Package 'STRMPS' reference manual

Title:	Analysis of Short Tandem Repeat (STR) Massively Parallel Sequencing (MPS) Data
Description:	Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 'STRMPS' can work with the package 'STRaitRazoR' (an R interface to the 'STRaitRazor' commandline tool) for added speed. 'STRaitRazoR' only works on linux and can found at <https://github.com/svilsen/STRaitRazoR>. The analyses and framework implemented in this package relies on the papers of Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018) <doi:10.1016/j.fsigen.2018.04.003>. Lastly, note that the parallelisation in the package relies on 'mclapply()' and, thus, speed-ups will only be seen on UNIX based systems.
Authors:	Søren B. Vilsen
Maintainer:	Søren B. Vilsen <[email protected]>
License:	MIT + file LICENSE
Version:	0.5.8
Built:	2025-01-04 04:41:49 UTC
Source:	https://github.com/svilsen/strmps

Block length of the missing motif.

Description

Given a motif length and a string it finds the blocks of the string.

Usage

BLMM(s, motifLength = 4, returnType = "numeric")
BLMM(s, motifLength = 4, returnType = "numeric")

Arguments

`s`	a string of either class: 'character' or 'DNAString'.
`motifLength`	the known motif length of the STR region.
`returnType`	the type of return wanted. Takes three values 'numeric', 'string', or 'fullList' (or any other combination cased letters).

Details

If returnType is 'numeric', the function returns the numeric value of the LUS. If returnType is instead chosen as 'string', the function returns "[AATG]x" i.e. the motif, AATG, is repeated 'x' times. Lastly if the returnType is set to fullList, the function returns a list of data.frames containing every possible repeat structure their start and the numeric value of the repeat unit length.

Value

Depending on returnType it return an object of class 'numeric', 'string', or 'fulllist'.

Examples

# Creating compound string 's'
stretch1 = paste0(rep("AATG", 10), collapse = "")
stretch2 = paste0(rep("ATCG", 4), collapse = "")

s = paste0(stretch1, stretch2)

# Return BLMM only
BLMM(s, motifLength = 4, returnType = "numeric")

# Return BLMM and motif of stretch
BLMM(s, motifLength = 4, returnType = "string")

# Return all blocks of 's'
BLMM(s, motifLength = 4, returnType = "fulllist")
# Creating compound string 's'
stretch1 = paste0(rep("AATG", 10), collapse = "")
stretch2 = paste0(rep("ATCG", 4), collapse = "")

s = paste0(stretch1, stretch2)

# Return BLMM only
BLMM(s, motifLength = 4, returnType = "numeric")

# Return BLMM and motif of stretch
BLMM(s, motifLength = 4, returnType = "string")

# Return all blocks of 's'
BLMM(s, motifLength = 4, returnType = "fulllist")

Extract STR region information

Description

Identifies the marker of the read using flanking regions and trims the read to include what is between the flanking regions.

Combined extract STR region information.

Description

Identifies the marker of the read for both the provided and reverse complement flanking regions. The resulting lists are then combined into a single list.

Combined extract STR region information.

Description

Identifies the marker of the read for both the provided and reverse complement flanking regions.

Extract STR region information of the reverse complement DNA strand.

Description

Identifies the marker of the read using reverse complement flanking regions and trims the read to include what is between the flanking regions.

Find neighbours

Description

Generic function for finding neighbouring strings, given identified alleles.

Usage

findNeighbours(stringCoverageGenotypeListObject, searchDirection,
  trace = FALSE)
findNeighbours(stringCoverageGenotypeListObject, searchDirection,
  trace = FALSE)

Arguments

`stringCoverageGenotypeListObject`	A stringCoverageGenotypeList-class object.
`searchDirection`	The direction to search for neighbouring strings. Default is -1, indicating a search for '-1' stutters.
`trace`	Should a trace be shown?

Value

A 'neighbourList' with the neighbouring strings, in the specified direction, for the identified allele regions.

Find neighbours

Description

Generic function for finding neighbouring strings, given identified alleles.

Usage


  ## S4 method for signature 'stringCoverageGenotypeList'
findNeighbours(stringCoverageGenotypeListObject,
  searchDirection = -1, trace = FALSE)
## S4 method for signature 'stringCoverageGenotypeList'
findNeighbours(stringCoverageGenotypeListObject,
  searchDirection = -1, trace = FALSE)

Arguments

`stringCoverageGenotypeListObject`	A stringCoverageGenotypeList-class object.
`searchDirection`	The direction to search for neighbouring strings. Default is -1, indicating a search for '-1' stutters.
`trace`	Should a trace be shown?

