January 24, 2018
Package: naddaR
Title: Prediction of Protein Conserved Regions Using NADDA Algorithm
Version: 0.99.0
Author: Armen Abnousi <a.abnousi@gmail.com>
Maintainer: Armen Abnousi <a.abnousi@gmail.com>
Description: Generates protein sequence profiles using frequency of the k-mers present in the sequences and predicts conserved regions using those profiles. Can operate in parallel or on single processor.
biocViews: Clustering,
MultipleSequenceAlignment
Depends:
R (>= 3.4.1),
pbdMPI,
data.table,
Biostrings,
utils,
stats
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: Rdpack
RdMacros: Rdpack
counts the number of times each k-mer appears in the dataset and returns a dataframe indicating these counts. Each k-mer in each sequence is counted at most once, i.e., if there are multiple occureneces of a k-mer in one sequence, only one of them is counted.
count_kmers(obj, klen = 6, parallel = TRUE, nproc = ifelse(parallel,
comm.size(), 1), distributed = FALSE)| Argument | Description |
|---|---|
obj |
A filepath to a fasta file containing protein sequences or an AAStringSet object containing the sequences |
klen |
length of the k-mers to be used |
parallel |
Indicating whether the operation should be p erformed in parallel |
nproc |
Currently not supported. Will use all processors available to the job on cluster |
distributed |
A boolean, indicating whether the data is spread among multiple processors. |
If parallel is set to TRUE and distributed is set to FALSE , the method distributes the data between different processors and sets distributed to TRUE . Otherwise, if the parallel is set to FALSE and distributed is set to TRUE , the kmer frequencies are computed on each processor separately but then communicated between each other, and therefore at the end all processors have the same set of frequencies for kmers stored, using which they will generate frequency profiles for their chunk of sequences. If you prefer to run the operation in serial, set both parallel and distributed to FALSE .
Returns a dataframe with two columns. Each row includes one k-mer and an integer indicating the number of times that k-mer appears in the input dataset. Each k-mer in each sequence is counted at most once, i.e., if there are multiple occureneces of a k-mer in one sequence, only one of them is counted.
generate_instances for generation of
training and test instances for each index in each
sequence. list()
generate_profiles list()
comm.size for writing a distributed data object to a
single file
Armen Abnousi
list("\insertRef", list("abnousi2016fast"), list("naddaR"))
library(pbdMPI)
## Generate a set of three example protein sequences
seqs <- AAStringSet(c("seq1"="MLVVD",
"seq2"="PVVRA",
"seq3"="LVVR"))
## Count the kmers and generate a dataframe of the frequencies
freqs <- count_kmers(seqs, klen = 3, parallel = FALSE)
head(freqs)
## kmer count
##1: LVV 2
##2: MLV 1
##3: PVV 1
##4: VRA 1
##5: VVD 1
##6: VVR 2Tconstructs a dataframe where each row corresponds to one index of one protein sequence from the input dataset. It can be used to generate training and test sets to train a NADDA classification model or to predict the conserved indices of input sequences based on a trained model.
generate_instances(obj, labeled = TRUE, parallel = TRUE,
nproc = ifelse(parallel, comm.size(), 1), groundtruth = NULL,
truth_filename = NULL, klen = 6, normalize = TRUE, impute = TRUE,
winlen = 20, imputing_length = winlen%/%2, distributed = FALSE)| Argument | Description |
|---|---|
obj |
A filepath to a fasta file containing protein sequences or an AAStringSet object containing the sequences |
labeled |
TRUE if the method is called to construct a training set using a Pfam or InterPro labels or FALSE otherwise. |
parallel |
Indicating whether the operation should be performed in parallel |
nproc |
Currently not supported. Will use all processors available to the job on cluster |
groundtruth |
A character string. Can be Pfam or InterPro |
truth_filename |
The filepath to the labels file for generating the training set based on it |
klen |
length of the k-mers to be used |
normalize |
A boolean value, indicating whether the k-mer frequencies should be normalized |
impute |
A boolean value, indicating whether imputed values should be inserted at the beginning and the end of the profiles |
winlen |
An integer, size the window used for generation of each instance |
imputing_length |
An integer, number of frequencies from the beginning and end of a sequence profile that should be used to impute the new values |
distributed |
A boolean, indicating whether the data is spread among multiple processors. |
Current version only supports Pfam and InterPro output files for generation of training set. The output from Pfam output file needs to be tabularized (replacing spaces with tabs).
