recipe bioconductor-massspecwavelet

Peak Detection for Mass Spectrometry data using wavelet-based algorithms



LGPL (>= 2)




biotools: massspecwavelet

Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See <doi:10.1093/bioinformatics/btl355} for further details.

package bioconductor-massspecwavelet

(downloads) docker_bioconductor-massspecwavelet



depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-massspecwavelet

and update with::

   mamba update bioconductor-massspecwavelet

To create a new environment, run:

mamba create --name myenvname bioconductor-massspecwavelet

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-massspecwavelet/tags`_ for valid values for ``<tag>``)

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