Welcome!

Here you will find further details and resources for exploring data and analysis related to the paper “A computational framework for identifying promoter sequences in non-model organisms using RNA-seq datasets” (published in ACS Synthetic Biology in May 2021). The git repo contains all instructions for running the code but below you will find static html tutorials as well as links to their corresponding Jupyter Notebooks. We’ve also provided a data visualization gallery of interactive plots that we hope will facilitate further exploration and understanding of M. buryatense expression data.

Tutorials

See the following Jupyter Notebooks for tutorials on how various pieces of analysis were carried out:

1. Exploration of TPM data across conditions ipynb
2. Inspection of BioProspector output files ipynb
3. Consensus motif analysis ipynb
4. Compare consensus predictions across organisms ipynb
5. Operon distance exploration ipynb

Interactive Visualizations

Choose an image in the gallery to explore explore data interactively.

Top Gene Set Tradeoff	Top 1% Expression	Top 3% Expression
Intra-operon upstream distances	Promoter Enrichment vs Information Content