Type: Package
Title: Predict Presence of Signal Peptides
Version: 1.5
LazyData: true
Date: 2018-11-11
Description: Predicts the presence of signal peptides in eukaryotic protein using hidden semi-Markov models. The implemented algorithm can be accessed from both the command line and GUI.
License: GPL-3
URL: https://github.com/michbur/signalhsmm
Depends: R (≥ 3.0.0)
Imports: graphics, seqinr, shiny, stats, utils
LinkingTo: Rcpp
Suggests: DT, rmarkdown, shinythemes
NeedsCompilation: yes
Repository: CRAN
Encoding: UTF-8
RoxygenNote: 6.1.0
Packaged: 2018-11-15 08:53:01 UTC; michal
Author: Michal Burdukiewicz ORCID iD [cre, aut], Piotr Sobczyk [aut], Jaroslaw Chilimoniuk ORCID iD [ctb]
Maintainer: Michal Burdukiewicz <michalburdukiewicz@gmail.com>
Date/Publication: 2018-11-15 09:10:03 UTC

signalHsmm - prediction of signal peptides

Description

Using hidden semi-Markov models as a probabilistic framework, signalHsmm is new, highly accurate signal peptide predictor for eukaryotic proteins.

Details

Secretory signal peptides are short (20-30 residues) N-terminal amino acid sequences tagging among others tag among others hormons, immune system proteins, structural proteins, and metabolic enzymes. They direct a protein to the endomembrane system and next to the extracellular localization. All signal peptides possess three distinct domains with variable length and characteristic amino acid composition. Despite their variability, signal peptides are universal enough to direct properly proteins in different secretory systems. For example, artifically introduced bacterial signal peptides can guide proteins in mammals and plants.

The development of signalHsmm was funded by National Science Center (2015/17/N/NZ2/01845).

Examples

few_predictions <- run_signalHsmm(benchmark_dat[1:3])
#see all predictions
pred2df(few_predictions)
#summary one prediction
summary(few_predictions[[1]])
#plot one prediction
plot(few_predictions[[1]])

#have fun with GUI
## Not run: 
gui_signalHsmm()

## End(Not run)

Reduced amino acid alphabet

Description

Amino acids are grouped together in larger sets based on their physicochemical properties important in the recognition of signal peptide.

Usage

aaaggregation

Format

a list of length four. Each element contains a character vector of amino acid names (one-letter abbreviations).

Adds k-mer hidden state to signalHsmm model

Description

Changes parameters for Hidden Semi-Markov Model to add k-mer

Usage

add_k_mer_state(kMer, pipar, tpmpar, od, params, pState, nState, pTrans, d)

Arguments

kMer

character vector representing k-mer aminoacid sequence.

pipar

Probabilities of initial state in Markov Model.

tpmpar

Matrix with transition probabilities between states.

od

Matrix of response probabilities. Eg. od[1,2] is a probability of signal 2 in state 1.

params

Matrix of probability distribution for duration. Eg. params[10,2] is probability of duration of time 10 in state 2.

pState

number denoting hidden state right before k-mer.

nState

number denoting hidden state right after k-mer.

pTrans

Probability of change from pState to k-mer hidden state.

d

Duration of the state.

Value

A list of length four:

Note

Currently add only k-mers without distance.

Benchmark data set for signalHsmm

Description

Lists eukaryotic proteins added to UniProt database release 2015_06 between 1.01.2010 and 1.06.2015 (140 proteins with signal peptide and 280 randomly sampled proteins without signal peptide).

Usage

benchmark_dat

Format

a list of SeqFastaAA objects. Slot sig contains the range of signal peptide (if any).

Source

UniProt

Examples

summary(benchmark_dat)

Compute most probable path with extended Viterbi algorithm.

Description

Viterbi algorithm for Hidden Markov Model with duration

Usage

duration_viterbi(aa_sample, pipar, tpmpar, od, params)

Arguments

aa_sample

character vector representing single aminoacid sequence.

pipar

probabilities of initial state in Markov Model.

tpmpar

matrix of transition probabilities between states.

od

matrix of response probabilities. Eg. od[1,2] is a probability of signal 2 in state 1.

params

matrix of probability distribution for duration. Eg. params[10,2] is probability of duration of time 10 in state 2.

