Package 'ftrCOOL'

Description

This function transforms an amino acid to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

AA2Binary(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*20. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-AA2Binary(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This function converts the amino acids of a sequence to a list of physicochemical properties in the aaIndex file. For each amino acid, the function uses a numeric vector which shows the aaIndex of the amino acid.

Usage

AAindex(
  seqs,
  selectedAAidx = 1:554,
  standardized = TRUE,
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

In this function each amino acid is converted to a numeric vector. Elements of the vector represent a physicochemical property for the amino acid. In the aaIndex database, there are 554 amino acid indices. Users can choose the desired aaindex by specifying aaindexes through their ids or indexes in the aaIndex file, via selectedAAidx parameter.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(number of selected amino acid indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-AAindex(seqs = ptmSeqsVect, selectedAAidx=1:5,outFormat="mat")

ad<-paste0(dir,"/aaidx.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
AAindex(seqs = filePrs, selectedAAidx=1:5,standardized=TRUE,threshold=1,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Description

In this function, each sequence is divided into k equal partitions. The length of each part is equal to ceiling(l(lenght of the sequence)/k). The last part can have a different length containing the residual amino acids. The amino acid composition is calculated for each part.

Usage

AAKpartComposition(seqs, k = 3, normalized = TRUE, label = c())

Arguments

Value

a feature matrix with k*20 number of columns. The number of rows is equal to the number of sequences.

Note

Warning: The length of all sequences should be greater than k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-AAKpartComposition(seqs=filePrs,k=5,normalized=FALSE)

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AAutoCor(
  seqs,
  selectedAAidx = list(c("CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102",
    "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201")),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-AAutoCor(seqs=filePrs,maxlag=20,threshold=0.9,
type=c("Moran","Geary","NormalizeMBorto","AC"))

mat2<-AAutoCor(seqs=filePrs,maxlag=20,threshold=0.9,selectedAAidx=
list(c('CIDH920105','BHAR880101','CHAM820101','CHAM820102'),c('CHOC760101','BIGC670101')
,c('CHAM810101','DAYM780201')),type=c("AC","CC","ACC"))

Description

This function replace each amino acid of the sequence with a three-dimensional vector. Values are taken from the three hidden units of the neural network trained on structure alignments. The AESNN3 function can be applied to encode peptides of equal length.

Usage

AESNN3(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(5) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes.

References

Lin K, May AC, Taylor WR. Amino acid encoding schemes from protein structure alignments: multi-dimensional vectors to describe residue types. J Theor Biol (2002).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-AESNN3(seqs = ptmSeqsVect,outFormat="mat")

Description

This function checks the alphabets in a sequence. If one of the following conditions hold, the sequence will be deleted: 1. A peptide sequence containing non-standard amino acids, 2. A DNA sequence with an alphabet other than A, C, G, or T, 3. An RNA sequence having an alphabet other than A, C, G, or U.

Usage

alphabetCheck(sequences, alphabet = "aa", label = c())

Arguments

Value

'alphabetCheck' returns a list with two elements. The first element is a vector which contains valid sequences. The second element is a vector which contains the labels of the sequences (if any exists).

Note

This function receives a sequence vector and the label of sequences (if any). It deletes sequences (and their labels) containing non-standard alphabets.

Examples

seq<-alphabetCheck(sequences=c("AGDFLIAACNMLKIVYT","ADXVGAJK"),alphabet="aa")

Description

This function replaces nucleotides with a four-length vector. The first three elements represent the nucleotides and the forth holds the frequency of the nucleotide from the beginning of the sequence until the position of the nucleotide in the sequence. 'A' will be replaced with c(1, 1, 1, freq), 'C' with c(0, 1, 0, freq),'G' with c(1, 0, 0, freq), and 'T' with c(0, 0, 1, freq).

Usage

ANF_DNA(seqs, outFormat = "mat", outputFileDist = "", label = c())

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(4) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, W., Tran, H., Liang, Z. et al. Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci Rep 5, 13859 (2015).