Value

A 'neighbourList' with the neighbouring strings, in the specified direction, for the identified allele regions.

Find stutters

Description

Given identified alleles it search for '-1' stutters of the alleles.

Usage

findStutter(stringCoverageGenotypeListObject, trace = FALSE)
findStutter(stringCoverageGenotypeListObject, trace = FALSE)

Arguments

`stringCoverageGenotypeListObject`	A stringCoverageGenotypeList-class object.
`trace`	Should a trace be shown?

Value

A 'neighbourList' with the stutter strings for the identified allele regions.

Examples

# The object returned by merging a stringCoverageList-Object
# and a genotypeList-Object.
data("stringCoverageGenotypeList")

stutterList <- findStutter(stringCoverageGenotypeList)
stutterTibble <- subset(do.call("rbind", stutterList), !is.na(Genotype))

stutterTibble$BlockLengthMissingMotif
stutterTibble$NeighbourRatio
# The object returned by merging a stringCoverageList-Object
# and a genotypeList-Object.
data("stringCoverageGenotypeList")

stutterList <- findStutter(stringCoverageGenotypeList)
stutterTibble <- subset(do.call("rbind", stutterList), !is.na(Genotype))

stutterTibble$BlockLengthMissingMotif
stutterTibble$NeighbourRatio

Find stutters

Description

Given identified alleles it search for '-1' stutters of the alleles.

Usage


  ## S4 method for signature 'stringCoverageGenotypeList'
findStutter(stringCoverageGenotypeListObject,
  trace = FALSE)
## S4 method for signature 'stringCoverageGenotypeList'
findStutter(stringCoverageGenotypeListObject,
  trace = FALSE)

Arguments

`stringCoverageGenotypeListObject`	A stringCoverageGenotypeList-class object.
`trace`	Should a trace be shown?

Value

A 'neighbourList' with the stutter strings for the identified allele regions.

Examples

# The object returned by merging a stringCoverageList-Object
# and a genotypeList-Object.
data("stringCoverageGenotypeList")

stutterList <- findStutter(stringCoverageGenotypeList)
stutterTibble <- subset(do.call("rbind", stutterList), !is.na(Genotype))

stutterTibble$BlockLengthMissingMotif
stutterTibble$NeighbourRatio
# The object returned by merging a stringCoverageList-Object
# and a genotypeList-Object.
data("stringCoverageGenotypeList")

stutterList <- findStutter(stringCoverageGenotypeList)
stutterTibble <- subset(do.call("rbind", stutterList), !is.na(Genotype))

stutterTibble$BlockLengthMissingMotif
stutterTibble$NeighbourRatio

Flanking regions

Description

The flanking regions searched for to identify the markers and STR regions of all autosomal/X/Y STR's in the Illumina ForenSeq prep kit.

Usage

data("flankingRegions")
data("flankingRegions")

Format

A tibble containing the flanks (forward and reverse), motif, motif length, adjustment need to make it compatible with CE, and the shifts needed for further trimming, for each marker

Author(s)

Søren B. Vilsen [email protected]

Genotype list

Description

A reduced stringCoverageList restricted to the identified genotypes.

Genotype list

Description

The identified genotypes of the stringCoverageList data, created by the getGenotype function.

Usage

data("genotypeList")
data("genotypeList")

Format

A list of tibble's one for each of the 10 markers, showing which strings are the potential alleles based on the 'Coverage'.

Author(s)

Søren B. Vilsen [email protected]

Assigns genotype.

Description

getGenotype takes an stringCoverageList-object, assumes the sample is a reference file and assings a genotype, based on a heterozygote threshold, for every marker in the provided list.

Usage

getGenotype(stringCoverageListObject, colBelief = "Coverage",
  thresholdSignal = 0, thresholdHeterozygosity = 0.35,
  thresholdAbsoluteLowerLimit = 1)
getGenotype(stringCoverageListObject, colBelief = "Coverage",
  thresholdSignal = 0, thresholdHeterozygosity = 0.35,
  thresholdAbsoluteLowerLimit = 1)

Arguments

`stringCoverageListObject`	an stringCoverageList-object, created using the stringCoverage-function.
`colBelief`	the name of the coloumn used for identification.
`thresholdSignal`	threshold applied to the signal (generally the coverage) of every string.
`thresholdHeterozygosity`	threshold used to determine whether a marker is hetero- or homozygous.
`thresholdAbsoluteLowerLimit`	a lower limit on the coverage for it to be called as an allele.

Value

Returns a list, with an element for every marker in stringCoverageList-object, each element contains the genotype for a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

getGenotype(stringCoverageList)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

getGenotype(stringCoverageList)

Assigns genotype.

