PPTP-seq

PPTP-seq (pooled promoter responses to transcription factor perturbations sequencing) is a highly scalable method to dissect regulatory genome. This repository includes data process scripts, processed data, external data, and jupyter notebooks used in the paper.

Citation:

Han, Y., Li, W., Filko, A. et al. Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli. Nat Commun 14, 5757 (2023). https://doi.org/10.1038/s41467-023-41572-4

Setup

All the scripts were run on a HPC cluster using Linux system, where SLURM workload manager (version: slurm-wlm 17.11.7) was used. Bowtie2, Samtools, and Bedtools need to be installed in the system. Jupyer notebooks depend on Python 3.8.5 and a few commonly used modules including scipy, numpy, pandas, matplotlib, seaborn, and multiprocess.

Raw NGS data deposition

NGS data deposited to NCBI GEO as Series GSE213624.

Scripts

The scripts #1-#4 were designed to process raw sequencing data for each replicate. It returns a BED file that contains read counts of each sgRNA-promoter pair in each bin.

$ 1_bowtie2-build.sh
$ sbatch 2_bowtie2.sbatch
$ sbatch 3_create_bed_files.sbatch
$ 4_find_closest_operon.sh

After obtaining the read counts for all triplicates, the script #5 converted read counts to cell counts and fitted cell counts using log-normal distribution.

$ python 5_calculate_read_counts.py
$ python 6_data_process.py

Jupyter Notebooks

Four files are used for exploratory data analysis, differential expression analysis, TF binding site analysis, and validation using a tunable TF library. Figures were reproduced in these notebooks.

Processed data

Columns in PPTP-seq_Glu.csv, PPTP-seq_LB.csv, PPTP-seq_Gly.csv:

1. operon: operon expressed by the promoter of interest
2. tf_gene: tf gene targeted by the sgRNA
3. mean: average promoter activity across replicates at natural log scale
4. std: standard deviation of the promoter activity across replicates at natural log scale
5. std_linear: standard deviation of the promoter activity across replicates at linear scale
6. n_rep: number of replicates measured for the variant
7. FC: log2 fold change compared to the promoter activity in negative controls
8. FCOM: log2 fold change compared to the median promoter activity across all CRISPRi perturbations
9. -logP: -log10(adjusted p value)
10. class: 1 represents up-regulated, 0 represents not significant, and -1 represents down-regulated
11. FC_global: average FC of all promoter responses to a TF perturbation
12. FC_specific: FC - FC_global, the gene specific effects that exclude global effects from a TF