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-ANF_DNA(seqs = LNC50Nuc,outFormat="mat")

Description

This function replaces ribonucleotides with a four-length vector. The first three elements represent the ribonucleotides and the forth holds the frequency of the ribonucleotide from the beginning of the sequence until the position of the ribonucleotide in the sequence. 'A' will be replaced with c(1, 1, 1, freq), 'C' with c(0, 1, 0, freq),'G' with c(1, 0, 0, freq), and 'U' with c(0, 0, 1, freq).

Usage

ANF_RNA(seqs, outFormat = "mat", outputFileDist = "", label = c())

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(4) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, W., Tran, H., Liang, Z. et al. Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci Rep 5, 13859 (2015).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-ANF_RNA(seqs = fileLNC,outFormat="mat")

Description

This function calculates the amphiphilic pseudo amino acid composition (Series) for each sequence.

Usage

APAAC(
  seqs,
  aaIDX = c("ARGP820101", "HOPT810101"),
  lambda = 30,
  w = 0.05,
  l = 1,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo amino acid composition for each physicochemical property. We have provided users with the ability to choose among different properties (i.e., not confined to hydrophobicity or hydrophilicity).

Value

A feature matrix such that the number of columns is 20^l+(number of chosen aaIndex*lambda) and the number of rows equals the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-APAAC(seqs=filePrs,l=2,lambda=3,threshold=1)

Description

This function calculates the amphiphilic pseudo k nucleotide composition(Di) (Series) for each sequence.

Usage

APkNUCdi_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of dinucleotides. We have provided users with the ability to choose among the 148 properties in the di-nucleotide index database.

Value

It is a feature matrix. The number of columns is 4^l+(number of the chosen indices*lambda) and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-APkNUCdi_DNA(seqs=fileLNC,ORF=TRUE,threshold=1)

Description

This function calculates the amphiphilic pseudo k ribonucleotide composition(Di) (Series) for each sequence.

Usage

APkNUCdi_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo ribonucleotide composition for each physicochemical property of di-ribonucleotides. We have provided users with the ability to choose among the 22 properties in the di-ribonucleotide index database.

Value

It is a feature matrix. The number of columns is 4^l+(number of the chosen indices*lambda) and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-APkNUCdi_RNA(seqs=fileLNC,ORF=TRUE,threshold=0.8)

Description

This function calculates the amphiphilic pseudo k nucleotide composition(Tri) (Series) for each sequence.

Usage

APkNUCTri_DNA(
  seqs,
  selectedIdx = c("Dnase I", "Bendability (DNAse)"),
  lambda = 3,
  w = 0.05,
  l = 3,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of trinucleotides. We have provided users with the ability to choose among the 12 properties in the tri-nucleotide index database.

Value

It is a feature matrix. The number of columns is 4^l+(number of the chosen indices*lambda) and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-APkNUCTri_DNA(seqs=fileLNC,l=3,threshold=1)

Description

ASA represents an amino acid by a numeric value. This function extracts the ASA from the output of SPINE-X software which predicts ASA for each amino acid in a peptide or protein sequence. The output of SPINE-X is a tab-delimited file. ASAs are in the 11th column of the file.

Usage

ASA(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same lengths such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ad<-paste0(dir,"/asa.txt")


PredASAdir<-system.file("testForder",package="ftrCOOL")
PredASAdir<-paste0(PredASAdir,"/ASAdir/")
ASA(PredASAdir,outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Description

This descriptor sufficiently considers the correlation information present not only between adjacent residues but also between intervening residues. This function calculates frequency of pair amino acids omitting gaps between them. Then this function normalizes each value through dividing each frequency by summition(frequencies).

Usage

ASDC(seqs, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 400 (all posible amino acid pairs).

References

Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-ASDC(seqs=filePrs)

Description

This descriptor sufficiently considers the correlation information present not only between adjacent nucleotides but also between intervening nucleotides This function calculates frequency of pair nucleotides omitting gaps between them. Then this function normalizes each value through dividing each frequency by summition(frequencies).

Usage

ASDC_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16 (All posible nucleotide pairs).

References

Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
fileLNC<-fa.read(file=fileLNC,alphabet="dna")[1:5]
mat1<-ASDC_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This descriptor sufficiently considers the correlation information present not only between adjacent ribo ribonucleotides but also between intervening nucleotides This function calculates frequency of pair ribonucleotides omitting gaps between them. Then this function normalizes each value through dividing each frequency by summition(frequencies).

