Package 'RNAmodR'

Aggregate data per positions

Description

The aggregate function is defined for each SequenceData object and can be used directly on a SequenceData object or indirectly via a Modifier object.

For the letter the call is redirect to the SequenceData object, the result summarized as defined for the individual Modifier class and stored in the aggregate slot of the Modifier object. The data is then used for subsequent tasks, such as search for modifications and visualization of the results.

The summarization is implemented in the aggregateData for each type of Modifier class. The stored data from the aggregate slot can be retrieved using the getAggregateData function.

Whether the aggrgeated data is already present in the aggregate slot can be checked using the hasAggregateData function.

For SequenceDataSet, SequenceDataList and ModfierSet classes wrapper of the aggregate function exist as well.

Usage

aggregate(x, ...)

aggregateData(x, ...)

getAggregateData(x)

hasAggregateData(x)

## S4 method for signature 'SequenceData'
aggregate(x, condition = c())

## S4 method for signature 'SequenceData'
aggregateData(x, condition)

## S4 method for signature 'SequenceDataSet'
aggregate(x, condition = "Treated")

## S4 method for signature 'SequenceDataList'
aggregate(x, condition = "Treated")

## S4 method for signature 'Modifier'
aggregate(x, force = FALSE)

## S4 method for signature 'Modifier'
aggregateData(x)

## S4 method for signature 'Modifier'
getAggregateData(x)

## S4 method for signature 'Modifier'
hasAggregateData(x)

## S4 method for signature 'ModifierSet'
aggregate(x, force = FALSE)

Arguments

x	a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModfierSet object.
...	additional arguments
condition	character value, which selects, for which condition the data should be aggregated. One of the following values: Both, Control, Treated
force	whether to recreate the aggregated data, if it is already stored inside the Modifier object.

Value

• aggregate: for SequenceData object the aggregated data is returned as a SplitDataFrameList with an element per transcript, whereas for a Modifier the modified input object is returned, containing the aggregated data, which can be accessed using getAggregateData.

• getAggregateData: only for Modifier: a SplitDataFrameList with an element per transcript is returned. If the aggregated data is not stored in the object, it is generated on the fly, but does not persist.

• hasAggregateData: TRUE or FALSE. Does the Modifier object already contain aggregated data?

If 'x' is a

• SequenceData a SplitDataFrameList with elments per transcript.

• SequenceDataSet or SequenceDataList a SimpleList with SplitDataFrameList as elements.

• Modifier or ModifierSet an updated Modifier object. The data can be accessed by using the aggregateData function.

Examples

data(e5sd,package="RNAmodR")
data(msi,package="RNAmodR")
# modify() triggers the search for modifications in the data contained in
# the Modifier or ModifierSet object
sdfl <- aggregate(e5sd)
mi <- aggregate(msi[[1]])

---

Comparison of Samples

Description

To compare data of different samples, a ModifierSet can be used. To select the data alongside the transcripts and their positions a GRanges or a GRangesList needs to be provided. In case of a GRanges object, the parent column must match the transcript names as defined by the out put of ranges(x), whereas in case of a GRangesList the element names must match the transcript names.

Usage

compare(x, name, pos = 1L, ...)

compareByCoord(x, coord, ...)

plotCompare(x, name, pos = 1L, normalize, ...)

plotCompareByCoord(x, coord, normalize, ...)

## S4 method for signature 'ModifierSet'
compare(x, name, pos = 1L, normalize, ...)

## S4 method for signature 'ModifierSet,GRanges'
compareByCoord(x, coord, normalize, ...)

## S4 method for signature 'ModifierSet,GRangesList'
compareByCoord(x, coord, normalize, ...)

## S4 method for signature 'ModifierSet'
plotCompare(x, name, pos = 1L, normalize, ...)

## S4 method for signature 'ModifierSet,GRanges'
plotCompareByCoord(x, coord, normalize, ...)

## S4 method for signature 'ModifierSet,GRangesList'
plotCompareByCoord(x, coord, normalize, ...)

Arguments

x	a Modifier or ModifierSet object.
name	Only for compare: the transcript name
pos	Only for compare: pos for comparison
...	optional parameters: alias a data.frame with two columns, tx_id and name, to convert transcipt ids to another identifier name Limit results to one specific gene or transcript sequenceData TRUE or FALSE? Should the aggregate of sequenceData be used for the comparison instead of the aggregate data if each Modifier element? (default: sequenceData = FALSE) compareType a valid score type to use for the comparison. If sequenceData = FALSE this defaults to mainScore(x), whereas if sequenceData = TRUE all columns will be used by setting allTypes = TRUE. allTypes TRUE or FALSE? Should all available score be compared? (default: allTypes = sequenceData) ... passed on to subsetByCoord
coord	coordinates of position to subset to. Either a GRanges or a GRangesList object. For both types the 'Parent' column is expected to match the transcript name. The GRangesList object is unlisted and only non duplicated entries are retained.
normalize	either a single logical or character value. If it is a character, it must match one of the names in the ModifierSet.

Value

compareByCoord returns a DataFrame and plotCompareByCoord returns a ggplot object, which can be modified further. The DataFrame contains columns per sample as well as the columns names, positions and mod incorporated from the coord input. If coord contains a column Activity this is included in the results as well.

Examples

data(msi,package="RNAmodR")
# constructing a GRanges obejct to mark positive positions
mod <- modifications(msi)
coord <- unique(unlist(mod))
coord$score <- NULL
coord$sd <- NULL
# return a DataFrame
compareByCoord(msi,coord)
# plot the comparison as a heatmap
plotCompareByCoord(msi,coord)

---

CoverageSequenceData

Description

CoverageSequenceData implements SequenceData to contain and aggregate the coverage of reads per position along the transcripts.

CoverageSequenceData contains one column per data file named using the following naming convention coverage.condition.replicate.

aggregate calculates the mean and sd for samples in the control and treated condition separatly.

Usage

CoverageSequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

CoverageSequenceData(bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 
## 'CoverageSequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'CoverageSequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'CoverageSequenceData'
getDataTrack(x, name, ...)

Arguments

df, ranges, sequence, replicate	inputs for creating a SequenceDataFrame. See SequenceDataFrame.
condition	For aggregate: condition for which the data should be aggregated.
bamfiles, annotation, seqinfo, grl, sequences, param, args, ...	See SequenceData
x	a CoverageSequenceData
name	For getDataTrack: a valid transcript name. Must be a name of ranges(x)

Value

a CoverageSequenceData object

Examples

# Construction of a CoverageSequenceData objectobject
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
sequences <- RNAmodR.Data.example.man.fasta()
files <- c(treated = RNAmodR.Data.example.wt.1())
csd <- CoverageSequenceData(files, annotation = annotation,
                            sequences = sequences)

---

End5SequenceData/End3SequenceData/EndSequenceData

Description

The End5SequenceData/End3SequenceData/EndSequenceData classes aggregate the counts of read ends at each position along a transcript. End5SequenceData/End3SequenceData classes aggregate either the 5'-end or 3'-end, the EndSequenceData aggregates both.

All three classes contain one column per data file named using the following naming convention (end5/end3/end).condition.replicate.

aggregate calculates the mean and sd for samples in the control and treated condition separatly.

Usage

End5SequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

End3SequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

EndSequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

End5SequenceData(bamfiles, annotation, sequences, seqinfo, ...)

End3SequenceData(bamfiles, annotation, sequences, seqinfo, ...)

EndSequenceData(bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 
## 'End5SequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 
## 'End3SequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 
## 'EndSequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'End5SequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'End3SequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'EndSequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'EndSequenceData'
getDataTrack(x, name, ...)

## S4 method for signature 'End5SequenceData'
getDataTrack(x, name, ...)

## S4 method for signature 'End3SequenceData'
getDataTrack(x, name, ...)

Arguments

df, ranges, sequence, replicate	inputs for creating a SequenceDataFrame. See SequenceDataFrame.
condition	For aggregate: condition for which the data should be aggregated.
bamfiles, annotation, seqinfo, grl, sequences, param, args, ...	See SequenceData and SequenceData-functions
x	a End5SequenceData, End3SequenceData or EndSequenceData object
name	For getDataTrack: a valid transcript name. Must be a name of ranges(x).

