# Parallel Protein Sequence Similarity Calculation Between Two Sets Based on Sequence Alignment (In-Memory Version) Parallel calculation of protein sequence similarity based on sequence alignment between two sets of protein sequences. ```r 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. ## Returns A `n` x `m` similarity matrix. ## Examples ```r ## 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) ``` ## Author(s) Sebastian Mueller <[https://alva-genomics.com](https://alva-genomics.com)>

crossSetSim function