- recipe bioconductor-rnadecay
Maximum Likelihood Decay Modeling of RNA Degradation Data
- Homepage:
https://bioconductor.org/packages/3.20/bioc/html/RNAdecay.html
- License:
GPL-2
- Recipe:
RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.
- package bioconductor-rnadecay¶
- Versions:
1.30.0-0,1.26.0-0,1.19.0-0,1.18.0-2,1.18.0-1,1.18.0-0,1.14.0-2,1.14.0-1,1.14.0-0,1.30.0-0,1.26.0-0,1.19.0-0,1.18.0-2,1.18.0-1,1.18.0-0,1.14.0-2,1.14.0-1,1.14.0-0,1.12.0-0,1.10.0-1,1.10.0-0,1.8.0-0,1.6.0-0,1.4.0-1,1.4.0-0,1.2.1-0,1.2.0-0- Depends:
on libblas
>=3.9.0,<4.0a0on libgcc
>=14on liblapack
>=3.9.0,<4.0a0on liblzma
>=5.8.2,<6.0a0on libstdcxx
>=14on libzlib
>=1.3.1,<2.0a0on r-base
>=4.5,<4.6.0a0on r-ggplot2
on r-gplots
on r-nloptr
on r-scales
on r-tmb
- 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-rnadecay
to add into an existing workspace instead, run:
pixi add bioconductor-rnadecay
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-rnadecay
Alternatively, to install into a new environment, run:
conda create -n envname bioconductor-rnadecay
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-rnadecay:<tag>
(see bioconductor-rnadecay/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:
[](http://bioconda.github.io/recipes/bioconductor-rnadecay/README.html)
