local cache, don't
    #run it unless you want to do this!
    #annmapClearCache()
 }

---

annmap coding functions

Description

Functions to deal with coding regions and UTRs

Usage

transcriptToUtrRange( ids, end=c( "both", "5", "3" ), as.data.frame=FALSE, on.translation.error=stop )
  transcriptToUtrExon( ids, end=c( 'both', '5', '3' ), as.vector=FALSE, on.translation.error=stop )
  transcriptToCodingRange( ids, end=c( "both", "5", "3" ), as.data.frame=FALSE, on.translation.error=stop )
  transcriptToCodingExon( ids, end=c( 'both', '5', '3' ), as.vector=FALSE, on.translation.error=stop )
  utrProbesets( probesets, transcripts, end=c( "both", "5", "3" ), on.translation.error=stop )
  codingProbesets( probesets, transcripts, end=c( "both", "5", "3" ), on.translation.error=stop )
  nonIntronicTranscriptLength( ids, end=c( 'none', 'both', '5', '3' ), on.translation.error=stop )
  nonIntronicGeneLength( ids )

Arguments

ids	A vector of Transcript Names, or a RangedData object of Transcripts returned from another annmap call.
as.data.frame	If FALSE, data will be converted to a RangedData object if possible, otherwise a data.frame
as.vector	If TRUE returns a vector of database identifiers. If FALSE returns a link{GRanges} object containing detailed annotation.
probesets	An optional vector of Probeset Names, or a RangedData object of Probesets returned from another annmap call.
transcripts	An optional vector of Transcript Names, or a RangedData object of Transcripts returned from another annmap call.
end	Which end ("both", "3" or "5") of the Transcript(s) you are interested in (defaults to both).
on.translation.error	A function to call with a character vector explaining the problem if one is encountered with the translation locations in the database.

Details

The first two functions given here, transcriptToUtrRange and transcriptToCodingRange return the transcripts of interest, with their ranges adjusted depending on the UTR of each.

With transcriptToUtrRange, a RangedData object is returned with the name of the transcript, the end in question, and the genomic location of that UTR. If both is passed as the end parameter, then each transcript will generate up to two rows in the returned object. It may return less than two rows if the end parameter is used, or if there is no UTR for the end specified. (A Transcript with no UTR will return zero results)

The transcriptToCodingRange function returns the same as calling transcriptDetails, but with the start and end locations modified by the range of the UTR. If end is passed, then only the UTR at this end will be taken into consideration and used to modify the returned location.

The transcriptToCodingExon and transcriptToUtrExon functions return the exons for each transcript limited to only those exons (or partions thereof) which are coding or part of the UTR.

utrProbesets and codingProbesets are functions to find or filter probesets which have probes targeting the type of region specified by the function name.

A call to utrProbesets with a list of Probesets will return those probesets that have at least one probe hitting the UTR of any transcript.

A call to utrProbesets with a list of Probesets and a list of Transcripts will return those probesets the have at least one probe hitting the UTR of any of the specified Transcripts.

A call to utrProbesets with only the probesets parameter omitted, will return all probesets which have at least one probe in the UTR region of the specified Transcripts.

You cannot omit both the Probesets and Transcripts parameters simultaneously.

The codingProbesets method does the inverse of the utrProbesets function: it returns probesets having at least one probe in the coding region of a Transcipt (or the specified Transcripts).

Note that the UTR of a Transcript includes the intronic UTR regions, and the coding region of a Transcript includes the intronic coding regions.

This means that utrProbesets and codingProbesets can sometimes return intronic and/or intergenic probesets. These can be removed with a call to the appropriate filter function (see examples).

All unreliable probesets are automatically removed by these functions before mapping.

Calling nonIntronicTranscriptLength will return the length of the exons (coding can be specified via the end parameter) in a given list of transcripts.

And nonIntronicGeneLength will give the length of all exons in a given gene when overlaps are taken into account (so two exactly overlapping exons will count once for the length)

Author(s)

Tim Yates

See Also

annmapTo
annmapDetails
annmapAll
annmapRange
annmapFilters

Examples

if(interactive()) {
    # Only return exonic probesets hitting the UTRs of ENST00000414566
    exonic( utrProbesets( NULL, "ENST00000414566" ) )
  }

---

Plotting a section of a chromosome.

Description

These functions are used when we need to plot one or both strands of a section of chromosome.

