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.
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.
All the TSRchitect dependencies are encapsulated in a Singularity container available from Singularity Hub. Thus, once you know what you are doing, execution could be as simple as
singularity pull --name tsr.simg shub://BrendelGroup/TSRchitect
singularity exec tsr.simg R
which will bring up an R console with the TSRchitect library and all its prerequisites available. For example, in that console, you should see
R version 3.5.3 (2019-03-11) -- "Great Truth"
...
> packageVersion("TSRchitect")
[1] '1.17.3'
>
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.
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.
Our own publications will be linked here in due course.
Please direct all comments and suggestions to Volker Brendel at Indiana University and Taylor Raborn at his current address.