Description

getGenotype takes an stringCoverageList-object, assumes the sample is a reference file and assings a genotype, based on a heterozygote threshold, for every marker in the provided list.

Usage

## S4 method for signature 'stringCoverageList'
getGenotype(stringCoverageListObject,
  colBelief = "Coverage", thresholdSignal = 0,
  thresholdHeterozygosity = 0.35, thresholdAbsoluteLowerLimit = 1)
## S4 method for signature 'stringCoverageList'
getGenotype(stringCoverageListObject,
  colBelief = "Coverage", thresholdSignal = 0,
  thresholdHeterozygosity = 0.35, thresholdAbsoluteLowerLimit = 1)

Arguments

`stringCoverageListObject`	an stringCoverageList-object, created using the stringCoverage-function.
`colBelief`	the name of the coloumn used for identification.
`thresholdSignal`	threshold applied to the signal (generally the coverage) of every string.
`thresholdHeterozygosity`	threshold used to determine whether a marker is hetero- or homozygous.
`thresholdAbsoluteLowerLimit`	a lower limit on the coverage for it to be called as an allele.

Value

Returns a list, with an element for every marker in stringCoverageList-object, each element contains the genotype for a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

getGenotype(stringCoverageList)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

getGenotype(stringCoverageList)

Identified STR regions

Description

The identified STR regions of the sampleSequences.fastq file, created by the identifySTRRegions function.

Usage

data("identifiedSTRs")
data("identifiedSTRs")

Format

A list with an element for each of the 10 identified markers indicating which sequences were identified for each marker.

Author(s)

Søren B. Vilsen [email protected]

Idenfities the noise.

Description

identifyNoise takes an stringCoverageList-object and identifies the noise based on a signal threshold for every marker in the provided list.

Usage

identifyNoise(stringCoverageListObject, colBelief = "Coverage",
  thresholdSignal = 0.01)
identifyNoise(stringCoverageListObject, colBelief = "Coverage",
  thresholdSignal = 0.01)

Arguments

`stringCoverageListObject`	an stringCoverageList-object, created using the stringCoverage-function.
`colBelief`	the name of the coloumn used for identification.
`thresholdSignal`	threshold applied to the signal (generally the coverage) of every string.

Value

Returns a list, with an element for every marker in stringCoverageList-object, each element contains the genotype for a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

identifyNoise(stringCoverageList, thresholdSignal = 0.03)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

identifyNoise(stringCoverageList, thresholdSignal = 0.03)

Idenfities the noise.

Description

identifyNoise takes an stringCoverageList-object and identifies the noise based on a signal threshold for every marker in the provided list.

Usage

## S4 method for signature 'stringCoverageList'
identifyNoise(stringCoverageListObject,
  colBelief = "Coverage", thresholdSignal = 0.01)
## S4 method for signature 'stringCoverageList'
identifyNoise(stringCoverageListObject,
  colBelief = "Coverage", thresholdSignal = 0.01)

Arguments

`stringCoverageListObject`	an stringCoverageList-object, created using the stringCoverage-function.
`colBelief`	the name of the coloumn used for identification.
`thresholdSignal`	threshold applied to the signal (generally the coverage) of every string.

Value

Returns a list, with an element for every marker in stringCoverageList-object, each element contains the genotype for a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

identifyNoise(stringCoverageList, thresholdSignal = 0.03)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")

identifyNoise(stringCoverageList, thresholdSignal = 0.03)

Identify the STR regions of a fastq-file or ShortReadQ-object.

Description

identifySTRRegions takes a fastq-file location or a ShortReadQ-object and identifies the STR regions based on a directly adjacent flanking regions. The function allows for mutation in the flanking regions through the numberOfMutation argument.

Usage

identifySTRRegions(reads, flankingRegions, numberOfMutation, control)
identifySTRRegions(reads, flankingRegions, numberOfMutation, control)

Arguments

`reads`	either a fastq-file location or a ShortReadQ-object
`flankingRegions`	containing marker ID/name, the directly adjacent forward and reverse flanking regions, used for identification.
`numberOfMutation`	the maximum number of mutations (base-calling errors) allowed during flanking region identification.
`control`	an identifySTRRegions.control-object.

Value

The returned object is a list of lists. If the reverse complement strings are not included or if the control$combineLists == TRUE, a list, contains lists of untrimmed and trimmed strings for each row in flankingRegions. If control$combineLists == FALSE, the function returns a list of two such lists, one for forward strings and one for the reverse complement strings.

