K-mer counting and clustering for biological sequence analysis
kmer is an R package for rapidly computing distance matrices and
clustering large sequence datasets using fast alignment-free k-mer counting and
recursive k-means partitioning.
Installation
To download kmer from CRAN and load the package, run
install.packages("kmer")
library("kmer")
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).
To download and install devtools, run
install.packages("devtools")
The kmer package can then be installed and loaded by running
If you experience a problem using this package please feel free to
raise it as an issue on GitHub.
Any feedback is appreciated.
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.
kmer
K-mer counting and clustering for biological sequence analysis
kmeris an R package for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning.Installation
To download
kmerfrom CRAN and load the package, runTo 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). To download and install devtools, run
The
kmerpackage can then be installed and loaded by runningHelp
An overview of the package and its functions can be found by running
To view the tutorial, you can either run
or access it directly from CRAN.
If you experience a problem using this package please feel free to raise it as an issue on GitHub. Any feedback is appreciated.
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.