Value

A list of length four:

Note

All computations are on logarithms of probabilities.

Localize n-, h- and c-region in signal peptide

Description

Finds borders between distinct regions constituting signal peptides using a heuristic algorithm.

Usage

find_nhc(protein, signal = NULL)

Arguments

protein

a vector of amino acids or object of class SeqFastaAA.

signal

range of signal peptide. If NULL, the attribute sig of protein will be used.

Value

a vector of length 4 containing positions of:

  1. start of n-region,

  2. start of h-region,

  3. start of c-region,

  4. cleavage site.

References

Henrik Nielsen, Anders Krogh (1998). Prediction of signal peptides and signal anchors by a hidden Markov model. Proc. Sixth Int. Conf. on Intelligent Systems for Molecular Biology.

GUI for signalHsmm

Description

A graphical user interface for predicting presence of signal peptides.

Usage

gui_signalHsmm()

Value

null.

Note

Any ad-blocking software may be cause of malfunctions.

See Also

run_signalHsmm

hsmm_pred class

Description

A single prediction of signalHsmm.

A stochastic model of signal peptide produced by signalHsmm.

Details

Always a named list of five elements

  1. sp_probability is a probability of signal peptide presence.

  2. sp_start is a start of potential signal peptide (naively 1 aminoacid).

  3. sp_end is a position of last amino acid of signal peptide.

  4. struc is numeric vector representing predicted structure of input protein.

  5. prot is character vector containing input sequence of amino acids.

  6. str_approx has value bigger than 0 if the predicted signal peptide structure was approximated (usually in case of sequences that have no signal peptides).

Always a named list of five elements

  1. aa_group encoding of amino acids. See aaaggregation for an example.

  2. pipar probabilities of initial state in Markov Model.

  3. tpmpar matrix of transition probabilities between states.

  4. od matrix of response probabilities. Eg. od[1,2] is a probability of signal 2 in state 1.

  5. overall_probs_log probabilities of amino acids in mature protein.

  6. params matrix of probability distribution for duration. Eg. params[10,2] is probability of duration of time 10 in state 2.

See Also

summary.hsmm_pred plot.hsmm_pred

train_hsmm predict.sighsmm_model

hsmm_pred_list class

Description

A list of prediction(s) generated by run_signalHsmm function.

Details

A named list. Each element belongs to the hsmm_pred class.

See Also

summary.hsmm_pred_list, pred2df

Protein test

Description

Checks if an object is a protein (contains letters from one-letter amino acid code).

Usage

is_protein(object)

Arguments

object

character vector where each elemenents represent one amino acid.

Value

TRUE or FALSE.

Plot single signalHsmm prediction

Description

Plots objects of class hsmm_pred.

Usage

## S3 method for class 'hsmm_pred'
plot(x, add_legend = TRUE, only_sure = TRUE, ...)

Arguments

x

object of class hsmm_pred.

add_legend

logical, if TRUE, legend is added to the plot.

only_sure

logical, if FALSE does not draw signal peptide structure when probability is smaller than 0.5.

...

ignored.

Value

Nothing.

Convert list of signalHsmm predictions

Description

Converts objects of class hsmm_pred_list to data frame.

Usage

pred2df(object)

Arguments

object

of class hsmm_pred_list.

Value

Data frame which columns contain respectively the probability of signal peptide presence as well as the start and the end of predicted signal peptide.

Predict sighsmm_model object

Description

Predicts the presence of signal peptides using signalHsmm models.

Usage

## S3 method for class 'sighsmm_model'
predict(object, newdata, ...)

Arguments

object

sighsmm_model object.

newdata

unknown sequence of class character or character. Alternatively, a list of sequences in mentioned formats.

...

further arguments passed to or from other methods.

Examples

#remember to remove it
## Not run: 
pos_train_ultrahard <- read_uniprot("pos_ultrahard_data.txt", euk = TRUE)
model1 <- train_hsmm(pos_train_ultrahard, aa_group = aaaggregation)
predict(model1, benchmark_dat[1L:5])

## End(Not run)

Read sequences from .txt file

Description

Read sequence data saved in text file.

