sidier (version 2.2)

SIC: Indel distances following the Simple Index Coding method

Description

This function codifies gapped positions in a sequence alignment following the rationale of the method described by Simmons and Ochoterrena (2000). Based on the yielded indel coding matrix, this function also computes a pairwise indel distance matrix.

Usage

SIC(inputFile = NA, align = NA, saveFile = T,
outnameDist=paste(inputFile,"IndelDistanceSIC.txt",
sep = "_"), outnameCode = paste(inputFile,
"SIC_coding.txt", sep = "_"), addExtremes = F)

Arguments

inputFile

the name of the fasta file to be analysed.

align

the name of the alignment to be analysed. See "read.dna" in ape package for details about reading alignments.

saveFile

a logical; if TRUE (default), function output is saved as a text file.

outnameDist

if "saveFile" is set to TRUE (default), contains the name of the distance output file.

outnameCode

if "saveFile" is set to TRUE (default), contains the name of the indel coding output file.

addExtremes

a logical; if TRUE, additional nucleotide sites are included in both extremes of the alignment. This will allow estimating distances for alignments showing gaps in terminal positions.

Value

• A list with two elements:
• indel conding matrix
  Describes the initial and final site of each gap and its presence or absence per sequence.
• distance matrix
  Contains genetic distances based on comparing indel presence/ausence between sequences.

Details

It is recommended to estimate this distance matrix after removing repeated sequences from the alignment. Repeated sequences increase computation time but do not provide additional information (because they produce duplicated rows and columns in the final distance matrix).

References

Simmons, M., Ochoterena, H. & Carr, T. (2001). Incorporation, relative homoplasy, and effect of gap characters in sequence-based phylogenetic analyses. Systematic Biology, 50, 454-62.

See Also

BARRIEL, MCIC, FIFTH

Examples

cat(">Population1_sequence1",
"A-AGGGTC-CT---G",
">Population1_sequence2",
"TAA---TCGCT---G",
">Population1_sequence3",
"TAAGGGTCGCT---G",
">Population1_sequence4",
"TAA---TCGCT---G",
">Population2_sequence1",
"TTACGGTCG---TTG",
">Population2_sequence2",
"TAA---TCG---TTG",
">Population2_sequence3",
"TAA---TCGCTATTG",
">Population2_sequence4",
"TTACGGTCG---TTG",
">Population3_sequence1",
"TTA---TCG---TAG",
">Population3_sequence2",
"TTA---TCG---TAG",
">Population3_sequence3",
"TTA---TCG---TAG",
">Population3_sequence4",
"TTA---TCG---TAG",
     file = "ex3.fas", sep = "")

SIC (align=read.dna("ex3.fas",format="fasta"), saveFile = FALSE)

# Analysing the same dataset, but using only unique sequences:
uni<-GetHaplo(inputFile="ex3.fas",saveFile=FALSE)
SIC (align=uni, saveFile = FALSE)