Value

a End5SequenceData, a End3SequenceData or a EndSequenceData object

Examples

# Construction of a End5SequenceData object
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
sequences <- RNAmodR.Data.example.man.fasta()
files <- c(treated = RNAmodR.Data.example.wt.1())
e5sd <- End5SequenceData(files, annotation = annotation,
                        sequences = sequences)

---

The Modifier class

Description

The Modifier class is a virtual class, which provides the central functionality to search for post-transcriptional RNA modification patterns in high throughput sequencing data.

Each subclass has to implement the following functions:

• Slot nucleotide: Either "RNA" or "DNA". For conveniance the subclasses RNAModifier and DNAModifier are already available and can be inherited from.

• Function aggregateData: used for specific data aggregation

• Function findMod: used for specific search for modifications

Optionally the function settings<- can be implemented to store additional arguments, which the base class does not recognize.

Modifier objects are constructed centrally by calling Modifier() with a className matching the specific class to be constructed. This will trigger the immediate analysis, if find.mod is not set to FALSE.

Usage

Modifier(className, x, annotation, sequences, seqinfo, ...)

## S4 method for signature 'SequenceData'
Modifier(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'SequenceDataSet'
Modifier(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'SequenceDataList'
Modifier(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'character'
Modifier(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'list'
Modifier(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'BamFileList'
Modifier(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

Arguments

className	The name of the class which should be constructed.
x	the input which can be of the following types SequenceData: a single SequenceData or a list containing only SequenceData objects. The input will just be used to file the data slot of the Modifier and must match the requirements of specific Modifier class. BamFileList: a named BamFileList character: a character vector, which must be coercible to a named BamFileList referencing existing bam files. Valid names are control and treated to define conditions and replicates
annotation	annotation data, which must match the information contained in the BAM files. This parameter is only required if x is not a SequenceData object or a list of SequenceData objects.
sequences	sequences matching the target sequences the reads were mapped onto. This must match the information contained in the BAM files. TThis parameter is only required if x is not a SequenceData object or a list of SequenceData objects.
seqinfo	An optional Seqinfo argument or character vector, which can be coerced to one, to subset the sequences to be analyzed on a per chromosome basis.
...	Additional otpional parameters: find.mod: TRUE or FALSE: should the search for for modifications be triggered upon construction? If not the search can be started by calling the modify() function. additional parameters depending on the specific Modifier class All additional options must be named and will be passed to the settings function and onto the SequenceData objects, if x is not a SequenceData object or a list of SequenceData objects.

Value

a Modifier object of type className

Slots

nucleotide

a character value, which needs to contain "RNA" or "DNA"

mod

a character value, which needs to contain one or more elements from the alphabet of a ModRNAString or ModDNAString class.

score

the main score identifier used for visualizations

dataType

the class name(s) of the SequenceData class used

bamfiles

the input bam files as BamFileList

condition

conditions along the BamFileList: Either control or treated

replicate

replicate number along the BamFileList for each of the condition types.

data

The sequence data object: Either a SequenceData, SequenceDataSet or a SequenceDataList object, if more than one dataType is used.

aggregate

the aggregated data as a SplitDataFrameList

modifications

the found modifications as a GRanges object

settings

arguments used for the analysis as a list

aggregateValidForCurrentArguments

TRUE or FALSE whether the aggregate data was constructed with the current arguments

modificationsValidForCurrentArguments

TRUE or FALSE whether the modifications were found with the current arguments

Creation

Modifier objects can be created in two ways, either by providing a list of bamfiles or SequenceData/SequenceDataSet/SequenceDataList objects, which match the structure in dataType().

dataType() can be a character vector or a list of character vectors and depending on this the input files have to follow this structure:

• a single character: a SequenceData is constructed/expected.

• a character vector: a SequenceDataSet is constructed/expected.

• a list of character vectors: a SequenceDataList is constructed/expected.

The cases for a SequenceData or SequenceDataSet are straight forward, since the input remains the same. The last case is special, since it is a hypothetical option, in which bam files from two or more different methods have to be combined to reliably detect a single modification (The elements of a SequenceDataList don't have to be created from the bamfiles, whereas from a SequenceDataSet they have to be).

For this example a list of character vectors is expected. Each element must be named according to the names of dataType() and contain a character vector for creating a SequenceData object.

All additional options must be named and will be passed to the settings function and onto the SequenceData objects, if x is not a SequenceData object or a list of SequenceData objects.

---

Modifier/ModifierSet functions

Description

For the Modifier and ModifierSet classes a number of functions are implemented to access the data stored by the object.

The validAggregate and validModification functions check if settings have been modified, after the data was loaded. This potentially invalidates them. To update the data, run the aggregate or the modify function.

Usage

bamfiles(x)

mainScore(x)

modifierType(x)

modType(x)

dataType(x)

sequenceData(x)

sequences(x, ...)

validAggregate(x)

validModification(x)

## S4 method for signature 'Modifier'
show(object)

## S4 method for signature 'Modifier'
bamfiles(x)

## S4 method for signature 'Modifier'
conditions(object)

## S4 method for signature 'Modifier'
mainScore(x)

## S4 method for signature 'Modifier'
modifierType(x)

## S4 method for signature 'Modifier'
modType(x)

## S4 method for signature 'Modifier'
dataType(x)

## S4 method for signature 'Modifier'
names(x)

## S4 method for signature 'Modifier'
ranges(x)

## S4 method for signature 'Modifier'
replicates(x)

## S4 method for signature 'Modifier'
seqinfo(x)

## S4 method for signature 'Modifier'
seqtype(x)

## S4 method for signature 'Modifier'
sequenceData(x)

## S4 method for signature 'Modifier'
sequences(x, modified = FALSE)

## S4 method for signature 'Modifier'
validAggregate(x)

## S4 method for signature 'Modifier'
validModification(x)

## S4 method for signature 'ModifierSet'
show(object)

## S4 method for signature 'ModifierSet'
bamfiles(x)

## S4 method for signature 'ModifierSet'
conditions(object)

## S4 method for signature 'ModifierSet'
mainScore(x)

## S4 method for signature 'ModifierSet'
modifications(x, perTranscript = FALSE)

## S4 method for signature 'ModifierSet'
modifierType(x)

## S4 method for signature 'ModifierSet'
modType(x)

## S4 method for signature 'ModifierSet'
dataType(x)

## S4 method for signature 'ModifierSet'
ranges(x)

## S4 method for signature 'ModifierSet'
replicates(x)

## S4 method for signature 'ModifierSet'
seqinfo(x)

## S4 method for signature 'ModifierSet'
seqtype(x)

## S4 method for signature 'ModifierSet'
sequences(x, modified = FALSE)

Arguments

x, object	a Modifier or ModifierSet class
...	Additional arguments.
modified	For sequences: TRUE or FALSE: Should the sequences be returned as a ModRNAString/ModDNAString with the found modifications added on top of the RNAString/ DNAString? See combineIntoModstrings.
perTranscript	TRUE or FALSE: Should the positions shown per transcript? (default: perTranscript = FALSE)

Value

• modifierType: a character vector with the appropriate class Name of a Modifier.

• modType: a character vector with the modifications detected by the Modifier class.

• seqtype: a single character value defining if either "RNA" or "DNA" modifications are detected by the Modifier class.

• mainScore: a character vector.

• sequenceData: a SequenceData object.

• modifications: a GRanges or GRangesList object describing the found modifications.

• seqinfo: a Seqinfo object.

• sequences: a RNAStingSet object.

• ranges: a GRangesList object with each element per transcript.

• bamfiles: a BamFileList object.

