Package Recipe 'bioconductor-wavcluster'

recipe bioconductor-wavcluster

Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/wavClusteR.html
License:: GPL-2
Recipe:: /bioconductor-wavcluster/meta.yaml
Links:: biotools: wavcluster

The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).

package bioconductor-wavcluster¶

Versions:

2.44.0-0, 2.40.0-0, 2.36.0-0, 2.34.0-0, 2.32.0-0, 2.28.0-0, 2.26.0-0, 2.24.0-1, 2.24.0-0,

2.44.0-0, 2.40.0-0, 2.36.0-0, 2.34.0-0, 2.32.0-0, 2.28.0-0, 2.26.0-0, 2.24.0-1, 2.24.0-0, 2.22.0-0, 2.20.0-0, 2.18.0-1, 2.16.0-0, 2.14.0-0, 2.11.0-0

Depends:

on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-biostrings >=2.78.0,<2.79.0
on bioconductor-genomicfeatures >=1.62.0,<1.63.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-rsamtools >=2.26.0,<2.27.0
on bioconductor-rtracklayer >=1.70.0,<1.71.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on r-base >=4.5,<4.6.0a0
on r-foreach
on r-ggplot2
on r-hmisc
on r-mclust
on r-seqinr
on r-stringr

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-wavcluster

to add into an existing workspace instead, run:

pixi add bioconductor-wavcluster

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-wavcluster

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-wavcluster

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-wavcluster:<tag>

(see bioconductor-wavcluster/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-wavcluster/README.html)