Usage

read_txt(connection)

Arguments

connection

a connection to the text (.txt) file.

Details

The input file should contain one or more amino acid sequences separated by empty line(s).

Value

a list of sequences. Each element has class SeqFastaAA. If connection contains no characters, function prompts warning and returns NULL.

Read data from UniProt database

Description

Read data saved in UniProt original flat text format.

Usage

read_uniprot(connection, ft_names, kwds = NULL)

Arguments

connection

a connection to UniProt data in text format.

ft_names

a character vector of UuniProt features to be extracted, for example "signal", "transit", "propep". The case is not matched.

kwds

a NULL or character vector of keywords (not UniProt keywords, but words of interest, that may occur in the protein description).

Value

a list of sequences. Each element has a class SeqFastaAA. Attributes OS and OC represents respectively OS and OC fields in the protein description. A value of each feature is preserved as an attribute named after the feature.

Predict presence of signal peptide in protein

Description

Using the hidden semi-Markov model predict presence of signal peptide in eukaryotic proteins.

Usage

run_signalHsmm(test_data)

Arguments

test_data

single protein sequence (character vector) or list of sequences. It may be an object of class SeqFastaAA.

Details

Function signalHsmm returns respectively probability of presence of signal peptide, start of signal peptide and the probable cleavage site localization. If input consists of more than one sequence, result is a data.frame where each column contains above values for different proteins.

Value

An object of class hsmm_pred_list.

Note

Currently start of signal peptide is naively set as 1 amino acid. The prediction of a cleavage site is still an experimental feature, use on your own risk.

See Also

hsmm_pred_list hsmm_pred

Examples

#run signalHsmm on one sequence
x1 <- run_signalHsmm(benchmark_dat[[1]])

#run signalHsmm on one sequence, but input is a character vector
x2 <- run_signalHsmm(c("M", "A", "G", "K", "E", "V", "I", "F", "I", "M", "A", "L", 
"F", "I", "A", "V", "E", "S", "S", "P", "I", "F", "S", "F", "D", 
"D", "L", "V", "C", "P", "S", "V", "T", "S", "L", "R", "V", "N", 
"V", "E", "K", "N", "E", "C", "S", "T", "K", "K", "D", "C", "G", 
"R", "N", "L", "C", "C", "E", "N", "Q", "N", "K", "I", "N", "V", 
"C", "V", "G", "G", "I", "M", "P", "L", "P", "K", "P", "N", "L", 
"D", "V", "N", "N", "I", "G", "G", "A", "V", "S", "E", "S", "V", 
"K", "Q", "K", "R", "E", "T", "A", "E", "S", "L"))

#run signalHsmm on list of sequences
x3 <- run_signalHsmm(benchmark_dat[1:3])
#see summary of results
summary(x3)
#print results as data frame
pred2df(x3)
#summary one result
summary(x3[[1]])
plot(x3[[1]])

Summarize single signalHsmm prediction

Description

Summarizes objects of class hsmm_pred.

Usage

## S3 method for class 'hsmm_pred'
summary(object, only_sure = TRUE,
  double_linebreak = FALSE, ...)

Arguments

object

of class hsmm_pred.

only_sure

logical, if FALSE does not draw signal peptide structure when probability is smaller than 0.5.

double_linebreak

logical, if TRUE adds aestically pleasing (in .Rmd) double linebreaks between the elements of summary.

...

ignored

Value

Nothing.

Summarize list of signalHsmm predictions

Description

Summarizes objects of class hsmm_pred_list.

Usage

## S3 method for class 'hsmm_pred_list'
summary(object, ...)

Arguments

object

of class hsmm_pred_list.

...

ignored

Value

nothing.

Train sighsmm_model object

Description

Train sighsmm_model object

Usage

train_hsmm(train_data, aa_group, max_length = 32,
  region_fun = find_nhc)

Arguments

train_data

training data.

aa_group

method of aggregating amino acids.

max_length

maximum length of signal peptide.

region_fun

function defining borders of regions (see find_nhc).

Value

object of class sighsmm_model.