• validAggregate: TRUE or FALSE. Checks if current settings are the same for which the data was aggregate

• validModification: TRUE or FALSE. Checks if current settings are the same for which modification were found

See Also

settings

Examples

data(msi,package="RNAmodR")
mi <- msi[[1]]
modifierType(mi) # The class name of the Modifier object
modifierType(msi)
seqtype(mi)
modType(mi)
mainScore(mi)
sequenceData(mi)
modifications(mi)
# general accessors
seqinfo(mi)
sequences(mi)
ranges(mi)
bamfiles(mi)

---

The ModifierSet class

Description

The ModifierSet class allows multiple Modifier objects to be created from the same annotation and sequence data varying only the bam input files.

In addition the comparison of samples is also done via calling functions on the ModifierSet objects.

The ModifierSet is a virtual class, which derives from the SimpleList class with the slot elementType = "Modifier". The ModifierSet class has to be implemented for each specific analysis.#'

Usage

ModifierSet(className, x, annotation, sequences, seqinfo, ...)

## S4 method for signature 'list'
ModifierSet(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'character'
ModifierSet(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'BamFileList'
ModifierSet(
  className,
  x,
  annotation = NULL,
  sequences = NULL,
  seqinfo = NULL,
  ...
)

## S4 method for signature 'Modifier'
ModifierSet(className, x, annotation, sequences, seqinfo, ...)

Arguments

className	The name of the class which should be constructed.
x	the input which can be of the following types Modifier: a single Modifier or a list containg only Modifier objects. The input will just be used as elements of the ModifierSet BamFileList: a named BamFileList or a list of named BamFileList list: a list of one or more types of elements: BamFileList, a named list or named character vector. All elements must be or be coercible to a named BamFileList referencing existing bam files. Valid names are control and treated
annotation	annotation data, which must match the information contained in the BAM files. This is parameter is only required, if x is not a Modifier object.
sequences	sequences matching the target sequences the reads were mapped onto. This must match the information contained in the BAM files. This is parameter is only required, if x is not a Modifier object.
seqinfo	An optional Seqinfo argument or character vector, which can be coerced to one, to subset the sequences to be analyzed on a per chromosome basis.
...	Additional otpional parameters: internalBP TRUE or FALSE: should parallelization used internally during creation of each Modifier or should the creation of the Modifier objects be parallalized? (default: internalBP = FALSE). Setting internalBP only makes sense, if the getData function for SequenceData class, the aggregateData or the findMod function contains parallelized code. All other arguments will be passed onto the Modifier objects.

Value

a ModifierSet object of type className

Creation

The input files have to be provided as a list of elements. Each element in itself must be valid for the creation of Modifier object (Have a look at the man page for more details) and must be named.

---

Searching for modifications in SequenceData

Description

The modify function executes the search for modifications for a Modifier class. Usually this is done automatically during construction of a Modifier object.

When the modify functions is called, the aggregated data is checked for validity for the current settings and the search for modifications is performed using the findMod. The results are stored in the modification slot of the Modifier object, which is returned by modify. The results can be accessed via the modifications() function.

findMod returns the found modifications as a GRanges object and has to be implemented for each individual Modifier class.

Usage

modifications(x, ...)

modify(x, ...)

findMod(x)

## S4 method for signature 'Modifier'
modifications(x, perTranscript = FALSE)

## S4 method for signature 'Modifier'
modify(x, force = FALSE)

## S4 method for signature 'Modifier'
findMod(x)

## S4 method for signature 'ModifierSet'
modify(x, force = FALSE)

Arguments

x	a Modifier object.
...	additional arguments
perTranscript	For modifications> TRUE or FALSE: Should the coordinates be returned as local per transcript coordinates?
force	force to run aggregate again, if data is already stored in x.

Value

• modify: the updated Modifier object.

• modifications: the modifications found as a GRanges object.

Examples

data(msi,package="RNAmodR")
# modify() triggers the search for modifications in the data contained in
# the Modifier or ModifierSet object
mi <- modify(msi[[1]])

---

ModInosine

Description

Inosine can be detected in RNA-Seq data by the conversion of A positions to G. This conversion is detected by ModInosine and used to search for Inosine positions. dataType is "PileupSequenceData".

Only samples labeled with the condition treated are used for this analysis, since the A to G conversion is common feature among the reverse transcriptases usually emploied. Let us know, if that is not the case, and the class needs to be modified.

Further information on Functions of ModInosine.

Usage

ModInosine(x, annotation, sequences, seqinfo, ...)

ModSetInosine(x, annotation = NA, sequences = NA, seqinfo = NA, ...)

Arguments

x	the input which can be of the different types depending on whether a ModRiboMethSeq or a ModSetRiboMethSeq object is to be constructed. For more information have a look at the documentation of the Modifier and ModifierSet classes.
annotation	annotation data, which must match the information contained in the BAM files. This is parameter is only required, if x is not a Modifier object.
sequences	sequences matching the target sequences the reads were mapped onto. This must match the information contained in the BAM files. This is parameter is only required, if x is not a Modifier object.
seqinfo	An optional Seqinfo argument or character vector, which can be coerced to one, to subset the sequences to be analyzed on a per chromosome basis.
...	Optional arguments overwriting default values, which are minCoverage: The minimal coverage at the position as integer value (default: minCoverage = 10L). minReplicate: minimum number of replicates needed for the analysis (default: minReplicate = 1L). minScore: minimum score to identify Inosine positions de novo (default: minScore = 0.4).

Details

ModInosine score: the scores for reported Inosine positions are between 0 and 1. They are calculated as the relative amount of called G bases ((G / N)) and only saved for genomic A positions.

Value

a ModInosine or ModSetInosine object

Author(s)

Felix G.M. Ernst [aut]

Examples

# construction of ModInosine object
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
sequences <- RNAmodR.Data.example.man.fasta()
files <- c(treated = RNAmodR.Data.example.wt.1())
mi <- ModInosine(files,annotation = annotation ,sequences = sequences)
# construction of ModSetInosine object
## Not run: 
files <- list("SampleSet1" = c(treated = RNAmodR.Data.example.wt.1(),
                               treated = RNAmodR.Data.example.wt.2(),
                               treated = RNAmodR.Data.example.wt.3()),
              "SampleSet2" = c(treated = RNAmodR.Data.example.bud23.1(),
                               treated = RNAmodR.Data.example.bud23.2()),
              "SampleSet3" = c(treated = RNAmodR.Data.example.trm8.1(),
                               treated = RNAmodR.Data.example.trm8.2()))
msi <- ModSetInosine(files, annotation = annotation, sequences = sequences)

## End(Not run)

---

Functions for ModInosine

Description

All of the functions of Modifier and the ModifierSet classes are inherited by the ModInosine and ModSetInosine classes.

Check below for the specifically implemented functions.

Usage

## S4 replacement method for signature 'ModInosine'
settings(x) <- value

## S4 method for signature 'ModInosine'
aggregateData(x)

## S4 method for signature 'ModInosine'
findMod(x)

## S4 method for signature 'ModInosine'
getDataTrack(x, name, type, ...)

## S4 method for signature 'ModInosine,GRanges'
plotDataByCoord(x, coord, type = "score", window.size = 15L, ...)

## S4 method for signature 'ModInosine'
plotData(x, name, from = 1L, to = 30L, type = "score", ...)

## S4 method for signature 'ModSetInosine,GRanges'
plotDataByCoord(x, coord, type = "score", window.size = 15L, ...)

## S4 method for signature 'ModSetInosine'
plotData(x, name, from = 1L, to = 30L, type = "score", ...)

Arguments

x	a Modifier or a ModifierSet object. For more details see also the man pages for the functions mentioned below.
value	See settings
coord, name, from, to, type, window.size, ...	See plotData

Details

ModInosine specific arguments for plotData:

• colour.bases - a named character vector of length = 4 for the colours of the individual bases. The names are expected to be c("G","A","U","C")

Value

• settings See settings.

• aggregate See aggregate.

• modify See modify.

• getDataTrack a list of DataTrack objects. See plotDataByCoord.

• plotData See plotDataByCoord.

• plotDataByCoord See plotDataByCoord.

Examples

data(msi,package="RNAmodR")
mi <- msi[[1]]
settings(mi)
## Not run: 
aggregate(mi)
modify(mi)

## End(Not run)
getDataTrack(mi, "1", mainScore(mi))

---

ModInosine internal functions

Description

These functions are not intended for general use, but are used for additional package development.

