Code to analyze the landscape of human ssRNA virus-host interactions.
Full list of PWMs used in the study
List of cluster-representative PWMs
See notebooks for the code used to produce these PWM lists.
python scan_fasta.py --genome_file <Path to genome fasta file> \
--pwm_file <Path to .h5 file with processed PWMs> \
--out_home <head directory to save output folder> \
--threshold <threshold for binding site identification> \
--sense <whether input sequence is + or - sense> \
--simN <Number of shuffling iterations for background distribution> \
--simK <k-mer frequency to conserve in background distribution> \
--rcomp <none, only or both> \
--out_tsv <if this flag is specified, output is saved as .tsv; otherwise output is saved as.h5> \
An example command is below:
python scan_fasta.py --genome_file DATA/Genomes/Genomes/V/Segmented/Arenaviridae/GCF_000851025.1/GCF_000851025.1_ViralMultiSegProj14862_genomic.fna.gz \
--pwm_file DATA/PWMs/attract_rbpdb_encode_filtered_human_pwms.h5 \
--out_home RESULTS \
--threshold 0.8 \
--sense + \
--simN 1000 \
--simK 2 \
--rcomp both \
--out_tsv
The above script creates a folder in the directory that was supplied through --out_home. The output files depend on the chosen format and options.
The example command above would produce the following output files:
RESULTS (head directory)
--GCF_000851025.1 (results directory)
----results_attract_filtered.h5
----shuf_1000_2.fa.gz
shuf_1000_2.fa.gz contains 1000 shuffled sequences generated from the given genome.
results_attract_filtered.h5 contains the results of binding site prediction and analysis.
If --out_tsv is provided, the example command above would instead produce the following output files:
RESULTS (head directory)
--GCF_000851025.1 (results directory)
----enrichment_shuf_1000_2_attract_filtered.tsv
----shuf_1000_2.fa.gz
----sites_attract_filtered.tsv
sites_attract_filtered.h5 contains the locations of binding sites found on the given genome.
enrichment_shuf_1000_2_attract_filtered.h5 contains enrichment results for the given genome relative to the shuffled sequences.
If --out_tsv and --out_all are both provided, the example command above would also produce the following additional output file:
----sites_shuf_1000_2_attract_filtered.tsv
This file contains the locations of binding sites found on the shuffled genome sequences. If -out_all is provided but not out_tsv, these results will still be written, but to the same .h5 file as the other results.
python scan_utrs.py --utr_file DATA/Genomes/UTRs/Group_IV_UTR.txt \
--pwm_file DATA/PWMs/attract_rbpdb_encode_filtered_human_pwms.h5 \
--root_dir DATA/Genomes/Genomes/IV/ \
--out_home RESULTS \
--threshold 0.8 \
--sense + \
--simN 1000 \
--simK 2 \
--rcomp none
The .h5 output files can be opened in python using the pandas read_hdf command. For example:
import pandas as pd
results_file = 'RESULTS/GCF_000851025.1/results_attract_filtered.h5'
real_sites = pd.read_hdf(results_file, key='real_sites') # Read binding sites on real genome
enrichment_results = pd.read_hdf(results_file, key='enr') # Read enrichment results
shuf_sites = pd.read_hdf(results_file, key='shuf_sites') # Read binding sites on shuffled genomes, if they were written
real_sites.head()
You can save the same data to a tab-separated text file. For example:
enrichment_results.to_csv('output_file_name.csv', sep='\t', index=False)
Create a new environment:
conda create --name VirusHostInteractionAtlas python=3.7
Activate the environment:
conda activate VirusHostInteractionAtlas
Clone SMEAGOL:
git clone https://github.com/gruber-sciencelab/SMEAGOL && cd SMEAGOL
Install SMEAGOL:
pip install -e .
Run SMEAGOL tests:
cd tests
pytest
cd ..
Install openpyxl so we can read the supplementary data directly from the Excel files:
pip install openpyxl
Add the installation to jupyter
python -m ipykernel install --user --name=VirusHostInteractionAtlas --display-name='Python 3.7 (VirusHostInteractionAtlas)'
Go to the 'VirusHostInteractionAtlas' directory
cd ..
Start the jupyter notebook
jupyter notebook &
Afterwards, make sure that you have chosen the correct kernel (Top Menu Bar: Kernel --> Change Kernel --> 'Python 3.7 (VirusHostInteractionAtlas)')