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This repository contains the code and data required for reproducing the analyses in the Puffin manuscript. For using Puffin and training Puffin models, please visit our main repository. For most use cases, we highly recommend running Puffin from our webserver, tss.zhoulab.io or puffin.zhoulab.io.

Most of the analyses code are provided in jupyter notebook format. Each jupyter notebook contains a series of analyses and typically generates multiple plots for related analyses.

The jupyter notebooks are grouped by topics:

• Puffin_analysis.ipynb : The main analysis notebook for most Puffin-based analyses.
• Motif_contribution.ipynb : Motif contribution score-based analyses including analysis of insertion screen results.
• Puffin_insilicoKO.ipynb : In silico KO analyses.
• NFY_TATA_experiment_insilico : Motif insertion and deletion effect analysis.
• Trinucleotide_effects.ipynb : Normalizing and analyzing trinucleotide effects (raw trinucleotide effect scores have to be properly normalized before any analysis, as detailed in Supplementary Note 2 of the manuscript).
• Evolution_conservation.ipynb : Evolutionary conservation analysis.
• Evolution_mouse_comparison.ipynb : Human and mouse sequence dependency comparison analysis.
• Puffin-D_analysis.ipynb : Analysis of Puffin-D synthetic promoter screen results.
• train/FANTOM_CAGE_biascorrection.ipynb : Preprocessing of FANTOM CAGE data.
• train/train_puffin_D.py : Training code for Puffin-D.
• train/train_puffin_stage_1.py : Training code for Puffin.
• train/train_puffin_stage_2.py : Training code for Puffin.
• train/train_puffin_stage_3.py : Training code for Puffin.
• train/Process_models.ipynb : Processing of models during multi-stage training.
• train/train_puffin_mouse.py : Training code for mouse Puffin for human-mouse comparison.
• puffin_D_synthetic_screen.py : Code for the Puffin-D synthetic promoter screen.
• puffin_D_insertion_screen.py : Code for the Puffin-D promoter insertion screen.

Other than Puffin dependencies, you will also need jupyter, rpy2 python packages which can be installed with Anaconda or pip. For R packages, we will use data.table, R.utils, ggplot2, patchwork, ggridges, ggrastr, ggthemes, and ggExtra.

For training Puffin-D, you need to install the custom_target_support branch of Selene

git clone https://github.com/kathyxchen/selene.git
cd selene
git checkout custom_target_support
python setup.py build_ext --inplace
python setup.py install 

You will need resource files for reproducing the analyses, and we have provided these files through Zenodo and can be downloaded using the commands below

#under the puffin_mansucript directory
wget https://zenodo.org/record/7954971/files/resources.tar.xz
tar xf ./resources.tar.xz

cd resources 
wget https://zenodo.org/record/10810327/files/GSM3323461_oligos_measurements_processed_data.tab
wget https://zenodo.org/record/10810327/files/CRISPRCapV3_S45_R2_001Aligned.sortedByCoord.out.bam 
wget https://zenodo.org/record/10810327/files/CRISPRCapV4_S51_R2_001Aligned.sortedByCoord.out.bam 
wget https://zenodo.org/record/10810327/files/pSTAP.bam 
wget https://zenodo.org/record/10810327/files/Supplemental_Data_S2.tsv

wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173678/suppl/GSE173678_RAW.tar 
tar -xf GSE173678_RAW.tar