Arguments

x, data, seqdata, sequence, args

internally used arguments

---

NormEnd5SequenceData/NormEnd3SequenceData

Description

The NormEnd5SequenceData/NormEnd3SequenceData aggregate the counts of read ends (Either 5' or 3') at each position along a transcript. In addition, the number of counts are then normalized to the length of the transcript and to the overlapping reads.

Both classes contain three columns per data file named using the following naming convention (normend5/normend3).condition.replicate. The three columns are distinguished by additional identifiers ends, norm.tx and norm.ol.

aggregate calculates the mean and sd for samples in the control and treated condition separatly. Similar to the stored results for each of the two conditions six columns are returned (three for mean and sd each) ending in ends, tx and ol.

Usage

NormEnd5SequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

NormEnd3SequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

NormEnd5SequenceData(bamfiles, annotation, sequences, seqinfo, ...)

NormEnd3SequenceData(bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 
## 'NormEnd5SequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 
## 'NormEnd3SequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'NormEnd5SequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'NormEnd3SequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'NormEnd5SequenceData'
getDataTrack(x, name, ...)

## S4 method for signature 'NormEnd3SequenceData'
getDataTrack(x, name, ...)

Arguments

df, ranges, sequence, replicate	inputs for creating a SequenceDataFrame. See SequenceDataFrame.
condition	For aggregate: condition for which the data should be aggregated.
bamfiles, annotation, seqinfo, grl, sequences, param, args, ...	See SequenceData and SequenceData-functions
x	a CoverageSequenceData
name	For getDataTrack: a valid transcript name. Must be a name of ranges(x)

Value

a NormEnd5SequenceData or NormEnd3SequenceData object

Examples

# Construction of a NormEnd5SequenceData object
## Not run: 
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
sequences <- RNAmodR.Data.example.man.fasta()
files <- c(treated = RNAmodR.Data.example.wt.1())
ne5sd <- NormEnd5SequenceData(files, annotation = annotation,
                              sequences = sequences)

## End(Not run)

---

PileupSequenceData

Description

The PileupSequenceData aggregates the pileup of called bases per position.

PileupSequenceData contains five columns per data file named using the following naming convention pileup.condition.replicate. The five columns are distinguished by additional identifiers -, G, A, T and C.

aggregate calculates the mean and sd for each nucleotide in the control and treated condition separatly. The results are then normalized to a row sum of 1.

Usage

PileupSequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

PileupSequenceData(bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 
## 'PileupSequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'PileupSequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'PileupSequenceData'
getDataTrack(x, name, ...)

pileupToCoverage(x)

## S4 method for signature 'PileupSequenceData'
pileupToCoverage(x)

Arguments

df, ranges, sequence, replicate	inputs for creating a SequenceDataFrame. See SequenceDataFrame.
condition	For aggregate: condition for which the data should be aggregated.
bamfiles, annotation, seqinfo, grl, sequences, param, args, ...	See SequenceData and SequenceData-functions
x	a PileupSequenceData
name	For getDataTrack: a valid transcript name. Must be a name of ranges(x)

Value

a PileupSequenceData object

Examples

# Construction of a PileupSequenceData object
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
sequences <- RNAmodR.Data.example.man.fasta()
files <- c(treated = RNAmodR.Data.example.wt.1())
psd <- PileupSequenceData(files, annotation = annotation,
                          sequences = sequences)

---

Visualizing data data from a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModifierSet object.

Description

With the plotData and plotDataByCoord functions data from a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModifierSet object can be visualized.

Internally the functionality of the Gviz package is used. For each SequenceData and Modifier class the getDataTrack is implemented returning a DataTrack object from the Gviz package.

Positions to be visualized are selected by defining a genomic coordinate, for which x has to contain data.

Usage

plotData(x, name, from = 1L, to = 30L, type, ...)

plotDataByCoord(x, coord, type, window.size = 15L, ...)

getDataTrack(x, name, ...)

## S4 method for signature 'Modifier,GRanges'
plotDataByCoord(x, coord, type = NA, window.size = 15L, ...)

## S4 method for signature 'Modifier'
plotData(
  x,
  name,
  from,
  to,
  type = NA,
  showSequenceData = FALSE,
  showSequence = TRUE,
  showAnnotation = FALSE,
  ...
)

## S4 method for signature 'Modifier'
getDataTrack(x, name = name, ...)

## S4 method for signature 'ModifierSet,GRanges'
plotDataByCoord(x, coord, type = NA, window.size = 15L, ...)

## S4 method for signature 'ModifierSet'
plotData(
  x,
  name,
  from,
  to,
  type = NA,
  showSequenceData = FALSE,
  showSequence = TRUE,
  showAnnotation = FALSE,
  ...
)

## S4 method for signature 'SequenceData,GRanges'
plotDataByCoord(x, coord, type = NA, window.size = 15L, ...)

## S4 method for signature 'SequenceData'
plotData(
  x,
  name,
  from,
  to,
  perTranscript = FALSE,
  showSequence = TRUE,
  showAnnotation = FALSE,
  ...
)

## S4 method for signature 'SequenceData'
getDataTrack(x, name = name, ...)

## S4 method for signature 'SequenceDataList'
getDataTrack(x, name = name, ...)

## S4 method for signature 'SequenceDataList,GRanges'
plotDataByCoord(x, coord, type = NA, window.size = 15L, ...)

## S4 method for signature 'SequenceDataList'
plotData(
  x,
  name,
  from,
  to,
  perTranscript = FALSE,
  showSequence = TRUE,
  showAnnotation = FALSE,
  ...
)

## S4 method for signature 'SequenceDataSet'
getDataTrack(x, name = name, ...)

## S4 method for signature 'SequenceDataSet,GRanges'
plotDataByCoord(x, coord, type = NA, window.size = 15L, ...)

## S4 method for signature 'SequenceDataSet'
plotData(
  x,
  name,
  from,
  to,
  perTranscript = FALSE,
  showSequence = TRUE,
  showAnnotation = FALSE,
  ...
)

Arguments

x	a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModifierSet object.
name	Only for plotData: the transcript name
from	Only for plotData: start position
to	Only for plotData: end position
type	the data type of data show as data tracks.
...	optional parameters: modified.seq TRUE or FALSE. Should the sequence shown with modified nucleotide positions? (default: modified.seq = FALSE) additional.mod other modifications, which should be shown in the annotation and sequence track. The must be a GRanges compatible with combineIntoModstrings. annotation.track.pars Parameters passed onto the AnnotationTrack. sequence.track.pars Parameters passed onto the SequenceTrack.
coord	coordinates of a positions to subset to as a GRanges object. The 'Parent' column is expected to match the transcript name.
window.size	integer value for the number of positions on the left and right site of the selected positions included in the plotting (default: window.size = 15L)
showSequenceData	TRUE or FALSE: should the sequence data be shown? (default: seqdata = FALSE)
showSequence	TRUE or FALSE: should a sequence track be shown? (default: seqdata = TRUE)
showAnnotation	TRUE or FALSE: should a annotation track be shown? (default: seqdata = FALSE)
perTranscript	TRUE or FALSE: Should the positions shown per transcript? (default: perTranscript = FALSE)

Value

a plot send to the active graphic device

Examples

data(msi,package="RNAmodR")
plotData(msi[[1]], "2", from = 10L, to = 45L)
## Not run: 
plotData(msi, "2", from = 10L, to = 45L)

## End(Not run)

---

ROCR functions for Modifier and ModifierSet objects

Description

plotROC streamlines labeling, prediction, performance and plotting functions to test the peformance of a Modifier object and the data analyzed via the functionallity from the ROCR package.

The data from x will be labeled as positive using the coord arguments. The other arguments will be passed on to the specific ROCR functions.

By default the prediction.args include three values:

• measure = "tpr"

• x.measure = "fpr"

• score = mainScore(x)

The remaining arguments are not predefined.

