Class "AlignedCluster"

Description

The AlignedCluster class is used to align a set of clustered sequences. The alignClusters function creates a new object of the AlignedCluster class. The alignAllClusters function takes a SequenceCluster object and returns a list of AlignedCluster objects. Clustering is performed using the ClustalW algorithm. The associated class and functions take care of encoding and decoding sequences into a form that can be used by the implementation of ClustalW in the msa package.

Usage

alignCluster(sequences, mysub = NULL, gapO = 10, gapE = 0.2)
alignAllClusters(sc, mysub = NULL, gapO = 10, gapE = 0.2)
makeSubsMatrix(match = 5, mismatch = -2)
## S4 method for signature 'AlignedCluster'
image(x, col = "black", cex = 1, main = "", ...)

Arguments

Value

The alignCluster function returns a new object of the AlignedCluster class. The alignAllClusters function returns a list of AlignedCluster objects. The makeSubMatrix function returns a symmetric substitution matrix.

Objects from the Class

Objects should be defined using the alignCluster or alignAllCluster functions. You typically pass in a character vector of sequences that have already been found to form a cluster.

Slots

alignment:

A matrix of aligned sequences; rows are sequences and columns are aligned positions..

weights

A numeric vactor; the numbof times each unique raw sequence occurs.

consensus:

A character vector; the consensus sequence of a successful alignment.

Details

Alignment is performed using the implementation of the ClustalW algorithm provided by the msa package. The existing code to align amino-acid protein sequences is used by converting the current alphabet to one that limits its use to the known amino acids. The decision to ue this method introduces a limitation: we are unable to align any set of seqeunces that use more than 25 distinct symbols. Attempting such an alignment will result in the alignCluster function returning a NULL value, which is passed on as one of the list items from alignAllClusters.

Note

These functions will only work if the ms package is installed. At the time of writing, CRAN does not install msa because of the way that msa uses the OpenMP protocol. So, SVAlignR only "Suggests" using the package and does not include it in the list of "Imports". Thus, to obtain this functionality, you must manually install msa from the yourself using the BiocManager::install function from BioConductor.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Examples

data(longreads)
seqs <- longreads$connection[1:15]
pad <- c(rep("0", 9), rep("", 6))
names(seqs) <- paste("LR", pad, 1:length(seqs), sep = "")
seqs <- seqs[!duplicated(seqs)]
mysub <- makeSubsMatrix(match = 2, mismatch = -6)
if (!requireNamespace("msa", quietly = TRUE)) {
  warning("Cluster alignment is only available if the 'msa' package is installed.\n")
} else {
  ab <- alignCluster(seqs, mysub)
  image(ab)
}

Class "Breakpoints"

Description

Classes for working with collections of breakpoints.

Usage

Breakpoints(working)
## S4 method for signature 'Breakpoints,missing'
plot(x, y, colset, ...)

Arguments

Value

The Breakpoints constructor returna a newly created object of the Breakpoints class. The plot method invisible returns its first argument.

Objects from the Class

Objects should be defined using the Breakpoints constructor. You typically pass in a data frame containing columns with the name/id of the breakpoint, and their chromosome name, start, and stop positions for each side of the break.

Slots

relLocation:

A numeric vector giving relative coordinates (in the unit interval) of the breakpoints along a chromosome, with first and last break points mapped to 0 and 1.

labels:

A character vector containing the names of the chromosomes.

ypos:

A numeric vector indicating the chromosomes involved in the full set of break points.

spread:

How far the display of different chromosomes should be spread apart on the y-axis.

id:

The character vector of break point names.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Class "Cipher"

Description

The Cipher class is used to change between different alphabets (and so behaves as a simple substitution cipher). The Cipher function creates a new object of the Cipher class.

Usage

Cipher(sampleText, split = "-", extras = c("-" = ":", "?" = "?"))
encode(cipher, text)
decode(cipher, text)

Arguments

Value

The Cipher function returns a new object of the Cipher class. The encode and decode functions return character vectors that are the same size as their input text parameters.

