SIC {sidier}

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 = TRUE,
outnameDist=paste(inputFile,"IndelDistanceSIC.txt",
sep = "_"), outnameCode = paste(inputFile,
"SIC_coding.txt", sep = "_"), addExtremes = FALSE)

Arguments

inputFile

the name of the fasta file to be analysed. Alternatively you can provide the name of a "DNAbin" class alignment stored in memory using the "align" option.

align

the name of the alignment to be analysed. See "read.dna" in ape package for details about reading alignments. Alternatively you can provide the name of the file containing the alignment in fasta format using the "inputFile" option.

saveFile

a logical; if TRUE (default), it produces two output text files containing the distance matrix and the codified indel positions.

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, but see Details.

Details

It is recommended to estimate this distance matrix using only the unique sequences in 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).

Value

A list with two elements:

indel coding 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/absence between sequences.

Author(s)

A. J. Muñoz-Pajares

References

Simmons, M.P. & Ochoterena, H. (2000). Gaps as Characters in Sequence-Based Phylogenetic Analyses. Systematic Biology, 49, 369-381.

See Also

BARRIEL, MCIC, FIFTH

Examples

# # This will generate an example file in your working directory:
# 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 = "\n")
# library(ape)
# 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)