Usage

plotROC(x, coord, ...)

## S4 method for signature 'Modifier'
plotROC(
  x,
  coord,
  score = NULL,
  prediction.args = list(),
  performance.args = list(),
  plot.args = list()
)

## S4 method for signature 'ModifierSet'
plotROC(
  x,
  coord,
  score = NULL,
  prediction.args = list(),
  performance.args = list(),
  plot.args = list()
)

Arguments

x	a Modifier or a ModifierSet object
coord	coordinates of position to label as positive. Either a GRanges or a GRangesList object. For both types the Parent column is expected to match the gene or transcript name.
...	additional arguments
score	the score identifier to subset to, if multiple scores are available.
prediction.args	arguments which will be used for calling prediction form the ROCR package
performance.args	arguments which will be used for calling performance form the ROCR package
plot.args	arguments which will be used for calling plot on the performance object of the ROCR package. If multiple scores are plotted (for example if the score argument is not explicitly set) add = FALSE will be set.

Value

a plot send to the active graphic device

References

Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer (2005): "ROCR: visualizing classifier performance in R." Bioinformatics 21(20):3940-3941 DOI: 10.1093/bioinformatics/bti623

Examples

data(msi,package="RNAmodR")
# constructing a GRanges obejct to mark positive positions
mod <- modifications(msi)
coord <- unique(unlist(mod))
coord$score <- NULL
coord$sd <- NULL
# plotting a TPR vs. FPR plot per ModInosine object
plotROC(msi[[1]],coord)
# plotting a TPR vs. FPR plot per ModSetInosine object
plotROC(msi,coord)

---

ProtectedEndSequenceData

Description

ProtectedEndSequenceData implements SequenceData to contain and aggregate the start and ends of reads per position along a transcript. ProtectedEndSequenceData offsets the start position by -1 to align the information on the 5'-3'-phosphate bonds to one position. The ProtectedEndSequenceData class is implemented specifically as required for the RiboMethSeq method.

The objects of type ProtectedEndSequenceData contain three columns per data file named using the following naming convention protectedend.condition.replicate.

aggregate calculates the mean and sd for samples in the control and treated condition separatly.

Usage

ProtectedEndSequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

ProtectedEndSequenceData(bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 
## 'ProtectedEndSequenceData,
##   BamFileList,
##   GRangesList,
##   XStringSet,
##   ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'ProtectedEndSequenceData'
aggregateData(x, condition = c("Both", "Treated", "Control"))

## S4 method for signature 'ProtectedEndSequenceData'
getDataTrack(x, name, ...)

Arguments

df, ranges, sequence, replicate	inputs for creating a SequenceDataFrame. See SequenceDataFrame.
condition	For aggregate: condition for which the data should be aggregated.
bamfiles, annotation, seqinfo, grl, sequences, param, args, ...	See SequenceData and SequenceData-functions
x	a ProtectedEndSequenceData
name	For getDataTrack: a valid transcript name. Must be a name of ranges(x)

Value

a ProtectedEndSequenceData object

Examples

# Construction of a ProtectedEndSequenceData object
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
sequences <- RNAmodR.Data.example.man.fasta()
files <- c(treated = RNAmodR.Data.example.wt.1())
pesd <- ProtectedEndSequenceData(files, annotation = annotation,
                                 sequences = sequences)

---

RNAmodR

Description

Post-transcriptional modifications can be found abundantly in rRNA and tRNA and can be detected classically via several strategies. However, difficulties arise if the identity and the position of the modified nucleotides is to be determined at the same time. Classically, a primer extension, a form of reverse transcription (RT), would allow certain modifications to be accessed by blocks during the RT changes or changes in the cDNA sequences. Other modification would need to be selectively treated by chemical reactions to influence the outcome of the reverse transcription.

With the increased availability of high throughput sequencing, these classical methods were adapted to high throughput methods allowing more RNA molecules to be accessed at the same time. With these advances post-transcriptional modifications were also detected on mRNA. Among these high throughput techniques are for example Pseudo-Seq (Carlile et al. 2014), RiboMethSeq (Birkedal et al. 2015) and AlkAnilineSeq (Marchand et al. 2018) each able to detect a specific type of modification from footprints in RNA-Seq data prepared with the selected methods.

Since similar pattern can be observed from some of these techniques, overlaps of the bioinformatical pipeline already are and will become more frequent with new emerging sequencing techniques.

RNAmodR implements classes and a workflow to detect post-transcriptional RNA modifications in high throughput sequencing data. It is easily adaptable to new methods and can help during the phase of initial method development as well as more complex screenings.

Briefly, from the SequenceData, specific subclasses are derived for accessing specific aspects of aligned reads, e.g. 5’-end positions or pileup data. With this a Modifier class can be used to detect specific patterns for individual types of modifications. The SequenceData classes can be shared by different Modifier classes allowing easy adaptation to new methods.

Author(s)

Felix G M Ernst [aut], Denis L.J. Lafontaine [ctb]

References

- Carlile TM, Rojas-Duran MF, Zinshteyn B, Shin H, Bartoli KM, Gilbert WV (2014): "Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells." Nature 515 (7525), P. 143–146. DOI: 10.1038/nature13802.

- Birkedal U, Christensen-Dalsgaard M, Krogh N, Sabarinathan R, Gorodkin J, Nielsen H (2015): "Profiling of ribose methylations in RNA by high-throughput sequencing." Angewandte Chemie (International ed. in English) 54 (2), P. 451–455. DOI: 10.1002/anie.201408362.

- Marchand V, Ayadi L, __Ernst FGM__, Hertler J, Bourguignon-Igel V, Galvanin A, Kotter A, Helm M, __Lafontaine DLJ__, Motorin Y (2018): "AlkAniline-Seq: Profiling of m7 G and m3 C RNA Modifications at Single Nucleotide Resolution." Angewandte Chemie (International ed. in English) 57 (51), P. 16785–16790. DOI: 10.1002/anie.201810946.

See Also

The RNAmodR.RiboMethSeq and RNAmodR.AlkAnilineSeq package.

---

Example data in the RNAmodR package

Description

The following datasets are contained in the RNAmodR package. They are used in the man page examples.

Usage

data(msi)

data(sds)

data(sdl)

data(psd)

data(e5sd)

data(e3sd)

data(esd)

data(csd)

data(ne3sd)

data(ne5sd)

data(pesd)

Format

• msi a ModSetInosine instance

• sds a SequenceDataSet instance

• sdl a SequenceDataList instance

• psd a PileupSequenceData instance

• e5sd a End5SequenceData instance

• e3sd a End3SequenceData instance

• esd a EndSequenceData instance

• csd a CoverageSequenceData instance

• ne3sd a NormEnd3SequenceData instance

• ne5sd a NormEnd5SequenceData instance

• pesd a ProtectedEndSequenceData instance

An object of class SequenceDataSet of length 2.

An object of class SequenceDataList of length 3.

An object of class PileupSequenceData of dimension 100 x 101 x 15 x 15.

An object of class End5SequenceData of dimension 100 x 101 x 3 x 3.

An object of class End3SequenceData of dimension 100 x 101 x 3 x 3.

An object of class EndSequenceData of dimension 100 x 101 x 3 x 3.

An object of class CoverageSequenceData of dimension 100 x 101 x 3 x 3.

An object of class NormEnd3SequenceData of dimension 100 x 101 x 9 x 9.

An object of class NormEnd5SequenceData of dimension 100 x 101 x 9 x 9.

An object of class ProtectedEndSequenceData of dimension 100 x 101 x 3 x 3.

---

RNAmodR developments functions

Description

These functions are not intended for general use, but are used for additional package development.

getData is used to load data into a SequenceData object and must be implented for all SequenceData classes. The results must match the requirements outlined in the value section.

In addition the following functions should be implemented for complete functionality:

aggregateData for each SequenceData and Modifier class. See also aggregateData

findMod for each Modifier class. See also findMod.

plotData/plotDataByCoord for each Modifier and ModifierSet class. See also plotData.

