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R/aphid.R
In aphid: Analysis with Profile Hidden Markov Models
#' The \pkg{aphid} package for analysis with profile hidden Markov models.
#'
#' \pkg{aphid} is an R package for the development and application of
#' hidden Markov models and profile HMMs for biological sequence analysis.
#' Functions are included for multiple and pairwise sequence alignment,
#' model construction and parameter optimization, calculation of conditional
#' probabilities (using the forward, backward and Viterbi algorithms),
#' tree-based sequence weighting, sequence simulation, and file import/export
#' compatible with the \href{http://www.hmmer.org}{HMMER} software package.
#' The package has a wide variety of uses including database searching,
#' gene-finding and annotation, phylogenetic analysis and sequence classification.
#'
#' @details
#' The \pkg{aphid} package is based on the algorithms outlined in the book 'Biological
#' sequence analysis: probabilistic models of proteins and nucleic acids' by Richard Durbin,
#' Sean Eddy, Anders Krogh and Graeme Mitchison. This book is highly recommended
#' for those wishing to develop a better understanding of HMMs and PHMMs, regardless of
#' prior experience. Many of the examples in the function help pages are taken directly
#' from the book, so that readers can learn to use the package as they work through the
#' chapters.
#'
#' There are also excellent rescources available for those wishing to use profile hidden
#' Markov models outside of the R environment. The \pkg{aphid} package maintains
#' compatibility with the \href{http://www.hmmer.org}{HMMER} software suite
#' through the file input and output functions \code{\link{readPHMM}} and
#' \code{\link{writePHMM}}.
#'
#' The \pkg{aphid} package is designed to work in conjunction with the "DNAbin"
#' and "AAbin" object types produced by the \code{\link[ape]{ape}} package
#' (Paradis et al 2004, 2012). This is an essential piece of software for those
#' using R for biological sequence analysis, and provides a binary coding format
#' for nucleotides and amino acids that maximizes memory and speed efficiency.
#' While \pkg{aphid} also works with standard character vectors and matrices,
#' it may not recognize the DNA and amino acid amibguity codes and therefore is not
#' guaranteed to treat them appropriately.
#'
#' To maximize speed, the low-level dynamic programming functions such
#' as \code{\link{Viterbi}}, \code{\link{forward}} and \code{\link{backward}}
#' are written in C++ with the help of the \code{\link[Rcpp]{Rcpp}}
#' package (Eddelbuettel & Francois 2011).
#' Note that R versions of these functions are also maintained
#' for the purposes of debugging, experimentation and code interpretation.
#'
#' @section Classes:
#' The \pkg{aphid} package creates two primary object classes, \code{"HMM"}
#' (hidden Markov models) and \code{"PHMM"} (profile hidden Markov models)
#' with the functions \code{\link{deriveHMM}} and \code{\link{derivePHMM}}, respectively.
#' These objects are lists consisting of emission and transition probability matrices
#' (denoted E and A), vectors of non-position-specific background emission and transition
#' probabilies (denoted qe and qa) and other model metadata.
#' Objects of class \code{"DPA"} (dynammic programming array) are also generated
#' by the Viterbi and forward/backward functions.
#' These are primarily created for succinct console printing.
#'
#' @section Functions:
#' A breif description of the primary \pkg{aphid} functions are provided with links
#' to their help pages below.
#'
#' @section File import and export:
#' \itemize{
#' \item \code{\link{readPHMM}} parses a \href{http://www.hmmer.org}{HMMER} text file
#' into R and creates an object of class \code{"PHMM"}
#' \item \code{\link{writePHMM}} writes a \code{"PHMM"} object to a text file in
#' \href{http://www.hmmer.org}{HMMER} v3 format
#' }
#'
#' @section Visualization:
#' \itemize{
#' \item \code{\link{plot.HMM}} plots a \code{"PHMM"} object as a cyclic directed graph
#' \item \code{\link{plot.PHMM}} plots a \code{"PHMM"} object as a directed graph with
#' sequential modules consisting of match, insert and delete states
#' }
#'
#' @section Model building and training:
#' \itemize{
#' \item \code{\link{deriveHMM}} builds a \code{"HMM"} object from a list of training
#' sequences
#' \item \code{\link{derivePHMM}} builds a \code{"PHMM"} object from a multiple sequence
#' alignment or a list of non-aligned sequences
#' \item \code{\link{map}} optimizes profile hidden Markov model construction
#' using the maximum \emph{a posteriori} algorithm
#' \item \code{\link{train}} optimizes the parameters of a \code{"HMM"} or
#' \code{"PHMM"} object using a list of training sequences
#' }
#'
#' @section Sequence alignment and weighting:
#' \itemize{
#' \item \code{\link{align}} performs a multiple sequence alignment
#' \item \code{\link{weight}} assigns weights to sequences
#' }
#'
#' @section Conditional probabilities:
#' \itemize{
#' \item \code{\link{Viterbi}} finds the optimal path of a sequence through a HMM
#' or PHMM, and returns its log odds or probability given the model
#' \item \code{\link{forward}} finds the full probability of a sequence
#' given a HMM or PHMM using the forward algorithm
#' \item \code{\link{backward}} finds the full probability of a sequence
#' given a HMM or PHMM using the backward algorithm
#' \item \code{\link{posterior}} finds the position-specific posterior probability
#' of a sequence given a HMM or PHMM
#' }
#'
#' @section Sequence simulation:
#' \itemize{
#' \item \code{\link{generate.HMM}} simulates a random sequence from an HMM
#' \item \code{\link{generate.PHMM}} simulates a random sequence from a PHMM
#' }
#'
#' @section Datasets:
#' \itemize{
#' \item \code{\link{substitution}} a collection of DNA and amino acid
#' substitution matrices from \href{ftp://ftp.ncbi.nih.gov/blast/matrices/}{NCBI}
#' including the PAM, BLOSUM, GONNET, DAYHOFF and NUC matrices
#' \item \code{\link{casino}} data from the dishonest casino example of
#' Durbin et al (1998) chapter 3.2
#' \item \code{\link{globins}} Small globin alignment data from
#' Durbin et al (1998) Figure 5.3
#' }
#'
#' @author Shaun Wilkinson
#'
#' @references
#' Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological
#' sequence analysis: probabilistic models of proteins and nucleic acids.