Examples

library("Biostrings")
library("ShortRead")

# Path to file
readPath <- system.file('extdata', "sampleSequences.fastq", package = 'STRMPS')

# Flanking regions
data("flankingRegions")

# Read the file into memory
readFile <- readFastq(readPath)
sread(readFile)
quality(readFile)

# Identify the STR's of the file, both readPath and readFile can be used.

identifySTRRegions(reads = readFile, flankingRegions = flankingRegions,
                   numberOfMutation = 1,
                   control = identifySTRRegions.control(
                       numberOfThreads = 1,
                       includeReverseComplement = FALSE)
                   )

library("Biostrings")
library("ShortRead")

# Path to file
readPath <- system.file('extdata', "sampleSequences.fastq", package = 'STRMPS')

# Flanking regions
data("flankingRegions")

# Read the file into memory
readFile <- readFastq(readPath)
sread(readFile)
quality(readFile)

# Identify the STR's of the file, both readPath and readFile can be used.

identifySTRRegions(reads = readFile, flankingRegions = flankingRegions,
                   numberOfMutation = 1,
                   control = identifySTRRegions.control(
                       numberOfThreads = 1,
                       includeReverseComplement = FALSE)
                   )

Identify the STR regions of a fastq-file or ShortReadQ-object.

Description

Usage

## S4 method for signature 'character'
identifySTRRegions(reads, flankingRegions,
  numberOfMutation = 1, control = identifySTRRegions.control())
## S4 method for signature 'character'
identifySTRRegions(reads, flankingRegions,
  numberOfMutation = 1, control = identifySTRRegions.control())

Arguments

`reads`	path to fastq-file.
`flankingRegions`	containing marker ID/name, the directly adjacent forward and reverse flanking regions, used for identification.
`numberOfMutation`	the maximum number of mutations (base-calling errors) allowed during flanking region identification.
`control`	an identifySTRRegions.control-object.

Value

Examples

library("Biostrings")
library("ShortRead")

# Path to file
readPath <- system.file('extdata', "sampleSequences.fastq", package = 'STRMPS')

# Flanking regions
data("flankingRegions")

# Read the file into memory
readFile <- readFastq(readPath)
sread(readFile)
quality(readFile)

# Identify the STR's of the file, both readPath and readFile can be used.

identifySTRRegions(reads = readFile, flankingRegions = flankingRegions,
                   numberOfMutation = 1,
                   control = identifySTRRegions.control(
                       numberOfThreads = 1,
                       includeReverseComplement = FALSE)
                   )

library("Biostrings")
library("ShortRead")

# Path to file
readPath <- system.file('extdata', "sampleSequences.fastq", package = 'STRMPS')

# Flanking regions
data("flankingRegions")

# Read the file into memory
readFile <- readFastq(readPath)
sread(readFile)
quality(readFile)

# Identify the STR's of the file, both readPath and readFile can be used.

identifySTRRegions(reads = readFile, flankingRegions = flankingRegions,
                   numberOfMutation = 1,
                   control = identifySTRRegions.control(
                       numberOfThreads = 1,
                       includeReverseComplement = FALSE)
                   )

Identify the STR regions of a fastq-file or ShortReadQ-object.

Description

Usage

## S4 method for signature 'ShortReadQ'
identifySTRRegions(reads, flankingRegions,
  numberOfMutation = 1, control = identifySTRRegions.control())
## S4 method for signature 'ShortReadQ'
identifySTRRegions(reads, flankingRegions,
  numberOfMutation = 1, control = identifySTRRegions.control())

Arguments

`reads`	a ShortReadQ-object
`flankingRegions`	containing marker ID/name, the directly adjacent forward and reverse flanking regions, used for identification.
`numberOfMutation`	the maximum number of mutations (base-calling errors) allowed during flanking region identification.
`control`	an identifySTRRegions.control-object.

Value

Examples

library("Biostrings")
library("ShortRead")

# Path to file
readPath <- system.file('extdata', "sampleSequences.fastq", package = 'STRMPS')

# Flanking regions
data("flankingRegions")

# Read the file into memory
readFile <- readFastq(readPath)
sread(readFile)
quality(readFile)

# Identify the STR's of the file, both readPath and readFile can be used.

identifySTRRegions(reads = readFile, flankingRegions = flankingRegions,
                   numberOfMutation = 1,
                   control = identifySTRRegions.control(
                       numberOfThreads = 1,
                       includeReverseComplement = FALSE)
                   )

library("Biostrings")
library("ShortRead")

# Path to file
readPath <- system.file('extdata', "sampleSequences.fastq", package = 'STRMPS')

# Flanking regions
data("flankingRegions")

# Read the file into memory
readFile <- readFastq(readPath)
sread(readFile)
quality(readFile)

# Identify the STR's of the file, both readPath and readFile can be used.

identifySTRRegions(reads = readFile, flankingRegions = flankingRegions,
                   numberOfMutation = 1,
                   control = identifySTRRegions.control(
                       numberOfThreads = 1,
                       includeReverseComplement = FALSE)
                   )

Control function for identifySTRRegions

Description

A list containing default parameters passed to the identifySTRRegions function.