The following helper function can be called from within findMod to construct a coordinate for each modification found:

constructModRanges constructs a GRanges object describing the location, type and associated scores of a modification. constructModRanges is typically called from the modify function, which must be implemented for all Modifier classes.

Usage

constructModRanges(range, data, modType, scoreFun, source, type)

getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'GRanges,DataFrame'
constructModRanges(range, data, modType, scoreFun, source, type)

Arguments

range	for constructModRanges: a GRanges object
data	for constructModRanges: a DataFrame object
modType	for constructModRanges: a valid shortName for the modification found. Must be present in shortName(ModRNAString()).
scoreFun	for constructModRanges: a custom function for extracting scores from data. The result must be a list.
source	for constructModRanges: a single character vector for populating the source column of the result.
type	for constructModRanges: a single character vector for populating the source column of the result.
x	for getData:a SequenceData object.
bamfiles	for getData:a BamFileList object.
grl	for getData:a GRangesList object.
sequences	for getData:a XStringSet object.
param	for getData:a ScanBamParam object.
args	for getData: a list with optional arguments.

Value

• getData: returns a list with elements per BamFile in bamfiles. Elements can be IntegerList, NumericList or a CompressedSplitDataFrameList. The data in the elements must be order by increasing positions numbers. However, names and rownames will be discarded.

• constructModRanges: returns a GRanges object with genomic coordinates of modified nucleotides in the associated transcripts.

Examples

# new SequenceData class
setClass(Class = "ExampleSequenceData",
         contains = "SequenceData",
         prototype = list(minQuality = 5L))
ExampleSequenceData <- function(bamfiles, annotation, sequences, seqinfo, ...){
  RNAmodR:::SequenceData("Example", bamfiles = bamfiles, 
                         annotation = annotation, sequences = sequences,
                         seqinfo = seqinfo, ...)
}
setMethod("getData",
          signature = c(x = "ExampleSequenceData",
                        bamfiles = "BamFileList",
                        grl = "GRangesList",
                        sequences = "XStringSet",
                        param = "ScanBamParam"),
          definition = function(x, bamfiles, grl, sequences, param, args){
            ###
          }
)
setMethod("aggregateData",
          signature = c(x = "ExampleSequenceData"),
          function(x, condition = c("Both","Treated","Control")){
            ###
          }
)
setMethod(
  f = "getDataTrack",
  signature = c(x = "ExampleSequenceData"),
  definition = function(x, name, ...) {
    ###
  }
)

# new Modifier class
setClass("ModExample",
         contains = "Modifier",
         prototype = list(mod = "X",
                          score = "score",
                          dataType = "ExampleSequenceData"))
ModExample <- function(x, annotation, sequences, seqinfo, ...){
  RNAmodR:::Modifier("ModExample", x = x, annotation = annotation,
                     sequences = sequences, seqinfo = seqinfo, ...)
}

setMethod(f = "aggregateData",
          signature = c(x = "ModExample"),
          definition =
            function(x, force = FALSE){
              # Some data with element per transcript
            }
)

setMethod("findMod",
          signature = c(x = "ModExample"),
          function(x){
            # an element per modification found.
          }
)
setMethod(
  f = "getDataTrack",
  signature = signature(x = "ModExample"),
  definition = function(x, name, type, ...) {
  }
)
setMethod(
  f = "plotDataByCoord",
  signature = signature(x = "ModExample", coord = "GRanges"),
  definition = function(x, coord, type = "score", window.size = 15L, ...) {
  }
)
setMethod(
  f = "plotData",
  signature = signature(x = "ModExample"),
  definition = function(x, name, from, to, type = "score", ...) {
  }
)
 
# new ModifierSet class
setClass("ModSetExample",
         contains = "ModifierSet",
         prototype = list(elementType = "ModExample"))
ModSetExample <- function(x, annotation, sequences, seqinfo, ...){
  RNAmodR:::ModifierSet("ModExample", x = x, annotation = annotation,
                        sequences = sequences, seqinfo = seqinfo, ...)
}

setMethod(
  f = "plotDataByCoord",
  signature = signature(x = "ModSetExample", coord = "GRanges"),
  definition = function(x, coord, type = "score", window.size = 15L, ...) {
  }
)
setMethod(
  f = "plotData",
  signature = signature(x = "ModSetExample"),
  definition = function(x, name, from, to, type = "score", ...) {
  }
)

---

The SequenceData class

Description

The SequenceData class is implemented to contain data on each position along transcripts and holds the corresponding annotation data and nucleotide sequence of these transcripts. To access this data several functions are available. The SequenceData class is a virtual class, from which specific classes can be extended. Currently the following classes are implemented:

• CoverageSequenceData

• End5SequenceData, End3SequenceData, EndSequenceData

• NormEnd5SequenceData, NormEnd5SequenceData

• PileupSequenceData

• ProtectedEndSequenceData

The annotation and sequence data can be accessed through the functions ranges and sequences, respectively. Beaware, that the data is always provided according to genomic positions with increasing rownames, but the sequence is given as the actual sequence of the transcript. Therefore, it is necessary to treat the minus strand accordingly.

The SequenceData class is derived from the CompressedSplitDataFrameList class with additional slots for annotation and sequence data. Some functionality is not inherited and might not available to full extend, e.g.relist.

SequenceDataFrame

The SequenceDataFrame class is a virtual class and contains data for positions along a single transcript. In addition to being used for returning elements from a SequenceData object, the SequenceDataFrame class is used to store the unlisted data within a SequenceData object. Therefore, a matching SequenceData and SequenceDataFrame class must be implemented.

The SequenceDataFrame class is derived from the DataFrame class.

Subsetting of a SequenceDataFrame returns a SequenceDataFrame or DataFrame, if it is subset by a column or row, respectively. The drop argument is ignored for column subsetting.

Usage

## S4 method for signature 'SequenceData'
cbind(..., deparse.level = 1)

## S4 method for signature 'SequenceData'
rbind(..., deparse.level = 1)

SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'character,character'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'character,BSgenome'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'TxDb,character'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'TxDb,BSgenome'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'GRangesList,character'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'GRangesList,BSgenome'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'GFF3File,BSgenome'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'GFF3File,character'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'character,FaFile'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'GFF3File,FaFile'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'TxDb,FaFile'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

## S4 method for signature 'GRangesList,FaFile'
SequenceData(dataType, bamfiles, annotation, sequences, seqinfo, ...)

Arguments

...	Optional arguments overwriting default values. Not all SequenceData classes use all arguments. The arguments are: minLength single integer value setting a threshold for minimum read length. Shorther reads are discarded (default: minLength = NA). maxLength single integer value setting a threshold for maximum read length. Longer reads are discarded (default: maxLength = NA). minQuality single integer value setting a threshold for maximum read quality. Reads with a lower quality are discarded (default: minQuality = 5L, but this is class dependent). max_depth maximum depth for pileup loading (default: max_depth = 10000L).
deparse.level	See base::cbind for a description of this argument.
dataType	The prefix for construction the class name of the SequenceData subclass to be constructed.
bamfiles	the input which can be of the following types BamFileList: a named BamFileList character: a character vector, which must be coercible to a named BamFileList referencing existing bam files. Valid names are control and treated to define conditions and replicates
annotation	annotation data, which must match the information contained in the BAM files.
sequences	sequences matching the target sequences the reads were mapped onto. This must match the information contained in the BAM files.
seqinfo	optional Seqinfo to subset the transcripts analyzed on a chromosome basis.

Value

A SequenceData object

Slots

sequencesType

a character value for the class name of sequences. Either RNAStringSet, ModRNAStringSet, DNAStringSet or ModDNAStringSet.

minQuality

a integer value describing a threshold of the minimum quality of reads to be used.

---

SequenceData/SequenceDataSet/SequenceDataList/SequenceDataFrame functions

Description

The SequenceData, SequenceDataSet, SequenceDataList and SequenceDataFrame classes share functionality. Have a look at the elements listed directly below.

