aphid

Analysis with profile hidden Markov models

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 HMMER software package. aphid also includes functions for developing and working with standard hidden Markov models.

This package was written based on the algorithms described in the book Biological Sequence Analysis by Richard Durbin, Sean Eddy, Anders Krogh and Graeme Mitchison. This book offers an in depth explanation of hidden Markov models and profile HMMs for users of all levels of familiarity. Many of the examples and datasets in the package are directly derived from the text, which serves as a useful primer for this package.

Installation

To download aphid from CRAN and load the package, run

install.packages("aphid")
library("aphid")

To download the development version from GitHub, first ensure a C/C++ compliler is available and the devtools R package is installed. Linux users will generally have a compiler installed by default; however Windows users may need to download Rtools and Mac OSX users will need Xcode (note that these are not R packages). Install and load the package by running

devtools::install_github("shaunpwilkinson/aphid", build_vignettes = TRUE) 
library("aphid")

Use and Examples

An overview of the package and its functions can be found by running

?aphid

To view the tutorial, run

vignette("aphid-vignette")

Issues

If you experience a problem using this package please feel free to raise it as an issue on GitHub.

Acknowledgements

This software was developed at Victoria University of Wellington with funding from a Rutherford Foundation Postdoctoral Research Fellowship award from the Royal Society of New Zealand.