Usage

identifySTRRegions.control(colList = NULL, numberOfThreads = 4L,
  reversed = TRUE, includeReverseComplement = TRUE, combineLists = TRUE,
  removeEmptyMarkers = TRUE, matchPatternMethod = "mclapply")
identifySTRRegions.control(colList = NULL, numberOfThreads = 4L,
  reversed = TRUE, includeReverseComplement = TRUE, combineLists = TRUE,
  removeEmptyMarkers = TRUE, matchPatternMethod = "mclapply")

Arguments

`colList`	The position of the forward, reverse, and motifLength columns in the flanking region tibble/data.frame. If 'NULL' a function searches for the words 'forward', 'reverse', and 'motif' ot identify the columns.
`numberOfThreads`	The number of threads used by mclapply (stuck at '2' on windows).
`reversed`	TRUE/FALSE: In a revrse complementary run, should the strings/quality be reversed (recommended)?
`includeReverseComplement`	TRUE/FALSE: Should the function also search for the reverse complement DNA strand (recommended)?
`combineLists`	TRUE/FALSE: If 'includeReverseComplement' is TRUE, should the sets be combined?
`removeEmptyMarkers`	TRUE/FALSE: Should markers returning no identified regions be removed?
`matchPatternMethod`	Which method should be used to identify the flanking regions (only 'mclapply' implemented at the moment)?

Value

A control list setting default behaviour.

Merge genotypeIdentifiedList and stringCoverageList.

Description

mergeGenotypeStringCoverage merges genotypeIdentifiedList-objects and stringCoverageList-objects.

Usage

mergeGenotypeStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)
mergeGenotypeStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)

Arguments

`stringCoverageListObject`	a stringCoverageList-object, created using the stringCoverage-function.
`noiseGenotypeIdentifiedListObject`	a noiseGenotypeIdentifiedList-object, created using the getGenotype-function.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)

Merge genotypeIdentifiedList and stringCoverageList.

Description

mergeGenotypeStringCoverage merges genotypeIdentifiedList-objects and stringCoverageList-objects.

Usage


  ## S4 method for signature 'genotypeIdentifiedList'
mergeGenotypeStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)
## S4 method for signature 'genotypeIdentifiedList'
mergeGenotypeStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)

Arguments

`stringCoverageListObject`	a stringCoverageList-object, created using the stringCoverage-function.
`noiseGenotypeIdentifiedListObject`	a noiseGenotypeIdentifiedList-object, created using the getGenotype-function.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)

Merge noiseIdentifiedList and stringCoverageList.

Description

mergeNoiseStringCoverage merges noiseIdentifiedList-objects and stringCoverageList-objects.

Usage

mergeNoiseStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)
mergeNoiseStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)

Arguments

`stringCoverageListObject`	a stringCoverageList-object, created using the stringCoverage-function.
`noiseGenotypeIdentifiedListObject`	a noiseGenotypeIdentifiedList-object, created using the identifyNoise-function.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)

Merge noiseIdentifiedList and stringCoverageList.

Description

mergeNoiseStringCoverage merges noiseIdentifiedList-objects and stringCoverageList-objects.

Usage


  ## S4 method for signature 'noiseIdentifiedList'
mergeNoiseStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)
## S4 method for signature 'noiseIdentifiedList'
mergeNoiseStringCoverage(stringCoverageListObject,
  noiseGenotypeIdentifiedListObject)

Arguments

`stringCoverageListObject`	a stringCoverageList-object, created using the stringCoverage-function.
`noiseGenotypeIdentifiedListObject`	a noiseGenotypeIdentifiedList-object, created using the identifyNoise-function.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)
# Strings aggregated by 'stringCoverage()'
data("stringCoverageList")
# Genotypes identified by 'getGenotype()'
data("genotypeList")
# Noise identified by 'identifyNoise()'
data("noiseList")

mergeGenotypeStringCoverage(stringCoverageList, genotypeList)
mergeNoiseStringCoverage(stringCoverageList, noiseList)

A neighbour list

Description

A list of the identified neightbours of the called alleles in a stringCoverageGenotypeList

Noise list

Description

Creates a flag to the sequences in a stringCoverageList which cloud be classified as noise.