Usage

ASDC_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16 (All posible ribonucleotide pairs).

References

Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
fileLNC<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA51.txt"),alphabet="rna")
mat1<-ASDC_RNA(seqs=fileLNC)

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AutoCorDiNUC_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")

mat2<-AutoCorDiNUC_DNA(seqs=fileLNC,selectedIdx=list(10,c(1,3),6:13,c(2:7))
,maxlag=15,type="CC")

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AutoCorDiNUC_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
fileLNC<-fa.read(fileLNC,alphabet="rna")
fileLNC<-fileLNC[1:20]
mat1<-AutoCorDiNUC_RNA(seqs=fileLNC,maxlag=20,type=c("Moran"))

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AutoCorTriNUC_DNA(
  seqs,
  selectedNucIdx = c("Dnase I", "Bendability (DNAse)"),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat1<-AutoCorTriNUC_DNA(seqs=fileLNC,selectedNucIdx=c(1:7),maxlag=20,type=c("Moran","Geary"))

mat2<-AutoCorTriNUC_DNA(seqs=fileLNC,selectedNucIdx=list(c(1,3),6:10,c(2:7)),
maxlag=15,type=c("AC","CC"))

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T1(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T1(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T2(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T2(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T3(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T3(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T4(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T4(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T5(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T5(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T6(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T6(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T7(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T7(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This function categorizes amino acids in 5 groups. Then represent group of amino acids by a 5 dimentional vector i.e.e1, e2, e3, e4, e5. e1=G, A, V, L, M, I, e2=F, Y, W, e3=K, R, H, e4=D, E, e5=S, T, C, P, N, Q. e1 is ecoded by 10000 e2 is encoded by 01000 and ... and e5 is encoded by 00001.

Usage

binary_5bit_T1(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*5. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_5bit_T1(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

The idea behind this function is: We have 20 amino acids and we can show them with at least 5 bits. A is encoded by (00011), C (00101), D (00110), E (00111), F(01001), G (01010), H (01011), I (01100), K (01101), L (01110), M (10001), N (10010), P (10011), Q (10100), R (10101), S (10110), T (11000), V (11001), W (11010), Y (11100). This function transforms an amino acid to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_5bit_T2(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*5. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_5bit_T2(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This function categorizes amino acids in 6 groups. Then represent group of amino acids by a 6 dimentional vector i.e.e1, e2, e3, e4, e5, e6. e1=H, R, K, e2=D, E, N, D, e3=C, e4=S, T, P, A, G, e5=M, I, L, V, e6=F, Y, W. e1 is ecoded by 100000 e2 is encoded by 010000 and ... and e6 is encoded by 000001.

Usage

binary_6bit(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*6. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_6bit(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Description

This function creates a 20-dimentional numeric vector for each amino acid of a sequence. Each entry of the vector contains the similarity score of the amino acid with other amino acids including itself. The score is extracted from the Blosum62 matrix.

Usage

BLOSUM62(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*20 and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
filePr<-system.file("extdata/protein.fasta",package="ftrCOOL")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

ad<-paste0(dir,"/blosum62.txt")
vect<-BLOSUM62(seqs = filePr,outFormat="mat")
BLOSUM62(seqs = filePrs,outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Description

This function calculates the composition of k-spaced amino acid pairs. In other words, it computes the frequency of all amino acid pairs with k spaces.

Usage

CkSAApair(seqs, rng = 3, upto = FALSE, normalized = TRUE, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 400*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-CkSAApair(seqs=filePrs,rng=2,upto=TRUE,normalized=TRUE)

mat2<-CkSAApair(seqs=filePrs,rng=c(1,3,5))

Description

In this function, amino acids are first grouped into a category which is defined by the user. Later, the composition of the k-spaced grouped amino acids is computed. Please note that this function differs from CkSAApair which works on individual amino acids.

Usage

CkSGAApair(
  seqs,
  rng = 3,
  upto = FALSE,
  normalized = TRUE,
  Grp = "locFus",
  label = c()
)

Arguments

Details

Column names in the feature matrix follow G(?ss?). For example, G(1ss2) means Group1**Group2, where '*' is a wild character.