#' Cambridge University Press, Cambridge, United Kingdom.
#'
#' Eddelbuettel D, Francois R (2011) Rcpp: seamless R and C++ integration.
#' \emph{Journal of Statistical Software} \strong{40}, 1-18.
#'
#' Finn RD, Clements J & Eddy SR (2011) HMMER web server: interactive sequence
#' similarity searching.
#' \emph{Nucleic Acids Research}. \strong{39}, W29-W37. \url{http://hmmer.org/}.
#'
#' HMMER: biosequence analysis using profile hidden Markov models.
#' \url{http://www.hmmer.org}.
#'
#' NCBI index of substitution matrices.
#' \url{ftp://ftp.ncbi.nih.gov/blast/matrices/}.
#'
#' Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics
#' and evolution in R language. \emph{Bioinformatics} \strong{20}, 289-290.
#'
#' Paradis E (2012) Analysis of Phylogenetics and Evolution with R
#' (Second Edition). Springer, New York.
#'
#' @docType package
#' @name aphid
################################################################################
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#' The \pkg{aphid} package for analysis with profile hidden Markov models.
#'
#' \pkg{aphid} is an R package for the development and application of
#' hidden Markov models and profile HMMs for biological sequence analysis.
#' Functions are included for multiple and pairwise sequence alignment,
#' model construction and parameter optimization, calculation of conditional
#' probabilities (using the forward, backward and Viterbi algorithms),
#' tree-based sequence weighting, sequence simulation, and file import/export
#' compatible with the \href{http://www.hmmer.org}{HMMER} software package.
#' The package has a wide variety of uses including database searching,
#' gene-finding and annotation, phylogenetic analysis and sequence classification.
#'
#' @details
#' The \pkg{aphid} package is based on the algorithms outlined in the book 'Biological
#' sequence analysis: probabilistic models of proteins and nucleic acids' by Richard Durbin,
#' Sean Eddy, Anders Krogh and Graeme Mitchison. This book is highly recommended
#' for those wishing to develop a better understanding of HMMs and PHMMs, regardless of
#' prior experience. Many of the examples in the function help pages are taken directly
#' from the book, so that readers can learn to use the package as they work through the
#' chapters.
#'
#' There are also excellent rescources available for those wishing to use profile hidden
#' Markov models outside of the R environment. The \pkg{aphid} package maintains
#' compatibility with the \href{http://www.hmmer.org}{HMMER} software suite
#' through the file input and output functions \code{\link{readPHMM}} and
#' \code{\link{writePHMM}}.
#'
#' The \pkg{aphid} package is designed to work in conjunction with the "DNAbin"
#' and "AAbin" object types produced by the \code{\link[ape]{ape}} package
#' (Paradis et al 2004, 2012). This is an essential piece of software for those
#' using R for biological sequence analysis, and provides a binary coding format
#' for nucleotides and amino acids that maximizes memory and speed efficiency.
#' While \pkg{aphid} also works with standard character vectors and matrices,
#' it may not recognize the DNA and amino acid amibguity codes and therefore is not
#' guaranteed to treat them appropriately.
#'
#' To maximize speed, the low-level dynamic programming functions such
#' as \code{\link{Viterbi}}, \code{\link{forward}} and \code{\link{backward}}
#' are written in C++ with the help of the \code{\link[Rcpp]{Rcpp}}
#' package (Eddelbuettel & Francois 2011).
#' Note that R versions of these functions are also maintained
#' for the purposes of debugging, experimentation and code interpretation.