Noise list

Description

The identified noise of the stringCoverageList data, created by the identifyNoise function.

Usage

data("noiseList")
data("noiseList")

Format

A list of tibble's one for each of the 10 markers, showing which strings can be safely classified as noise based on the 'Coverage'.

Author(s)

Søren B. Vilsen [email protected]

Quality score to probability

Description

Converts a quality score (Phred or Solexa) to a probability of error.

Usage

phredQualityProbability(q)

solexaQualityProbability(q)
phredQualityProbability(q)

solexaQualityProbability(q)

Arguments

`q`	Quality score.

Value

phredQualityScore(q_phred) and solexaQualityScore(q_solexa) returns a probability of error.

Examples

q_phred = phredQualityScore(1e-3)
q_solexa = solexaQualityScore(1e-3)

phredQualityProbability(q_phred)
solexaQualityProbability(q_solexa)
q_phred = phredQualityScore(1e-3)
q_solexa = solexaQualityScore(1e-3)

phredQualityProbability(q_phred)
solexaQualityProbability(q_solexa)

Convert probability to quality score

Description

Calculates the quality score (Phred or Solexa) given a probability of error.

Usage

phredQualityScore(p)

solexaQualityScore(p)
phredQualityScore(p)

solexaQualityScore(p)

Arguments

`p`	Probability of error.

Value

phredQualityScore(p) returns a Phred quality score.

solexaQualityScore(p) returns a Solexa quality score.

Examples

p <- 1e-3
phredQualityScore(p)
solexaQualityScore(p)
p <- 1e-3
phredQualityScore(p)
solexaQualityScore(p)

Get string coverage STR identified objects.

Description

stringCoverage takes an extractedReadsList-object and finds the coverage of every unique string for every marker in the provided list.

Usage

stringCoverage(extractedReadsListObject, control = stringCoverage.control())
stringCoverage(extractedReadsListObject, control = stringCoverage.control())

Arguments

`extractedReadsListObject`	an extractedReadsList-object, created using the identifySTRRegions-function.
`control`	an stringCoverage.control-object.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))
# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))

Get string coverage STR identified objects.

Description

stringCoverage takes an extractedReadsList-object and finds the coverage of every unique string for every marker in the provided list.

Usage

## S4 method for signature 'extractedReadsList'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())
## S4 method for signature 'extractedReadsList'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())

Arguments

`extractedReadsListObject`	an extractedReadsList-object, created using the identifySTRRegions-function.
`control`	an stringCoverage.control-object.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))
# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))

Get string coverage STR identified objects.

Description

stringCoverage takes an extractedReadsList-object and finds the coverage of every unique string for every marker in the provided list.

Usage

## S4 method for signature 'extractedReadsListCombined'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())
## S4 method for signature 'extractedReadsListCombined'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())

Arguments

`extractedReadsListObject`	an extractedReadsList-object, created using the identifySTRRegions-function.
`control`	an stringCoverage.control-object.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))
# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))

Get string coverage STR identified objects.

Description

stringCoverage takes an extractedReadsList-object and finds the coverage of every unique string for every marker in the provided list.

Usage


  ## S4 method for signature 'extractedReadsListNonCombined'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())
## S4 method for signature 'extractedReadsListNonCombined'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())

Arguments

`extractedReadsListObject`	an extractedReadsList-object, created using the identifySTRRegions-function.
`control`	an stringCoverage.control-object.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))
# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))

Get string coverage STR identified objects.

Description

stringCoverage takes an extractedReadsList-object and finds the coverage of every unique string for every marker in the provided list.

Usage


  ## S4 method for signature 'extractedReadsListReverseComplement'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())
## S4 method for signature 'extractedReadsListReverseComplement'
stringCoverage(extractedReadsListObject,
  control = stringCoverage.control())

Arguments

`extractedReadsListObject`	an extractedReadsList-object, created using the identifySTRRegions-function.
`control`	an stringCoverage.control-object.

Value

Returns a list, with an element for every marker in extractedReadsList-object, each element contains the string coverage of all unique strings of a given marker.