Value

This function returns a feature matrix. Row length is equal to the number of sequences and the number of columns is ((number of categorizes)^2)*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-CkSGAApair(seqs=filePrs,rng=2,upto=TRUE,Grp="aromatic")

mat2<-CkSGAApair(seqs=filePrs,rng=c(1,3,5),upto=FALSE,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Description

This function calculates the composition of k-spaced nucleotide pairs. In other words, it computes the frequency of all nucleotide pairs with k spaces.

Usage

CkSNUCpair_DNA(
  seqs,
  rng = 3,
  upto = FALSE,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat1<-CkSNUCpair_DNA(seqs=fileLNC,rng=2,upto=TRUE,ORF=TRUE,reverseORF=FALSE)
mat2<-CkSNUCpair_DNA(seqs=fileLNC,rng=c(1,3,5))

Description

This function calculates the composition of k-spaced ribonucleotide pairs. In other words, it computes the frequency of all ribonucleotide pairs with k spaces.

Usage

CkSNUCpair_RNA(
  seqs,
  rng = 3,
  upto = FALSE,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat1<-CkSNUCpair_RNA(seqs=fileLNC,rng=2,upto=TRUE,ORF=TRUE,reverseORF=FALSE)
mat2<-CkSNUCpair_RNA(seqs=fileLNC,rng=c(1,3,5))

Description

This function calculates the codon adaption index for each sequence.

Usage

codonAdaptionIndex(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains the value of the codon adaption index.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-codonAdaptionIndex(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This function calculates the codon fraction for each sequence.

Usage

CodonFraction(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

A feature matrix such that the number of columns is 4^3 and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-CodonFraction(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This function calculates the codon usage for each sequence.

Usage

CodonUsage_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

A feature matrix such that the number of columns is 4^3 and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-CodonUsage_DNA(fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This function calculates the codon usage for each sequence.

Usage

CodonUsage_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

A feature matrix such that the number of columns is 4^3 and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-CodonUsage_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This function calculates the grouped tripeptide composition with the conjoint triad grouping type.

Usage

conjointTriad(seqs, normalized = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows equals to the number of sequences and the number of columns is 7^3.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-conjointTriad(seqs=filePrs)

Description

This function calculates the grouped tripeptide composition with conjoint triad grouping type. For each k, it creates a 7^3 feature vector. K is the space between the first and the second amino acids and the second and the third amino acids of the tripeptide.

Usage

conjointTriadKS(seqs, rng = 3, upto = FALSE, normalized = FALSE, label = c())

Arguments

Details

A tripeptide with k spaces looks like AA1(ss..s)AA2(ss..s)AA3. AA stands for amino acids and s means space.

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (7^3)*(length rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-conjointTriadKS(filePrs,rng=2,upto=TRUE,normalized=TRUE)

mat2<-conjointTriadKS(filePrs,rng=c(1,3,5))

Description

This function calculates the composition, transition, and distribution for each sequence.

Usage

CTD(seqs, normalized = FALSE, label = c())

Arguments

Value

Output is a combination of three different matrices: Composition, Transition, and Distribution. You can obtain any of the three matrices by executing the corresponding function, i.e., CTDC, CTDT, and CTDD.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTDtotal<-CTD(seqs=filePrs,normalized=FALSE)

Description

This function computes the composition part of CTD. Thirteen properties are defined in this function. Each property categorizes the amino acids of the sequences into three groups. The grouped amino acid composition is calculated for each property. For more information, please check the references.

Usage

CTDC(seqs, normalized = FALSE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 3*7, where three is the number of groups and thirteen is the number of properties.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTD_C<-CTDC(seqs=filePrs,normalized=FALSE,label=c())

Description

This function computes the distribution part of CTD. It calculates fifteen values for each property. For more information, please check the references.

Usage

CTDD(seqs, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 15*7.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTD_D<-CTDD(seqs=filePrs)

Description

This function computes the transition part of CTD. Thirteen properties are defined in this function. Each property categorizes the amino acids of a sequence into three groups. For each property, the grouped amino acid transition (i.e., transitions 1-2, 1-3, and 2-3) is calculated. For more information, please check the references.