Usage

replicates(x)

## S4 method for signature 'SequenceDataFrame'
show(object)

## S4 method for signature 'SequenceDataFrame'
conditions(object)

## S4 method for signature 'SequenceDataFrame'
bamfiles(x)

## S4 method for signature 'SequenceDataFrame'
dataType(x)

## S4 method for signature 'SequenceDataFrame'
ranges(x)

## S4 method for signature 'SequenceDataFrame'
replicates(x)

## S4 method for signature 'SequenceDataFrame'
seqinfo(x)

## S4 method for signature 'SequenceDataFrame'
seqinfo(x)

## S4 method for signature 'SequenceDataFrame'
seqtype(x)

## S4 replacement method for signature 'SequenceDataFrame'
seqtype(x) <- value

## S4 method for signature 'SequenceDataFrame'
sequences(x)

## S4 method for signature 'SequenceData'
show(object)

## S4 method for signature 
## 'SequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam'
getData(x, bamfiles, grl, sequences, param, args)

## S4 method for signature 'SequenceData'
bamfiles(x)

## S4 method for signature 'SequenceData'
conditions(object)

## S4 method for signature 'SequenceData'
ranges(x)

## S4 method for signature 'SequenceData'
replicates(x)

## S4 method for signature 'SequenceData'
seqinfo(x)

## S4 method for signature 'SequenceData'
sequences(x)

## S4 method for signature 'SequenceData'
seqtype(x)

## S4 replacement method for signature 'SequenceData'
seqtype(x) <- value

## S4 method for signature 'SequenceData'
dataType(x)

## S4 method for signature 'SequenceDataSet'
show(object)

## S4 method for signature 'SequenceDataSet'
bamfiles(x)

## S4 method for signature 'SequenceDataSet'
conditions(object)

## S4 method for signature 'SequenceDataSet'
names(x)

## S4 method for signature 'SequenceDataSet'
ranges(x)

## S4 method for signature 'SequenceDataSet'
replicates(x)

## S4 method for signature 'SequenceDataSet'
seqinfo(x)

## S4 method for signature 'SequenceDataSet'
seqtype(x)

## S4 replacement method for signature 'SequenceDataSet'
seqtype(x) <- value

## S4 method for signature 'SequenceDataSet'
sequences(x)

## S4 method for signature 'SequenceDataList'
show(object)

## S4 method for signature 'SequenceDataList'
bamfiles(x)

## S4 method for signature 'SequenceDataList'
conditions(object)

## S4 method for signature 'SequenceDataList'
names(x)

## S4 method for signature 'SequenceDataList'
ranges(x)

## S4 method for signature 'SequenceDataList'
replicates(x)

## S4 method for signature 'SequenceDataList'
seqinfo(x)

## S4 method for signature 'SequenceDataList'
seqtype(x)

## S4 replacement method for signature 'SequenceDataList'
seqtype(x) <- value

## S4 method for signature 'SequenceDataList'
sequences(x)

Arguments

x, object	a SequenceData, SequenceDataSet, SequenceDataList or a SequenceDataFrame object.
value	a new seqtype, either "RNA" or "DNA"
bamfiles	a BamFileList.
grl	a GRangesList from exonsBy(..., by = "tx")
sequences	a XStringSet of type RNAStringSet, ModRNAStringSet, DNAStringSet or ModDNAStringSet
param	a ScanBamParam object
args	a list of addition arguments

Value

• seqinfo: a Seqinfo object ().

• sequences: a RNAStingSet object or a RNAString object for a SequenceDataFrame.

• ranges: a GRangesList object with each element per transcript or a GRanges object for a SequenceDataFrame.

• bamfiles: a BamFileList object or a SimpleList of BamFileList objects for a SequenceDataList.

Examples

data(e5sd,package="RNAmodR")
# general accessors
seqinfo(e5sd)
sequences(e5sd)
ranges(e5sd)
bamfiles(e5sd)

---

The SequenceDataFrame class

Description

The SequenceDataFrame class is a virtual class and contains data for positions along a single transcript. In addition to being used for returning elements from a SequenceData object, the SequenceDataFrame class is used to store the unlisted data within a SequenceData object. Therefore, a matching SequenceData and SequenceDataFrame class must be implemented.

The SequenceDataFrame class is derived from the DataFrame class. To follow the functionallity in the S4Vectors package, SequenceDataFrame implements the concept, whereas SequenceDFrame is the implementation for in-memory data representation from which some specific *SequenceDataFrame class derive from, e.g. CoverageSequenceData.

Subsetting of a SequenceDataFrame returns a SequenceDataFrame or DataFrame, if it is subset by a column or row, respectively. The drop argument is ignored for column subsetting.

Usage

## S4 method for signature 'SequenceDataFrame'
cbind(..., deparse.level = 1)

## S4 method for signature 'SequenceDataFrame,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

Arguments

x, i, j, ..., drop, deparse.level

arguments used for subsetting or base::cbind.

Value

A SequenceDataFrame object or if subset to row a DataFrame

Slots

ranges

a GRanges object each element describing a transcript including its element. The GRanges is constructed from the unlisted results of the exonsBy(x, by="tx") function. If during construction a GRangesList is provided instead of a character value pointing to a gff3 file or a TxDb object, it must have a comparable structure.

sequence

a XString of type sequencesType from the parent SequenceData object.

condition

conditions along the BamFileList: Either control or treated

replicate

replicate number along the BamFileList for each of the condition types.

bamfiles

the input bam files as BamFileList

seqinfo

a Seqinfo describing the avialable/used chromosomes.

See Also

for an example see ProtectedEndSequenceData and for more information see SequenceData

Examples

data(e5sd,package="RNAmodR")
# A SequenceDataFrame can is usually constructed by subsetting from 
# a SequenceData object
sdf <- e5sd[[1]]
# Its also used to store the unlisted data in a SequenceData object
sdf <- unlist(e5sd) # should probably only used internally
e5sd <- relist(sdf,e5sd)

---

The SequenceDataList class

Description

The SequenceDataList class is used to hold SequenceData or SequenceDataSet objects as its elements. It is derived from the List class.

The SequenceDataList is used to hold data from different sets of aligned reads. This allows multiple methods to be aggregated into one modification detection strategy. Annotation and sequence data must be the same for all elements, however the bam files can be different.

Usage

SequenceDataList(...)

Arguments

...	The elements to be included in the SequenceDataList.

Value

a SequenceDataList

Examples

data(psd,package="RNAmodR")
data(e5sd,package="RNAmodR")
sdl <- SequenceDataList(SequenceDataSet(psd,e5sd),e5sd)

---

The SequenceDataSet class

Description

The SequenceDataSet class is used to hold SequenceData objects as its elements. It is derived from the List class.

The SequenceDataSet is used to hold different data types from the of same aligned reads. The same dataset can be used to generate multiple sets of data types. Bam files, annotation and sequence data must be the same for all elements.

Usage

SequenceDataSet(...)

Arguments

...	The elements to be included in the SequenceDataSet.

Value

a SequenceDataSet

Examples

data(psd,package="RNAmodR")
data(e5sd,package="RNAmodR")
sdl <- SequenceDataSet(psd,e5sd)

---

ModDNASequenceTrack

Description

A Gviz compatible SequenceTrack for showing modified DNA sequences.

Usage

ModDNASequenceTrack(sequence, chromosome, genome, name = "SequenceTrack", ...)

## S4 method for signature 'SequenceModDNAStringSetTrack'
seqnames(x)

## S4 method for signature 'SequenceModDNAStringSetTrack'
seqlevels(x)

Arguments

sequence	A character vector or ModDNAString object of length one. The sequence to display. See SequenceTrack.
chromosome, genome, name, ...	See SequenceTrack.
x	A SequenceModDNAStringSetTrack object.

Value

a SequenceModDNAStringSetTrack object

Slots

sequence

A ModDNAStringSet object

Examples

seq <- ModDNAStringSet(c(chr1 = paste0(alphabet(ModDNAString()),
                                       collapse = "")))
st <- ModDNASequenceTrack(seq)
Gviz::plotTracks(st, chromosome = "chr1",from = 1L, to = 20L)

---

ModRNASequenceTrack

Description

A Gviz compatible SequenceTrack for showing modified RNA sequences.

