/Dalal_hippo_signaling_2024

MIT LicenseMIT

Date Purpose
Feb 2024
Give setup directions for Hippo signaling paper

Thanks, and Kudos to Melanie Weilert for many scripts!

Introduction

Here, we give setup instructions for someone interested in reproducing analysis from the Hippo signaling paper. The steps we take are:

  1. Environment setup (Anaconda3)
  2. Process sequencing data (Snakemake)
  3. Analysis (Python and R)

Conda (Anaconda3) environment setup

BPNet use TensorFlow1 It is HIGHLY recommended that you use conda=4.7.12 while reproducing this code, since BPNet operates under older sotfware and updated versions of conda are not compatible with the setup instructions below:

Setup for BPNet environment

The BPNet conda environment can be installed using the instructions found here: [https://github.com/kundajelab/bpnet]. There are 2 environments: 1 with and 1 without a GPU capability. If you choose to install the GPU-compatible BPNet environment on an Nvidia GPU (we trained on a NVIDIA® TITAN RTX GPU), then you will need the appropriate drivers:

  • CUDA v9.0
  • cuDNN v7.0.5

Process sequencing data

All data is located in data_preperation/* and the pipeline instructions are designated from two Snakefiles using Snakemake. The Snakefile sources all the input starting information from the setup/samples.csv and setup/samples_rna.csv file from the starting_file column.

Transcription factor binding datasets were processed separately to train BPNet model with Snakefile under bpnet_prep/ folder, which I plan to merge with other data.

In order to assign the nexus barcodes, we should parse through each site to get sequencing data.

parallel -j 10 bash scripts/nexus_identify_fixed_barcodes.sh -i {} -o txt/nexus_barcodes/\`basename {} .fastq.gz \`\.freqs.txt ::: fastq/mm10/nexus/*.fastq.gz
tail -n +1 txt/nexus_barcodes/*.str.txt

In order to process the data, navigate to the data_preparation/ folder, then type snakemake -j 6 for 6 simultaneous tasks running. *NOTE: currently broken and will fix asap.

Software versions associated with data processing

  • R==4.2.0
  • Python==3.7.6
  • bowtie1==1.1.2
  • bowtie2==2.3.5.1
  • cutadapt==2.5
  • samtools==1.14
  • Java OpenJDK==1.8.0_191
  • bamCompare==3.1.3
  • macs2==2.2.6
  • idr==2.0.3
  • snakemake==5.4.5

Analysis

The rendered .ipynb and .Rmd files are under the analysis/ folder. Files are numbered in the order by which they were run. Raw figures can be found here as well as code and associated scripts to run analysis.

The prep and analysis in this folder were done in following order:

  1. The bpnet_prep/ folder is where I had processed binding data with snamekmake and was used to train BPNet model
  2. The data_preperation/ folder is where I ran snakemake to generate all bws of different datasets including binding data.
  3. The bpnet/ folder has BPNet model outputs and analyses associated with it.