Usage

CTDT(seqs, normalized = FALSE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 3*7, where three is the number of transition types (i.e., 1-2, 1-3, and 2-3) and thirteen is the number of properties.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTD_T<-CTDT(seqs=filePrs,normalized=FALSE)

Description

This function computes the dipeptide deviation from the expected mean value.

Usage

DDE(seqs, label = c())

Arguments

Value

A feature matrix with 20^2=400 number of columns. The number of rows is equal to the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-DDE(seqs=filePrs)

Description

This function transforms a dinucleotide to a binary number with four bits which is enough to represent all the possible types of dinucleotides. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

DiNUC2Binary_DNA(
  seqs,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = "",
  label = c()
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the (length of the sequences-1). Otherwise, it is equal to (length of the sequences-1)*4. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-DiNUC2Binary_DNA(seqs = LNC50Nuc, binaryType="numBin",outFormat="mat")

Description

This function transforms a di-ribonucleotide to a binary number with four bits which is enough to represent all the possible types of di-ribonucleotides. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

DiNUC2Binary_RNA(
  seqs,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = "",
  label = c()
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is (length of the sequences-1). Otherwise, it is equal to (length of the sequences-1)*4. If outFormat is 'txt', all binary values will be written to a 'txt' file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-DiNUC2Binary_RNA(seqs = fileLNC, binaryType="numBin",outFormat="mat")

Description

This function replaces dinucleotides in a sequence with their physicochemical properties in the dinucleotide index file.

Usage

DiNUCindex_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 148 physicochemical indexes in the dinucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-nucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Athaliana1.fa",package="ftrCOOL")
vect<-DiNUCindex_DNA(seqs = fileLNC,outFormat="mat")

Description

This function replaces di-ribonucleotides in a sequence with their physicochemical properties in the di-ribonucleotide index file.

Usage

DiNUCindex_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 22 physicochemical indexes in the di-ribonucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-ribonucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-DiNUCindex_RNA(seqs = fileLNC,outFormat="mat")

Description

This function extracts the ordered and disordered amino acids in protein or peptide sequences. The input to the function is provided by VSL2 software. Also, the function converts order amino acids to '10' and disorder amino acids to '01'.

Usage

DisorderB(
  dirPath,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*2. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()

PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")
ad1<-paste0(dir,"/disorderB.txt")

DisorderB(PredDisdir,binaryType="numBin",outFormat="txt",outputFileDist=ad1)

unlink("dir", recursive = TRUE)

Description

This function extracts ordered and disordered amino acids in protein or peptide sequences. The input to the function is provided by VSL2 software. Also, the function returns number of order and disorder amino acids in the sequence.

Usage

DisorderC(dirPath)

Arguments

Value

The output is a feature matrix with 2 columns. The number of rows is equal to the number of sequences.

Examples

dir = tempdir()
PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")

mat<-DisorderC(PredDisdir)

Description

This function extracts ordered and disordered amino acids in protein or peptide sequences. The input to the function is provided by VSL2 software. The function represent order amino acids by 'O' and disorder amino acids by 'D'.

Usage

DisorderS(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same lengths such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()

PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")
ad1<-paste0(dir,"/disorderS.txt")

DisorderS(PredDisdir, outFormat="txt",outputFileDist=ad1)

unlink("dir", recursive = TRUE)

Description

In this function, first amino acids are grouped into a category which is one of 'cp13', 'cp14', 'cp19', 'cp20'. Users choose one of these terms to categorize amino acids. Then DistancePair function computes frequencies of all grouped residues and also all grouped-paired residues with [0,rng] distance. 'rng' is a parameter which already was set by the user.

Usage

DistancePair(seqs, rng = 3, normalized = TRUE, Grp = "cp14", label = c())

Arguments

Value

This function returns a feature matrix. Row length is equal to the number of sequences and the number of columns is (number of categorizes)+((number of categorizes)^2)*(rng+1).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-DistancePair(seqs=filePrs,rng=2,Grp="cp14")

Description

This function replaces dinucleotides in a sequence with their physicochemical properties which is multiplied by normalized frequency of that di-nucleotide.