Usage

ModRNASequenceTrack(sequence, chromosome, genome, name = "SequenceTrack", ...)

## S4 method for signature 'SequenceModRNAStringSetTrack'
seqnames(x)

## S4 method for signature 'SequenceModRNAStringSetTrack'
seqlevels(x)

Arguments

sequence	A character vector or ModRNAString object of length one. The sequence to display. See SequenceTrack.
chromosome, genome, name, ...	See SequenceTrack.
x	A SequenceModRNAStringSetTrack object.

Value

a SequenceModRNAStringSetTrack object

Slots

sequence

A ModRNAStringSet object

Examples

seq <- ModRNAStringSet(c(chr1 = paste0(alphabet(ModRNAString()),
                                       collapse = "")))
st <- ModRNASequenceTrack(seq)
Gviz::plotTracks(st, chromosome = "chr1",from = 1L, to = 20L)

---

Settings for Modifier objects

Description

Depending on data prepation, quality and desired stringency of a modification strategy, settings for cut off parameters or other variables may need to be adjusted. This should be rarely the case, but a function for changing these settings, is implemented as the... settings function.

For changing values the input can be either a list or something coercible to a list. Upon changing a setting, the validity of the value in terms of type(!) and dimensions will be checked.

If settings have been modified after the data was loaded, the data is potentially invalid. To update the data, run the aggregate or the modify function.

Usage

settings(x, name = NULL)

settings(x, name) <- value

## S4 method for signature 'Modifier'
settings(x, name = NULL)

## S4 replacement method for signature 'Modifier'
settings(x) <- value

## S4 method for signature 'ModifierSet'
settings(x, name = NULL)

## S4 replacement method for signature 'ModifierSet'
settings(x) <- value

Arguments

x	a Modifier or ModifierSet class
name	name of the setting to be returned or set
value	value of the setting to be set

Value

If name is omitted, settings returns a list of all settings. If name is set, settings returns a single settings or NULL, if a value for name is not available.

Examples

data(msi,package="RNAmodR")
mi <- msi[[1]]
# returns a list of all settings
settings(mi)
# accesses a specific setting
settings(mi,"minCoverage")
# modification of setting
settings(mi) <- list(minCoverage = 11L)

---

Retrieving information about used reads in RNAmodR

Description

stats returns information about reads used in the RNAmodR analysis. Three modes are available depending on which type of object is provided. If a SequenceData object is provided, a BamFile or BamFileList must be provided as well. If a Modifier object is used, the bam files returned from the bamfiles function are used. This is also the case, if a ModifierSet object is used.

Usage

stats(x, file, ...)

## S4 method for signature 'SequenceData,BamFile'
stats(x, file, ...)

## S4 method for signature 'SequenceData,BamFileList'
stats(x, file, ...)

## S4 method for signature 'Modifier,missing'
stats(x)

## S4 method for signature 'ModifierSet,missing'
stats(x)

Arguments

x	a SequenceData, Modifier or ModifierSet object
file	a BamFile or BamFileList, if x is a SequenceData object.
...	optional parameters used as stated here (except minQuality), if x is a SequenceData object.

Value

a DataFrame, DataFrameList or SimpleList with the results in aggregated form

Examples

library(RNAmodR.Data)
library(rtracklayer)
sequences <- RNAmodR.Data.example.AAS.fasta()
annotation <- GFF3File(RNAmodR.Data.example.AAS.gff3())
files <- list("SampleSet1" = c(treated = RNAmodR.Data.example.wt.1(),
                               treated = RNAmodR.Data.example.wt.2(),
                               treated = RNAmodR.Data.example.wt.3()),
              "SampleSet2" = c(treated = RNAmodR.Data.example.bud23.1(),
                               treated = RNAmodR.Data.example.bud23.2()),
              "SampleSet3" = c(treated = RNAmodR.Data.example.trm8.1(),
                               treated = RNAmodR.Data.example.trm8.2()))
msi <- ModSetInosine(files, annotation = annotation, sequences = sequences)
# smallest chunk of information
stats(sequenceData(msi[[1L]]),bamfiles(msi[[1L]])[[1L]])
# partial information
stats(sequenceData(msi[[1L]]),bamfiles(msi[[1L]]))
# the whole stats
stats(msi)

---

Subsetting data from a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModifierSet object.

Description

With the subsetByCoord function data from a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModifierSet object can be subset to positions as defined in coord.

If coord contains a column mod and x is a Modifier object, it will be filtered to identifiers matching the modType of x. To disable this behaviour remove the column mod from coord or set type = NA

labelByCoord functions similarly. It will return a SplitDataFrameList, which matches the dimensions of the aggregated data plus the labels column, which contains logical values to indicate selected positions.

Usage

subsetByCoord(x, coord, ...)

labelByCoord(x, coord, ...)

## S4 method for signature 'Modifier,GRanges'
subsetByCoord(x, coord, ...)

## S4 method for signature 'Modifier,GRangesList'
subsetByCoord(x, coord, ...)

## S4 method for signature 'ModifierSet'
subset(x, name, pos = 1L, ...)

## S4 method for signature 'ModifierSet,GRanges'
subsetByCoord(x, coord, ...)

## S4 method for signature 'ModifierSet,GRangesList'
subsetByCoord(x, coord, ...)

## S4 method for signature 'Modifier,GRanges'
labelByCoord(x, coord, ...)

## S4 method for signature 'Modifier,GRangesList'
labelByCoord(x, coord, ...)

## S4 method for signature 'ModifierSet,GRanges'
labelByCoord(x, coord, ...)

## S4 method for signature 'ModifierSet,GRangesList'
labelByCoord(x, coord, ...)

## S4 method for signature 'SplitDataFrameList,GRanges'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceData'
subset(x, name, pos = 1L, ...)

## S4 method for signature 'SequenceData,GRanges'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceData,GRangesList'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataSet'
subset(x, name, pos = 1L, ...)

## S4 method for signature 'SequenceDataSet,GRanges'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataSet,GRangesList'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataList'
subset(x, name, pos = 1L, ...)

## S4 method for signature 'SequenceDataList,GRanges'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataList,GRangesList'
subsetByCoord(x, coord, ...)

## S4 method for signature 'SequenceData,GRanges'
labelByCoord(x, coord, ...)

## S4 method for signature 'SequenceData,GRangesList'
labelByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataSet,GRanges'
labelByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataSet,GRangesList'
labelByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataList,GRanges'
labelByCoord(x, coord, ...)

## S4 method for signature 'SequenceDataList,GRangesList'
labelByCoord(x, coord, ...)

Arguments

x	a SequenceData, SequenceDataSet, SequenceDataList, Modifier or ModifierSet object.
coord	coordinates of position to subset to. Either a GRanges or a GRangesList object. For both types the 'Parent' column is expected to match the transcript name.
...	Optional parameters: type: the modification type used for subsetting. By default this is derived from the modType(x), but it can be overwritten using type. It must be a valid shortName for a modification according to shortName(ModRNAString()) or shortName(ModDNAString()) (depending on the type of Modifier class) and of course be present in metadata column mod of coord. To disable subsetting based on type, set type = NA. flanking: a single integer value to select how many flanking position should be included in the subset (default: flanking = 0L). merge: TRUE or FALSE: Should the overlapping selections be merged? This is particular important, if flanking value != 0L are set. (default: merge = TRUE). perTranscript: TRUE or FALSE: Should the positions labeled per transcript and not per chromosome? (default: perTranscript = FALSE).
name	Optional: Limit results to one specific transcript.
pos	Optional: Limit results to a specific position.

Value

If 'x' is a

• SequenceData or Modifier: a SplitDataFrameList with elments per transcript.

• SequenceDataSet, SequenceDataList or ModifierSet: a SimpleList of SplitDataFrameList with elments per transcript.

Examples

data(msi,package="RNAmodR")
mod <- modifications(msi)
coord <- unique(unlist(mod))
coord$score <- NULL
coord$sd <- NULL
subsetByCoord(msi,coord)

---