Usage

genomicPlot( xrange, gene.area.height=NULL, gene.layout.padding=100, highlights=NULL, draw.opposite.strand=FALSE, exon.depth.plot=genomicExonDepthPlot,
                padding.lines=1, .genes=NULL, .exons=NULL, invert.strands=FALSE, draw.scale=TRUE, ... )
  genomicExonDepthPlot( .exons, start, end, exon.depth.alpha=0.1, exon.depth.col='black', ... )
  genomicProbePlot( probes, start, end, probe.col='green', probe.alpha=0.3, ... )

Arguments

xrange	An IRanges object representing the region of interest (with a strand if reqd)
gene.area.height	If NULL then both strands to max height of either of them, else if NA then both strands limited to their implied height otherwise, if an integer then both strands limited to the specified height
gene.layout.padding	How much space (in bases) needs to be between each gene in a layer. Needed to stop gene names overlapping
highlights	You can pass this a data.frame of values to render as dummy genes in the view. Columns MUST include start, end, strand and name. It may also optionally include the columns col to specify a per-gene background colour, or bor to specify the colour to be used for the gene border and the label text. If these two are not passed, sensible defaults are chosen automatically.
draw.opposite.strand	Do we draw a washed out representation of the other strand. Only applies if strand( xrange ) != '*'
exon.depth.plot	Should we draw the exondepth? set to NULL if not
padding.lines	How much padding above and below the plot (in grid lines)
.genes	Optionally pass in the pre-loaded genes and exons (then we skip loading them in this function)
.exons	The exons that are to be used
invert.strands	Should the forward strand be on the bottom of the plot?
draw.scale	Draw a scale between the two strands?
...	Parameters passed on to functions called by this function
exon.depth.alpha	The transparency for the exon.depth rectangles
exon.depth.col	The color for the exon.depth rectangles
start	The start of the region of interest
end	The end of the region of interest
probe.alpha	How transparent should probes be rendered?
probe.col	The colour to use for probes.
probes	The probes for the region of interest (as a data.frame).

Author(s)

Tim Yates

---

Plotting BAM file data alongside the features of a chromosome

Description

These functions aid plotting a-la xmapbridge but in a format that is more publication friendly

Usage

# Utility Methods
  convertBamToRle( bam.file.name, chr, start, end, chr.name.mapping=function( name ){ name } )
  generateBridgeData( xrange, bamFiles, colours=NULL, names=NULL )
  ngsTraceScale( vector.of.xbams.and.ybams )
  ngsTraceLabel( rle.data )
  ngsTracePlotter( rle.data, start, end, ylim, trace.label.properties=list(), smoothing.function=function( rle, ... ) { IRanges::runmean( rle, k=1001, endrule='constant' ) },
                     trace.clip='inherit', trace.draw.scale=FALSE, trace.bor='transparent', trace.pad=c(0,0), ... )

  # Plotting Methods
  ngsBridgePlot( xrange, data=list(), main=NULL, sub=NULL, highlights=NULL, trace.plotter=ngsTracePlotter, genome.layout.weight=4,
               trace.scale=ngsTraceScale, trace.draw.scale=NULL, trace.match.strand=TRUE, probe.plot=NULL, exon.depth.plot=genomicExonDepthPlot,
               .genes=NULL, .exons=NULL, ... )

Arguments

bam.file.name	The name of the BAM file to read in
chr	The chromosome of interest.
start	The start of the region of interest
end	The end of the region of interest
chr.name.mapping	The function to convert between the Annmap chr name to the chr name in the BAM file. By default, this just uses chr supplied as the parameter, however it can be set to any function you like. One example of this is generalisedNameToNCBI
xrange	The genomic range for the x-axis. Should be a GRanges object.
bamFiles	A vector containing the filenames of your BAM files.
colours	A vector of colours for each file (sensible defaults will be chosen if NULL).
names	A vector of names to show on the traces drawn by ngsTracePlotter
vector.of.xbams.and.ybams	The trace.scale function is passes a vector of the elements of xbams and ybams concatenated together.
rle.data	A list containing fields rle (the Rle data to be plotted), name (the name of the Trace) and col (the colour for the trace).
ylim	A vector of min and max values for this plot (usually retrieved from ngs.trace.scale)
trace.label.properties	Properties to be sent to the grid.text call for plotting the label on the trace. To hide the label, this should be NA.
smoothing.function	A function that generates a smoothed RLE object.
trace.clip	Is the trace clipped to it's bounding box? One of 'inherit', 'on' or 'off'. See viewport.
trace.draw.scale	If TRUE, x and y scales are drawn with main=TRUE (see grid.xaxis and grid.yaxis), if FALSE, then neither axis is drawn. You can control individual axis drawing by passing a vector such as trace.draw.scale='x' to just draw the x axis. You can also pass a list such as trace.draw.scale=list(x=TRUE,y=FALSE), and this will draw both the x and y axis, but pass main=TRUE to the grid.xaxis call, and main=FALSE to grid.yaxis
trace.bor	The colour for a box that is drawn round this trace.plot.
trace.pad	A 2 element vector consisting of the number of 'lines' of padding to allow at the top and bottom of the plot respecively
data	A list containing an element per trace. Each element of this list is, in turn, passed to the trace.plotter and trace.scale functions where the plotting happens – see details. )
main	The main title for the plot.
sub	A sub-title for the plot.
highlights	Highlight regions for the plot. See genomicPlot.
trace.plotter	The function to call to draw the traces (see ngsTracePlotter)
genome.layout.weight	The weight for the genomic plot in the layout of this grid
trace.scale	Either a function to calculate the global max for the NGS traces (see ngsTraceScale) OR a 2 element vector cntaining the min and max extent of the trace.
trace.match.strand	If TRUE, we will only draw the rle data from the strand defined in xrange. If false, we will draw all of the rle data. Can also be set to '+' or '-' to only draw the trace from the given strand (ignoring the strand of xrange).
probe.plot	The function to plot the probes (see genomicProbePlot), NULL if not drawn.
exon.depth.plot	The function to draw the exon depth (see genomicExonDepthPlot), NULL if not drawn.
.genes	Optionally pass a list of genes to limit the plot to.
.exons	An optional list of exons to limit the plot to.
...	Parameters passed on to functions called by this function