Usage

DPCP_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 148 physicochemical indexes in the dinucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-nucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Athaliana1.fa",package="ftrCOOL")
vect<-DPCP_DNA(seqs = fileLNC,outFormat="mat")

Description

This function replaces di-ribonucleotides in a sequence with their physicochemical properties which is multiplied by normalized frequency of that di-ribonucleotide.

Usage

DPCP_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 22 physicochemical indexes in the di-ribonucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-ribonucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-DPCP_RNA(seqs = fileLNC,outFormat="mat")

Description

This function slides a window over the input sequence(s). Also, it computes the composition of amino acids that appears within the limits of the window.

Usage

EAAComposition(
  seqs,
  winSize = 50,
  overLap = TRUE,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

Column names in the output matrix are Wi(aa), where aa shows an amino acid type ("A", "C", "D",..., "Y") and i indicates the number of times that the window has moved over the sequence(s).

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (20 * number of partitions displayed by the window) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

When overlap is FALSE, the last partition represented by the window may have a different length with other parts.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-EAAComposition(seqs = ptmSeqsVect,winSize=50, overLap=FALSE,outFormat='mat')

ad<-paste0(dir,"/EaaCompos.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
EAAComposition(seqs = filePrs,winSize=50, overLap=FALSE,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Description

This function calculates the effective number of codon for each sequence.

Usage

EffectiveNumberCodon(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains the effective number of codon.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-EffectiveNumberCodon(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

In this function, amino acids are first grouped into user-defined categories. Then, enhanced grouped amino acid composition is computed. For details about the enhanced feature, please refer to function EAAComposition. Please note that this function differs from function EAAComposition which works on individual amino acids.

Usage

EGAAComposition(
  seqs,
  winSize = 50,
  overLap = TRUE,
  Grp = "locFus",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is ((number of categorizes) * (number of windows)) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat1<-EGAAComposition(seqs = ptmSeqsVect,winSize=20,overLap=FALSE,Grp="locFus")

mat2<-EGAAComposition(seqs = ptmSeqsVect,winSize=30,overLap=FALSE,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")),outFormat="mat")

ad<-paste0(dir,"/EGrpaaCompos.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
EGAAComposition(seqs = filePrs,winSize=20,Grp="cTriad",outFormat="txt"
,outputFileDist=ad)


unlink("dir", recursive = TRUE)

Description

This function replaces each nucleotide in the input sequence with its electron-ion interaction value. The resulting sequence is represented by a feature vector whose length is equal to the length of the sequence. Please check the references for more information.

Usage

EIIP(seqs, outFormat = "mat", outputFileDist = "", label = c())

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, Zhen, et al. "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data." Briefings in bioinformatics 21.3 (2020): 1047-1057.

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-EIIP(seqs = LNC50Nuc,outFormat="mat")

Description

This function slides a window over the input sequence(s). Also, it computes the composition of nucleotides that appears within the limits of the window.

Usage

ENUComposition_DNA(
  seqs,
  winSize = 50,
  overLap = TRUE,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (4 * number of partitions displayed by the window) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-ENUComposition_DNA(seqs = LNC50Nuc, winSize=20,outFormat="mat")

ad<-paste0(dir,"/ENUCcompos.txt")
fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
ENUComposition_DNA(seqs = fileLNC,outFormat="txt",winSize=20
,outputFileDist=ad,overLap=FALSE)


unlink("dir", recursive = TRUE)

Description

This function slides a window over the input sequence(s). Also, it computes the composition of ribonucleotides that appears within the limits of the window.

