BCalm and analyze your MPRA data

BCalm is a package that provides a modification from the mpralm package, an R package that provides tools for differential analysis in MPRA studies. BCalm allows users to use individual barcodes as model input. See the vignette for examples on how to run BCalm.

BCalm requires R >=3.5, <= 4.4.0 and can be installed using devtools or remotes.

Installation guide:

Using conda

We suggest using conda as a package management tool. Its installation guide can be found here.

BCalm is available from GitHub. Here we give installation instructions using either devtools or remotes.

Remotes:

conda create -n BCalm_env r-base=4.4.0 r-remotes

Devtools:

conda create -n BCalm_env r-base=4.4.0 r-devtools

After activating the environment (conda activate BCalm_env) you can start the R terminal and install BCalm using either devtools or remotes, by loading the chosen package and running:

install_github("kircherlab/BCalm")

After installation, you can start using BCalm (after loading it with library(BCalm)). For a more extensive user guide, please see the vignette (installation described below).

Vignette

In the following code snippets, we build the vignette and install all necessary packages for the vignette.

Remotes:

remotes::install_github("kircherlab/BCalm", build_vignette=TRUE, dependencies=TRUE)

Devtools:

devtools::install_github("kircherlab/BCalm", build_vignette=TRUE, dependencies=TRUE)

After this you can open the built vignette by vignette('BCalm') (Alternatively, we provide a pre-built vignette in vignettes/BCalm.html)

You can either follow the prepared vignette directly in the editor of your choice (vignettes/BCalm.Rmd) or scroll through it by opening it in your browser (vignettes/BCalm.html).

How to cite:

The BCalm package is still unpublished, citing details will be provided later. When using BCalm, please cite the mpra package (Myint et al. 2019) and the limma-voom framework (Law et al. 2014).

References

Myint, Leslie, Dimitrios G. Avramopoulos, Loyal A. Goff, and Kasper D. Hansen. Linear models enable powerful differential activity analysis in massively parallel reporter assays. BMC Genomics 2019, 209. doi: 10.1186/s12864-019-5556-x.

Law, Charity W., Yunshun Chen, Wei Shi, and Gordon K. Smyth. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 2014, 15: R29. doi: 10.1186/gb-2014-15-2-r29