Objects from the Class

Objects should be defined using the Cipher constructor. You typically pass in a character vector of "words" that contain all the symbols that are contained in the text to be translated (i.e., encoded and decoded) between languages. A standard target alphabet is created along with forward and reverse transliteration rules.

Slots

forward:

A named character vector.

reverse:

A named character vector.

bytes:

The number of bytes used to encode each 'character' in the input test. Text with more than 72 unique characters use a two-byte encoding, which is enough for languages with up to 26*72 = 1872 characters.

Note

Attempting to manipulate a Cipher object using text containing NAs, missing values, or previously unknown symbols will result in an error.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Examples

motif <- "0-50-74-0-50-74-25-26-35"
alfa <- Cipher(motif)
alfa
en <-encode(alfa, motif)
en
de <- decode(alfa, en)
de

Class "DeBruijn"

Description

Classes for contructing de Bruijn graphs from collections of long read sequences mappe over brakpoints.

Usage

deBruijn(rawseq, M)

Arguments

Value

The deBruijn constructor returns a newly created object of the DeBruijn class.

Objects from the Class

Objects should be defined using the deBruijn constructor.

Slots

G:

An object of hhe igraph class

adjmat:

An adjacency matrix.

motifs:

A table of motifs/words.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

SVAlignR Sample Data

Description

These data sets contain binary versions of data describing breakpoints and long read sequences from an HPV-positive head-and-neck cancer sample.

Usage

data("longreads")

Format

longreads

A data frame with 197 rows and 5 columns. Each row represents a single Oxford Nanopore long read from a study of a cell line from an HPV-positive head-and-neck squamous cell tumor. The five columns contain (i) a unique identifier of each long read, (ii) the length of the read, in bytes, (iii) the ordered sequence of break points, represented as a hyphen separated list of numeric identifiers, (iv) manually estimated natural groups of reads, and (v) a manually curated indication of whether certain long reads should be omitted from the analysis.

breakpoints

A data frame with 82 rows and 11 columns. Each row represents a single breakpoint from a study of a cell line from an HPV-positive head-and-neck squamous cell tumor. The columns contain (1) a unique identifier that is used in the long read connections, (2-4) a description of the chromosomal segment to the left of the breakpoint, (5-7) a description of the chromosomal segment to the right of the breakpoint, (8-9) the orientation of the two chromosomal segments, (10) a shorthand description of the breakpoint with the segment names separated by a vertical bar and negative strands contained in parentheses, and (11) a shorthand representation of the reverse orientation of the breakpoint.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Source

Long read (Oxford Nanopore) sequencing was performed on samples prepared at the laboratory of Maura Gillison and David Symer. Characterization of long reads as a sequence of well-defined break points was performed by Keiko Akagi.

Examples

data(longreads)
head(longreads)

alphabet <- Cipher(longreads$connection)
en <- encode(alphabet, "0-50-74-0-50-74-35")
en
decode(alphabet, en)

Class "SequenceCluster"

Description

The SequenceCluster class is used to cluster sequences of "words" from an arbitrarily long alphabet. The SequenceCluster function returns a new object of the SequenceCluster class.

Usage

SequenceCluster(rawseq, method = c("needelman", "levenshtein"), NC = 5)
## S4 method for signature 'SequenceCluster,missing'
plot(x, type = "rooted", main = "Colored Clusters", ...)
updateClusters(sc, NC)
heat(x, ...)

Arguments

Value

The SequenceCluster function returns a new object of the SequenceCluster class.

Objects from the Class

Objects should be defined using the SequenceCluster constructor. You typically pass in a character vector of "words" to be clustered.

Slots

method:

A character vector describing which algorithm was used.

rawSequences

A character vector that contains the input words or "sequences" tthat were clustered.

weights

A numeric vactor; the numbof times each unique raw sequence occurs.

distance:

A dist object.

hc:

An hclust object.