Usage

ENUComposition_RNA(
  seqs,
  winSize = 50,
  overLap = TRUE,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (4 * number of partitions displayed by the window) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-ENUComposition_RNA(seqs = fileLNC, winSize=20,outFormat="mat")

ad<-paste0(dir,"/ENUCcompos.txt")
ENUComposition_RNA(seqs = fileLNC,outFormat="txt",winSize=20
,outputFileDist=ad,overLap=FALSE)


unlink("dir", recursive = TRUE)

Description

This function is introduced by this package for the first time. It computes the expected value for each k-mer in a sequence. ExpectedValue(k-mer) = freq(k-mer) / ( freq(nucleotide1) * freq(nucleotide2) * ... * freq(nucleotidek) )

Usage

ExpectedValKmerNUC_DNA(
  seqs,
  k = 4,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (4^k).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-ExpectedValKmerNUC_DNA(seqs=fileLNC,k=4,ORF=TRUE,reverseORF=FALSE)

Description

This function is introduced by this package for the first time. It computes the expected value for each k-mer in a sequence. ExpectedValue(k-mer) = freq(k-mer) / ( freq(ribonucleotide1) * freq(ribonucleotide2) * ... * freq(ribonucleotidek) )

Usage

ExpectedValKmerNUC_RNA(
  seqs,
  k = 4,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (4^k).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-ExpectedValKmerNUC_RNA(seqs=fileLNC,k=4,ORF=TRUE,reverseORF=FALSE)

Description

This function is introduced by this package for the first time. It computes the expected value for each k-mer in a sequence. ExpectedValue(k-mer) = freq(k-mer) / (c_1 * c_2 * ... * c_k), where c_i is the number of codons that encrypt the i'th amino acid in the k-mer.

Usage

ExpectedValueAA(seqs, k = 2, normalized = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 20^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-ExpectedValueAA(seqs=filePrs,k=2,normalized=FALSE)

Description

This function is introduced by this package for the first time. In this function, amino acids are first grouped into user-defined categories. Later, the expected value of grouped amino acids is computed. Please note that this function differs from Function ExpectedValueAA which works on individual amino acids.

Usage

ExpectedValueGAA(seqs, k = 3, Grp = "locFus", normalized = TRUE, label = c())

Arguments

Details

for more information about ExpectedValueGAA, please refer to function ExpectedValueKmer.

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (number of categories)^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-ExpectedValueGAA(seqs=filePrs,k=2,Grp="locFus")

mat2<-ExpectedValueGAA(seqs=filePrs,k=1,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Description

This function is introduced by this package for the first time. In this function, amino acids are first grouped into user-defined categories. Later, the expected value of grouped k-mer is computed. Please note that this function differs from Function ExpectedValueKmerAA which works on individual amino acids.

Usage

ExpectedValueGKmerAA(
  seqs,
  k = 2,
  Grp = "locFus",
  normalized = TRUE,
  label = c()
)

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (number of categorizes)^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-ExpectedValueGKmerAA(seqs=filePrs,k=2,Grp="locFus")

mat2<-ExpectedValueGKmerAA(seqs=filePrs,k=1,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Description

This function computes the expected value of each k-mer by dividing the frequency of the kmer to multiplying frequency of each amino acid of the k-mer in the sequence.

Usage

ExpectedValueKmerAA(seqs, k = 2, normalized = TRUE, label = c())

Arguments

Details

ExpectedValue(k-mer) = freq(k-mer) / ( freq(aminoacid1) * freq(aminoacid2) * ... * freq(aminoacidk) )

Value

This function returns a feature matrix. The number of rows equals the number of sequences and the number of columns if upto set false, is 20^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-ExpectedValueKmerAA(filePrs,k=2,normalized=FALSE)

Description

This function reads a FASTA file. Each sequence starts with '>' in the file. This is a general function which can be applied to all types of sequences (i.e., protein/peptide, dna, and rna).

Usage

fa.read(file, legacy.mode = TRUE, seqonly = FALSE, alphabet = "aa")

Arguments

Value

a string vector such that each element is a sequence.

References

https://cran.r-project.org/web/packages/rDNAse/index.html

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
sequenceVectLNC<-fa.read(file=fileLNC,alphabet="dna")

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
sequenceVectPRO<-fa.read(file=filePrs,alphabet="aa")

Description

This function calculates the ficket score of each sequence.

Usage

fickettScore(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains the value of the fickett score.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-fickettScore(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This function calculates G-C content of each sequence.

Usage

G_Ccontent_DNA(
  seqs,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains G-C content of a sequence.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-G_Ccontent_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

This function calculates G-C content of each sequence.

