random.msa {Bios2cor}

Random Alignment

Description

Builds a multiple sequence alignment (MSA) of random sequences.

Usage

random.msa(nb.seq = 100, id = "SEQ", nb.pos = 100, gap = FALSE,
aa.strict = FALSE, align = NULL, align.replace = TRUE)

Arguments

nb.seq

a numeric value indicating the number of sequences in the random MSA. Default is 100.

id

a string of characters used to tag each sequence name. Default is "SEQ". An incremented number is attached to this tag to name each sequence.

nb.pos

a numeric value indicating the length of each sequence in the random MSA. Default is 100.

gap

a logical value indicating whether the gap character should be considered as a supplementary symbol (TRUE) or not (FALSE). Default is FALSE.

aa.strict

a logical value indicating whether only strict amino acids should be taken into account (TRUE) or not (FALSE). Default is FALSE.

align

an object of class 'align', obtained from import.fasta or import.msf function. If this parameter is not NULL, the composition of the output sequences is based on the composition of the input sequences. Default is NULL.

align.replace

a logical value indicating random drawing with replacement (TRUE) or without replacement (FALSE) of characters present in align. Default is FALSE.

Details

random.msa may be used to compare a reference MSA to a random MSA. The random MSA must have the same characteristics as the reference MSA (same number of sequences of same length).

A procedure can be applied to the random MSA to assess the amount of variance due to random mutations in the reference MSA.

The subset function is used for random selection of the amino acids. If a truly random procedure is needed, see random package.

Value

A named list whose objects correspond to random sequences.

Note

This function has been initially developped in the bios2mds R package (Julien PELE [aut], Marie CHABBERT [cre]).

Author(s)

Julien PELE

References

For an application of random MSA see :

Pele J, Abdi H, Moreau M, Thybert D and Chabbert M (2011) Multidimensional scaling reveals the main evolutionary pathways of class A G-protein-coupled receptors. PLoS ONE 6: e19094. doi:10.1371.

See Also

permutation and synsequence functions from seqinr package.

Examples

  #Importing MSA file
  aln <- import.fasta(system.file("msa/toy2_align.fa", package = "Bios2cor"))

  #Generating a random sequence alignment with the same characterics as input file
  nb.seq <- length(aln)
  nb.pos <- length(aln[[1]])
  aln.random <- random.msa(nb.seq = nb.seq, nb.pos = nb.pos)