NC:

An integer; the number of clusters cut from the dendrogram.

clusters:

An integer vector containing cluster assignments.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Examples

data(longreads)
sequences <- longreads$connection[1:30]        # named character vector
sequences <- sequences[!duplicated(sequences)] # dedup
sc <- SequenceCluster(sequences)               # cluster
plot(sc)                                    # visualize
sc <- updateClusters(sc, NC = 7)
plot(sc, type = "unrooted")

Class "StringGraph"

Description

The StringGraph class is used to represent graphs that arise from strings reprsenting long-read breakpoint sequences. The basic examples are: (1) "Motif Graphs" where the edges are subtring relations, and (2) "Decomposition Graphs" where the edges are restricted subtring relations that decompose a long read.

Usage

MotifGraph(motifNodes, alfa, name = "motif")
DecompositionGraph(decomp, alfa, motifNodes, name = "decomp")
exportSG(sg, outdir)
## S4 method for signature 'StringGraph,ANY'
plot(x, y, ...)

Arguments

Value

The MotifGraph and DecompositionGraph functions return a new object of the StringGraph class. The plot method and exportSG functions return nothing and are called for their side effects.

Objects from the Class

Objects should be defined using the MotifGraph or DecompositionGraph constructor. You typically pass in a "motifNodes" object, which is a list of sequence-strings separated by length, along with some auxiliary information.

Slots

name:

A character vector of length one.

edgelist:

A matrix representing a graph as a list of edges.

nodelist:

A matrix representing the nodes of the graph, along with their properties.

graph:

An igraph object.

layout:

A matrix containing x-y locations for the nodes.

Note

Attempting to manipulate a StringGraph object using text containing NAs, missing values, or previously unknown symbols will result in an error.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Class "Words"

Description

Provides the ability to find, count, and plot words of specific length in collections of strings in any sequence language.

Usage

makeWords(opstrings, K, nb = 1)
countWords(opstrings, K, alpha = NULL)
plotWords(K, m)

Arguments

Details

For constructing motifs, or for producing De Bruijn graphs, we need to be able to decompose a set of input strings into "words" of a fixed length. In our application, the words are derived from long-read sequences that cross multiple breakpoints. Each breakpoint is given a unique name/label, thatwhich can be of arbirtrary length in order to be maningful to the researchers. Using the Cipher class, we encode the breakpoint names into character strings of the same size. (In the original version of this package, we used single characters. That approach eventually proved to be inadequate when we looked at long-read data from samples with a very large number of breakpoints. We then extended the package to work with two-byte codes. This solution may eventually be extended to even longer coding sequences.)

The makeWords and countWords functions take as inputs a vector of character strings (typically describing long-read sequences) that have already been encoded into fixed-byte-length characters. They then find all words in those strings of a given fixed length. They only differ in the form of their output. The former function returns the word counts in their encoded form; the latter decodes them back to the original names (as long as you provide the optional appropriate Cipher argument).

The plotWords function gives a visible representaiton of words of length K sorted by their frequency. The x-axis contains the sorted word list; the y-axis is the frequency. The idea is that one can quickly figure out which words are most common in the input "text".

Value

The makeWords function returns a table of words (of length K) along with the counts of the number of times each one was seen in the input strings. The countWords function returns the same table, but with the words decoded back to the original language. The plotWords function returns a vector of the word counts for all words of length K in the list m.

Author(s)

Kevin R. Coombes <krc@silicovore.com>

Examples

data(longreads)             # read sample data
raw <- longreads$connection # get the raw strings
alfa <- Cipher(raw)         # make a translation cipher
coded <- encode(alfa, raw)  # encode all the input strings
makeWords(coded, 3)
countWords(coded, 3, alfa)
m <- lapply(1:8, function(J) countWords(coded, J, alfa))
plotWords(3, m)