Usage

G_Ccontent_RNA(
  seqs,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains G-C content of a sequence.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-G_Ccontent_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Description

In this function, amino acids are first grouped into user-defined categories. Later, the composition of the grouped amino acid k part is computed. Please note that this function differs from AAKpartComposition which works on individual amino acids.

Usage

GAAKpartComposition(
  seqs,
  k = 5,
  normalized = TRUE,
  Grp = "locFus",
  label = c()
)

Arguments

Value

a feature matrix with k*(number of categorizes) number of columns. The number of rows is equal to the number of sequences.

Note

Warning: The length of all sequences should be greater than k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-GAAKpartComposition(seqs=filePrs,k=5,Grp="aromatic")

mat2<-GAAKpartComposition(seqs=filePrs,k=3,normalized=FALSE,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Description

This function is introduced by this package for the first time. In this function, amino acids are first grouped into user-defined categories. Later, DDE is applied to grouped amino acids. Please note that this function differs from DDE which works on individual amino acids.

Usage

GrpDDE(seqs, Grp = "locFus", label = c())

Arguments

Value

A feature matrix with (number of categorizes)^2 number of columns. The number of rows is equal to the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-GrpDDE(seqs=filePrs,Grp="aromatic")

mat2<-GrpDDE(seqs=filePrs,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Description

This function calculates the frequency of all k-mers in the sequence(s).

Usage

kAAComposition(seqs, rng = 3, upto = FALSE, normalized = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns depends on rng vector. For each value k in the vector, (20)^k columns are created in the matrix.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-kAAComposition(seqs=filePrs,rng=3,upto=TRUE)
mat2<-kAAComposition(seqs=filePrs,rng=c(1,3),upto=TRUE)

Description

In this function, amino acids are first grouped into user-defined categories. Later, the composition of the k grouped amino acids is computed. Please note that this function differs from kAAComposition which works on individual amino acids.

Usage

kGAAComposition(
  seqs,
  rng = 3,
  upto = FALSE,
  normalized = TRUE,
  Grp = "locFus",
  label = c()
)

Arguments

Details

for more details, please refer to kAAComposition

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is ((number of categorizes)^k)*(length of rng vector).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-CkSGAApair(seqs=filePrs,rng=2,upto=TRUE,Grp="aromatic")

mat2<-CkSGAApair(seqs=filePrs,rng=c(1,3,5),Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNN_DNA(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testData51.txt"),alphabet="dna")

posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/posData51.txt"),alphabet="dna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/negData51.txt"),alphabet="dna")


trainSeq<-c(posSeqs,negSeqs)

labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNN_DNA(seqs=seqs,trainSeq=trainSeq,percent=5,labeltr=labeltr)

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNN_RNA(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

References

Wei,L., Su,R., Luan,S., Liao,Z., Manavalan,B., Zou,Q. and Shi,X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics, (2019).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/pos2RNA51.txt"),alphabet="rna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/neg2RNA51.txt"),alphabet="rna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA51.txt"),alphabet="rna")


trainSeq<-c(posSeqs,negSeqs)


labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNN_RNA(seqs=seqs,trainSeq=trainSeq,percent=10,labeltr=labeltr)

Description

This function needs an extra training data set and a label. We compute the similarity score of each input sequence with all sequences in the training data set. We use the BLOSUM62 matrix to compute the similarity score. The label shows the class of each sequence in the training data set. KNNPeptide finds the label of 1 It reports the frequency of each class for each k

Usage

KNNPeptide(seqs, trainSeq, percent = 30, label = c(), labeltr = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

Note

This function is usable for amino acid sequences with the same length in both training data set and the set of sequences.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])

posSeqs<-as.vector(read.csv(paste0(ptmSeqsADR,"/poSeqPTM101.csv"))[,2])
negSeqs<-as.vector(read.csv(paste0(ptmSeqsADR,"/negSeqPTM101.csv"))[,2])

posSeqs<-posSeqs[1:10]
negSeqs<-negSeqs[1:10]

trainSeq<-c(posSeqs,negSeqs)

labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNNPeptide(seqs=ptmSeqsVect,trainSeq=trainSeq,percent=10,labeltr=labeltr)

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNNProtein(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments