TSRchitect: Promoter identification from diverse types of large-scale TSS profiling data

The TSRchitect repository encompasses an R package developed in the Brendel Group for analyses of transcription start site data. The code conforms to our RAMOSE philosophy: it generates reproducible, accurate, and meaningful results; it is open (source) and designed to be scalable and easy to use.

Quick Start

Input to TSRchitect will be transcription profiling read alignment data in bam or bed format as well as the appropriate genome annotation (if available). Output consists of predicted Transcription Start Sites (TSS) and Transcription Start Regions (TSR) as well as statistics summarizing the distribution and characteristics of identified TSSs and TSRs.

The simplest way to get going is to use the TSRchitect Singularity container, e.g. as follows:

git clone https://github.com/BrendelGroup/TSRchitect
cd TSRchitect/demo
wget https://BrendelGroup.org/SingularityHub/tsr.sif
alias rws="singularity exec -e -B{PWD}/..  tsr.sif"
rws R

In the above example, you clone this repository into your current directory, go into the TSRchitect/demo directory that has been created, download the TSRchitect apptainer, define the bash alias rws ("run with singularity"), and check that everything works by launching an R console from within the container.

Of course this assumes that you have Apptainer/Singularity installed on your system. Check whether there is package built for your system. Otherwise, follow the instructions to install Singularity from source code.

The advantage of this approach is that the TSRchitect library and all its prerequisites are available within the container, so that there is no further installation necessary on your part to follow our examples and run your own analyses. For example, in that console, you should see

R version 4.2.2 Patched (2022-11-10 r83330) -- "Innocent and Trusting"
...
> packageVersion("TSRchitect")
[1] '1.17.3'
> packageVersion("GenomicRanges")
[1] '1.50.2'
>

Realistic Start

Please find detailed installation instructions and options in the INSTALL document. Once all preparatory steps are taken care of, see the HOWTO document for examples of how to load data into TSRchitect and predict and characterize promoters.

FAQ and References

Please see V. Brendel's TSRchitect FAQ for usage examples and suggestions.

If you find TSRchitect useful, you may cite:

Raborn RT, Sridharan K, Brendel VP (2017) TSRchitect: Promoter identification from large-scale TSS profiling data. doi: 10.18129/B9.bioc.TSRchitect, https://doi.org/doi:10.18129/B9.bioc.TSRchitect.

Contact

Please direct all comments and suggestions to Volker Brendel at Indiana University and Taylor Raborn at his current address.