Examples

# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))
# Regions identified using 'identifySTRs()'
data("identifiedSTRs")

# Limiting and restructuring
sortedIncludedMarkers <- sapply(names(identifiedSTRs$identifiedMarkersSequencesUniquelyAssigned),
                                function(m) which(m == flankingRegions$Marker))

# Aggregate the strings
stringCoverage(extractedReadsListObject = identifiedSTRs,
               control = stringCoverage.control(
                   motifLength = flankingRegions$MotifLength[sortedIncludedMarkers],
                   Type = flankingRegions$Type[sortedIncludedMarkers],
                   numberOfThreads = 1,
                   trace = FALSE,
                   simpleReturn = TRUE))

String coverage coontrol object

Description

String coverage coontrol object

Usage

stringCoverage.control(motifLength = 4, Type = "AUTOSOMAL",
  simpleReturn = TRUE, includeLUS = FALSE, numberOfThreads = 4L,
  meanFunction = mean, includeAverageBaseQuality = FALSE, trace = FALSE,
  uniquelyAssigned = TRUE)
stringCoverage.control(motifLength = 4, Type = "AUTOSOMAL",
  simpleReturn = TRUE, includeLUS = FALSE, numberOfThreads = 4L,
  meanFunction = mean, includeAverageBaseQuality = FALSE, trace = FALSE,
  uniquelyAssigned = TRUE)

Arguments

`motifLength`	The motif lengths of each marker.
`Type`	The chromosome type of each marker (autosomal, X, or Y).
`simpleReturn`	TRUE/FALSE: Should the returned object be simplified?
`includeLUS`	TRUE/FALSE: Should the LUS of each region be calculated?
`numberOfThreads`	The number of cores used for parallelisation.
`meanFunction`	The function used to average the base qualities.
`includeAverageBaseQuality`	Should the average base quality of the region be included?
`trace`	TRUE/FALSE: Show trace?
`uniquelyAssigned`	TRUE/FALSE: Should regions not uniquely assigned be removed?

Details

Control function for the 'stringCoverage' function. Sets default values for the parameters.

Value

List of parameters used for the 'stringCoverage' function.

Combined string coverage and genotype information

Description

A merge of the stringCoverageList and genotypeList data.

Usage

data("stringCoverageGenotypeList")
data("stringCoverageGenotypeList")

Format

A list of tibble's one for each of the 10 markers containing the combined string coverage and genotypic information.

Author(s)

Søren B. Vilsen [email protected]

Combined stringCoverage- and genotypeIdentifiedList

Description

Merges a stringCoverageList with a genotypeIdentifiedList.

Aggregated string coverage.

Description

The aggregated string coverage of the identifiedSTRs data, created by the stringCoverage function.

Usage

data("stringCoverageList")
data("stringCoverageList")

Format

A list of tibble's one for each of the 10 markers, showing the aggregated information on a string-by-string basis.

Author(s)

Søren B. Vilsen [email protected]

A string coverage list

Description

A list of tibbles, one for every marker, used to contain the sequencing information of STR MPS data. The tibbles should include columns with the following names: "Marker", "BasePairs", "Allele", "Type", "MotifLength", "ForwardFlank", "Region", "ReverseFlank", "Coverage", "AggregateQuality", and "Quality".

Combined stringCoverage- and noiseIdentifiedList

Description

Merges a stringCoverageList with a noiseIdentifiedList

Workflow function

Description

The function takes an input file and performs the entire analysis workflow described in (ADD REF). The function creates a series of objects needed for further analyses. An output folder can be provided to store the objects as .RData-files.

Usage

STRMPSWorkflow(input, output = NULL, continueCheckpoint = NULL,
  control = workflow.control())
STRMPSWorkflow(input, output = NULL, continueCheckpoint = NULL,
  control = workflow.control())

Arguments

`input`	A path to a `.fastq`-file.
`output`	A directory where output-files are stored.
`continueCheckpoint`	Choose a checkpoint to continue from in the workflow. If NULL the function will run the entire workflow.
`control`	Function controlling non-crucial parameters and other control functions.

Value

If 'output' not provided the function simply returns the stringCoverageList-object. If an output is provided the function will store ALL created objects at the output-path, i.e. nothing is returned.

Examples

readPath <- system.file('extdata', 'sampleSequences.fastq', package = 'STRMPS')

STRMPSWorkflow(readPath,
               control = workflow.control(restrictType = "Autosomal",
                                          numberOfThreads = 1)
               )

readPath <- system.file('extdata', 'sampleSequences.fastq', package = 'STRMPS')

STRMPSWorkflow(readPath,
               control = workflow.control(restrictType = "Autosomal",
                                          numberOfThreads = 1)
               )

Batch wrapper for the workflow function

Description

The function takes an input directory and performs the entire analysis workflow described in (ADD REF). The function creates a series of objects needed for further analyses and stores them at the output location.