If parallel is set to TRUE and distributed is set to FALSE , the method distributes the data between different processors and sets distributed to TRUE . Otherwise, if the parallel is set to FALSE and distributed is set to TRUE , the kmer frequencies are computed on each processor separately but then communicated between each other, and therefore at the end all processors have the same set of frequencies for kmers stored, using which they will generate frequency profiles and instances of their chunk of sequences. If you prefer to run the operation in serial, set both parallel and distributed to FALSE .
Returns a dataframe with one row for each instance. Each row contains winlen k-mer frequencies around an index of a protein. The index number is stored in position column. Name of the sequence is stored in name column. If a training set is constructed, one column indicating whether it is a conserved index or not and a second column indicating the number of proteins in the dataset that have a similar conserved region are added to the returned dataframe.
generate_profiles for generation of
frequency profiles for each sequence list()
comm.size for writing a distributed data object to a
single file
Armen Abnousi
list("\insertRef", list("abnousi2016fast"), list("naddaR"))
## Generate a set of three example protein sequences
seqs <- AAStringSet(c("seq1"="MLVVD",
"seq2"="PVVRA",
"seq3"="LVVR"))
## Count the kmers and generate a dataframe of the frequencies
ins <- generate_instances(seqs, labeled = FALSE, parallel = FALSE, klen = 3, impute = TRUE, winlen = 5, normalize = FALSE)
head(ins)
## name position freqn2 freqn1 freqindex freqp1 freqp2
## seq1 1 1.5 1.5 1.0 2.0 1.0
## seq1 2 1.5 1.0 2.0 1.0 1.5
## seq1 3 1.0 2.0 1.0 1.5 1.5
## seq1 4 2.0 1.0 1.5 1.5 1.5
## seq1 5 1.0 1.5 1.5 1.5 1.5
## seq2 1 1.5 1.5 1.0 2.0 1.0
constructs a dataframe where each row corresponds to one index of one protein sequence from the input dataset. It can be used to generate training and test sets to train a NADDA classification model or to predict the conserved indices of input sequences based on a trained model.
generate_profiles(obj, klen = 6, parallel = TRUE, nproc = ifelse(parallel,
comm.size(), 1), normalize = TRUE, impute = TRUE, winlen = 20,
imputing_length = winlen%/%2, distributed = FALSE)| Argument | Description |
|---|---|
obj |
A filepath to a fasta file containing protein sequences or an AAStringSet object containing the sequences |
klen |
length of the k-mers to be used |
parallel |
Indicating whether the operation should be performed in parallel |
nproc |
Currently not supported. Will use all processors available to the job on cluster |
normalize |
A boolean value, indicating whether the k-mer frequencies should be normalized |
impute |
A boolean value, indicating whether imputed values should be inserted at the beginning and the end of the profiles |
winlen |
An integer, size the window used for generation of each instance |
imputing_length |
An integer, number of frequencies from the beginning and end of a sequence profile that should be used to impute the new values |
distributed |
A boolean, indicating whether the data is spread among multiple processors. |
If parallel is set to TRUE and distributed is set to FALSE , the method distributes the data between different processors and sets distributed to TRUE . Otherwise, if the parallel is set to FALSE and distributed is set to TRUE , the kmer frequencies are computed on each processor separately but then communicated between each other, and therefore at the end all processors have the same set of frequencies for kmers stored, using which they will generate frequency profiles for their chunk of sequences. If you prefer to run the operation in serial, set both parallel and distributed to FALSE .
Returns a list with one vector for each protein sequence in the dataset. A vector for sequence s contains |s| - klen + 1 indices if impute is set to FALSE (where |s| is the length of the sequence). Otherwise it will include one index for each position in the sequence but also winlen %% 2 indices at the beginning and end of each sequence.
generate_instances for generation of
training and test instances for each index in each sequence list()
comm.size for writing a distributed data object to a
single file
Armen Abnousi
list("\insertRef", list("abnousi2016fast"), list("naddaR"))
## Generate a set of three example protein sequences
seqs <- AAStringSet(c("seq1"="MLVVD",
"seq2"="PVVRA",
"seq3"="LVVR"))
## Count the kmers and generate a dataframe of the frequencies
profs <- generate_profiles(seqs, klen = 3, parallel = FALSE, winlen = 5, normalize = FALSE)
head(profs)
profs
##[[1]]
##[[1]]$freqs
##[1] 1.5 1.5 1.0 2.0 1.0 1.5 1.5 1.5 1.5
##[[1]]$seq
##[1] "seq1"
##
##[[2]]
##[[2]]$freqs
##[1] 1.5 1.5 1.0 2.0 1.0 1.5 1.5 1.5 1.5
##[[2]]$seq
##[[1]] "seq2"
##
##[[3]]
##[[3]]$freqs
##[1] 2 2 2 2 2 2 2 2
##[[3]]$seq
##[1] "seq3"