Details

convertBamToRle will take a BAM file name, and a region of interest and return a list() containing two elements, '+' and '-'. Each element will be an Rle object, one for each strand.

The data parameter to ngsBridgePlot is a list of elements as defined in the rle.data parameter, one element per NGS trace, ie:

    library(grid)
    library(annmap)

    # Connect to datasource with annmapConnect()

    # Ensure we have a clean plot
    grid.newpage()

    bamFiles = c( 'data1.bam', 'data2.bam', 'data3.bam' )
    colours  = rainbow( 3, v=0.5, s=0.5 )
    data = lapply( seq_along( bamFiles ), function( idx ) {
      list( rle=convertBamToRle( bamFiles[ idx ], 'I', 40000, 100000 ),
            col=colours[ idx ],
            name=paste( 'Trace', bamFiles[ idx ] ) )
    } )
    ngsBridgePlot( RangedData( space='I', ranges=IRanges( 40000, 100000 ) ), data=data, main='Example Plot' )
  

Author(s)

Tim Yates

See Also

genomicProbePlot, genomicPlot, genomicExonDepthPlot

---

Splice indexing

Description

Calculates the splicing index for the probesets in one or more genes, as defined in the Affymetrix white paper "Alternative Transcript Analysis Methods for Exon Arrays".

Usage

spliceGroupIndex( x, group.column, members )
  spliceIndex( x, ids, group, gps, group.index.fn=spliceGroupIndex, median.gene=FALSE, median.probeset=FALSE, unlogged=TRUE )

Arguments

x	eSet containing expression data
group.column	a column name for the group data
members	a set of arrays
ids	Character vector of Ensembl gene names
group	If defined, the column name in the ExpressionSet's pData object in which to look for gps
gps	The two sets of arrays to compare
group.index.fn	a method which, when passed an ExpressionSet (from the Biobase package), a column name for the group data and a set of arrays, will return the indices of interest
median.gene	Use the median instead of the mean when calculating averages across genes
median.probeset	Use the median instead of the mean when calculating averages across probesets in each replicate group
unlogged	Unlog the expression data before calculating the splicing index (and then re-log afterwards)

Details

The splicing index gives a measure of the difference in expression level for each probeset in a gene between two sets of arrays, relative to the gene-level average in each set. This is calculated only for those probesets that are defined as exonic (See exonic).

The two sets of arrays can be specified in two ways: First, by using numeric indices defining the appropriate columns in the expression data. This is done by supplying these as a list to gps (e.g. gps=list(1:3,4:6) will calculate the splicing index between arrays 1,2,3 and 4,5,6. Alternatively, the annotation in the phenoData object from x can be used (e.g. group="treatment",gps=c("a","b") will compare between the arrays labelled 'a', and 'b' in the 'treatment' column of pData(x)).

The implementation also calculates a p.value and t.statistic for each probeset; these are returned alongside the splicing index.

By default, the splicing index is calculated using the mean across genes and samples. Specifying median.gene=TRUE or median.probeset=TRUE will use the median instead (for the gene or probeset level averages, respectively). It is calculated using the unlogged data, unless unlogged=FALSE. This only affects the internal calculations; values in x are always assumed to be logged, and the splicing index is always returned on the log2 scale.

Author(s)

Tim Yates Crispin J. Miller

See Also

exonic

Examples

if(interactive()) {
    # Loads the Expression Set into x.rma
    load( '../unitTests/HuEx-1_0.tp53.expr.RData' )
    spliceIndex( x.rma, symbolToGene( 'tp53' ), gps=list(1:3,4:6) )
  }

---