Promoter identification from diverse types of large-scale TSS profiling data
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README.md

TSRchitect: Promoter identifcation 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 https://www.singularity-hub.org/static/img/hosted-singularity--hub-%23e32929.svg

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 3.5.1 (2018-07-02) -- "Feather Spray"
...
> packageVersion("TSRchitect")
[1] '1.8.0'
>

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.

Reference

R. Taylor Raborn and Volker P. Brendel (2018) Promoter identification in plants from analysis of large-scale transcription start site (TSS) data. To be submitted.

Contact

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