crossSetSim {protr}

Parallel Protein Sequence Similarity Calculation Between Two Sets Based on Sequence Alignment (In-Memory Version)

Description

Parallel calculation of protein sequence similarity based on sequence alignment between two sets of protein sequences.

Usage

crossSetSim(
  protlist1,
  protlist2,
  type = "local",
  cores = 2,
  batches = 1,
  verbose = FALSE,
  submat = "BLOSUM62",
  gap.opening = 10,
  gap.extension = 4
)

Arguments

protlist1

A length n list containing n protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

protlist2

A length n list containing m protein sequences, each component of the list is a character string, storing one protein sequence. Unknown sequences should be represented as "".

type

Type of alignment, default is "local", can be "global" or "local", where "global" represents Needleman-Wunsch global alignment; "local" represents Smith-Waterman local alignment.

cores

Integer. The number of CPU cores to use for parallel execution, default is 2. Users can use the availableCores() function in the parallelly package to see how many cores they could use.

batches

Integer. How many batches should we split the similarity computations into. This is useful when you have a large number of protein sequences, enough number of CPU cores, but not enough RAM to compute and fit all the similarities into a single batch. Defaults to 1.

verbose

Print the computation progress? Useful when batches > 1.

submat

Substitution matrix, default is "BLOSUM62", can be one of "BLOSUM45", "BLOSUM50", "BLOSUM62", "BLOSUM80", "BLOSUM100", "PAM30", "PAM40", "PAM70", "PAM120", or "PAM250".

gap.opening

The cost required to open a gap of any length in the alignment. Defaults to 10.

gap.extension

The cost to extend the length of an existing gap by 1. Defaults to 4.

Value

A n x m similarity matrix.

Author(s)

Sebastian Mueller <https://alva-genomics.com>

Examples

## Not run: 

# Be careful when testing this since it involves parallelization
# and might produce unpredictable results in some environments

library("Biostrings")
library("foreach")
library("doParallel")

s1 <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
s2 <- readFASTA(system.file("protseq/P08218.fasta", package = "protr"))[[1]]
s3 <- readFASTA(system.file("protseq/P10323.fasta", package = "protr"))[[1]]
s4 <- readFASTA(system.file("protseq/P20160.fasta", package = "protr"))[[1]]
s5 <- readFASTA(system.file("protseq/Q9NZP8.fasta", package = "protr"))[[1]]

plist1 <- list(s1 = s1, s2 = s2, s4 = s4)
plist2 <- list(s3 = s3, s4_again = s4, s5 = s5, s1_again = s1)
psimmat <- crossSetSim(plist1, plist2)
colnames(psimmat) <- names(plist1)
rownames(psimmat) <- names(plist2)
print(psimmat)
#                 s1         s2         s4
# s3       0.10236985 0.18858241 0.05819984
# s4_again 0.04921696 0.12124217 1.00000000
# s5       0.03943488 0.06391103 0.05714638
# s1_again 1.00000000 0.11825938 0.04921696

## End(Not run)