#'
#' @section Classes:
#' The \pkg{aphid} package creates two primary object classes, \code{"HMM"}
#' (hidden Markov models) and \code{"PHMM"} (profile hidden Markov models)
#' with the functions \code{\link{deriveHMM}} and \code{\link{derivePHMM}}, respectively.
#' These objects are lists consisting of emission and transition probability matrices
#' (denoted E and A), vectors of non-position-specific background emission and transition
#' probabilies (denoted qe and qa) and other model metadata.
#' Objects of class \code{"DPA"} (dynammic programming array) are also generated
#' by the Viterbi and forward/backward functions.
#' These are primarily created for succinct console printing.
#'
#' @section Functions:
#' A breif description of the primary \pkg{aphid} functions are provided with links
#' to their help pages below.
#'
#' @section File import and export:
#' \itemize{
#' \item \code{\link{readPHMM}} parses a \href{http://www.hmmer.org}{HMMER} text file
#' into R and creates an object of class \code{"PHMM"}
#' \item \code{\link{writePHMM}} writes a \code{"PHMM"} object to a text file in
#' \href{http://www.hmmer.org}{HMMER} v3 format
#' }
#'
#' @section Visualization:
#' \itemize{
#' \item \code{\link{plot.HMM}} plots a \code{"PHMM"} object as a cyclic directed graph
#' \item \code{\link{plot.PHMM}} plots a \code{"PHMM"} object as a directed graph with
#' sequential modules consisting of match, insert and delete states
#' }
#'
#' @section Model building and training:
#' \itemize{
#' \item \code{\link{deriveHMM}} builds a \code{"HMM"} object from a list of training
#' sequences
#' \item \code{\link{derivePHMM}} builds a \code{"PHMM"} object from a multiple sequence
#' alignment or a list of non-aligned sequences
#' \item \code{\link{map}} optimizes profile hidden Markov model construction
#' using the maximum \emph{a posteriori} algorithm
#' \item \code{\link{train}} optimizes the parameters of a \code{"HMM"} or
#' \code{"PHMM"} object using a list of training sequences
#' }
#'
#' @section Sequence alignment and weighting:
#' \itemize{
#' \item \code{\link{align}} performs a multiple sequence alignment
#' \item \code{\link{weight}} assigns weights to sequences
#' }
#'
#' @section Conditional probabilities:
#' \itemize{
#' \item \code{\link{Viterbi}} finds the optimal path of a sequence through a HMM
#' or PHMM, and returns its log odds or probability given the model
#' \item \code{\link{forward}} finds the full probability of a sequence
#' given a HMM or PHMM using the forward algorithm
#' \item \code{\link{backward}} finds the full probability of a sequence
#' given a HMM or PHMM using the backward algorithm
#' \item \code{\link{posterior}} finds the position-specific posterior probability
#' of a sequence given a HMM or PHMM
#' }
#'
#' @section Sequence simulation:
#' \itemize{
#' \item \code{\link{generate.HMM}} simulates a random sequence from an HMM
#' \item \code{\link{generate.PHMM}} simulates a random sequence from a PHMM
#' }
#'
#' @section Datasets:
#' \itemize{
#' \item \code{\link{substitution}} a collection of DNA and amino acid
#' substitution matrices from \href{ftp://ftp.ncbi.nih.gov/blast/matrices/}{NCBI}
#' including the PAM, BLOSUM, GONNET, DAYHOFF and NUC matrices
#' \item \code{\link{casino}} data from the dishonest casino example of
#' Durbin et al (1998) chapter 3.2
#' \item \code{\link{globins}} Small globin alignment data from
#' Durbin et al (1998) Figure 5.3
#' }
#'
#' @author Shaun Wilkinson
#'
#' @references
#' Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological
#' sequence analysis: probabilistic models of proteins and nucleic acids.
#' Cambridge University Press, Cambridge, United Kingdom.
#'
#' Eddelbuettel D, Francois R (2011) Rcpp: seamless R and C++ integration.
#' \emph{Journal of Statistical Software} \strong{40}, 1-18.
#'
#' Finn RD, Clements J & Eddy SR (2011) HMMER web server: interactive sequence
#' similarity searching.
#' \emph{Nucleic Acids Research}. \strong{39}, W29-W37. \url{http://hmmer.org/}.
#'
#' HMMER: biosequence analysis using profile hidden Markov models.
#' \url{http://www.hmmer.org}.
#'
#' NCBI index of substitution matrices.
#' \url{ftp://ftp.ncbi.nih.gov/blast/matrices/}.
#'
#' Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics
#' and evolution in R language. \emph{Bioinformatics} \strong{20}, 289-290.
#'
#' Paradis E (2012) Analysis of Phylogenetics and Evolution with R
#' (Second Edition). Springer, New York.
#'
#' @docType package
#' @name aphid
################################################################################
NULL
Try the aphid package in your browser
Any scripts or data that you put into this service are public.
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We want your feedback!
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