Usage

STRMPSWorkflowBatch(input, output, ignorePattern = NULL,
  continueCheckpoint = NULL, control = workflow.control())
STRMPSWorkflowBatch(input, output, ignorePattern = NULL,
  continueCheckpoint = NULL, control = workflow.control())

Arguments

`input`	A directory where fastq input-files are stored.
`output`	A directory where output-files are stored.
`ignorePattern`	A pattern parsed to grepl used to filter input strings.
`continueCheckpoint`	Choose a checkpoint to continue from in the workflow. If NULL the function will run the entire workflow.
`control`	Function controlling non-crucial parameters and other control functions.

Value

If 'output' not provided the function simply returns the stringCoverageList-object. If an output is provided the function will store ALL created objects at the output-path, i.e. nothing is returned.

Collect stutters files

Description

Collects all stutter files created by the batch version of the STRMPSWorkflow function.

Usage

STRMPSWorkflowCollectStutters(stutterDirectory, storeCollection = TRUE)
STRMPSWorkflowCollectStutters(stutterDirectory, storeCollection = TRUE)

Arguments

`stutterDirectory`	The out most directory containing all stutter files to be collected.
`storeCollection`	TRUE/FALSE: Should the collected tibble be stored? If 'FALSE' the tibble is returned.

Value

If 'storeCollection' is TRUE nothing is returned, else the stutter collection is returned.

Workflow default options

Description

Control object for workflow function returning a list of default parameter options.

Usage

workflow.control(numberOfMutations = 1, numberOfThreads = 4,
  createdThresholdSignal = 0.05, thresholdHomozygote = 0.4,
  internalTrace = FALSE, simpleReturn = TRUE, identifyNoise = FALSE,
  identifyStutter = FALSE, flankingRegions = NULL, useSTRaitRazor = FALSE,
  trimRegions = TRUE, restrictType = NULL, trace = TRUE,
  variantDatabase = NULL, reduceSize = FALSE)
workflow.control(numberOfMutations = 1, numberOfThreads = 4,
  createdThresholdSignal = 0.05, thresholdHomozygote = 0.4,
  internalTrace = FALSE, simpleReturn = TRUE, identifyNoise = FALSE,
  identifyStutter = FALSE, flankingRegions = NULL, useSTRaitRazor = FALSE,
  trimRegions = TRUE, restrictType = NULL, trace = TRUE,
  variantDatabase = NULL, reduceSize = FALSE)

Arguments

`numberOfMutations`	The maximum number of mutations (base-calling errors) allowed during flanking region identification.
`numberOfThreads`	The number of threads used by either the mclapply-function (stuck at '2' on windows) or STRaitRazor.
`createdThresholdSignal`	Noise threshold.
`thresholdHomozygote`	Homozygote threshold for genotype identiication.
`internalTrace`	Show trace.
`simpleReturn`	TRUE/FALSE: Should the regions be aggregated without including flanking regions?
`identifyNoise`	TRUE/FALSE: Should noise be identified.
`identifyStutter`	TRUE/FALSE: Should stutters be identified.
`flankingRegions`	The flanking regions used to identify the STR regions. If 'NULL' a default set is loaded and used.
`useSTRaitRazor`	TRUE/FALSE: Should the STRaitRazor command line tool (only linux is implemented) be used for flanking region identification.
`trimRegions`	TRUE/FALSE: Should the identified regions be further trimmed.
`restrictType`	A character vector specifying the marker 'Types' to be identified.
`trace`	TRUE/FALSE: Should a trace be shown?
`variantDatabase`	A tibble of 'trusted' STR regions.
`reduceSize`	TRUE/FALSE: Should the size of the data-set be reduced using the quality and the variant database?

Value

List of default of options.

Package 'STRMPS'

Help Index

Block length of the missing motif.

Description

Usage

Arguments

Details

Value

Examples

Extract STR region information

Description

Combined extract STR region information.

Description

Combined extract STR region information.

Description

Extract STR region information of the reverse complement DNA strand.

Description

Find neighbours

Description

Usage

Arguments

Value

Find neighbours

Description

Usage

Arguments

Value

Find stutters

Description

Usage

Arguments

Value

Examples

Find stutters

Description

Usage

Arguments

Value

Examples

Flanking regions

Description

Usage

Format

Author(s)

Genotype list

Description

Genotype list

Description

Usage

Format

Author(s)

Assigns genotype.

Description

Usage

Arguments

Value

Examples

Assigns genotype.

Description

Usage

Arguments

Value

Examples

Identified STR regions

Description

Usage

Format

Author(s)

Idenfities the noise.

Description

Usage

Arguments

Value

Examples

Idenfities the noise.

Description

Usage

Arguments

Value

Examples