9e75f44f1a
Source-only snapshot of the cluster branch for WES execution. Large reference files (HPA/MANE/ensemble FASTA, model weights, ~597 MB) are omitted: they are baked into the container images at build time and mounted from the dreamdock-data PVC at runtime, and exceed the Gitea request size limit. Pipeline entry point is main.nf, which orchestrates the biotransformer, conplex and tissue modules as a single workflow. Ligand inputs are read from the eureka workspace; protein_zarr and chembl_db come from the dreamdock-data PVC.
124 lines
4.8 KiB
Plaintext
124 lines
4.8 KiB
Plaintext
nextflow.enable.dsl=2
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process CONPLEX {
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container "${params.container_conplex}"
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containerOptions "${params.containerOptions}"
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publishDir "${params.outdir}/${params.project_name}/receptors", mode: 'copy'
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debug true
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maxForks 5
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// scratch true //deletes workdir after successful completion
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input:
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path smi_drug
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path smi_metabolite
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path mut_fasta
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output:
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path "*drug_scores.tsv", emit: drug
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path "*significant_interactions.tsv", emit: interactions
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script:
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"""
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workdir=`pwd`
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#concat mutated and reference fasta files. If it has name with _2 its mutated.
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#after test is don retrun this
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cat $mut_fasta /home/omic/ConPLex/MANE_referent_transcipt_reference.fasta > \$workdir/all_protein.fasta
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### Test
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#head -n 100 /home/omic/ConPLex/MANE_referent_transcipt_reference.fasta > \$workdir/test.fasta
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#cat $mut_fasta \$workdir/test.fasta > \$workdir/all_protein.fasta
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###
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. activate conplex-dti
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#transform smi for conplex
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python3 -c "
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import sys
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from rdkit import Chem
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import numpy as np
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import pandas as pd
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def converter(drug_csv, outname):
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#open file to write drug/matabolite smi
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out_file = open(outname + '.txt', 'w')
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#load drug csv; only has drug in separate lines
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drug_smi_list = np.array(pd.read_csv(drug_csv)['SMILES']).tolist()
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#original drugs are anotated with drug_{}
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for n, smi in enumerate(drug_smi_list):
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out_file.write('{}\\tdrug_{}\\n'.format(smi, n))
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out_file.close()
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converter('$smi_drug', 'drag')
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"
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#concatinate drag and metabolites
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cat drag.txt $smi_metabolite > \$workdir/smiles.smi
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#conplex predicition 1. round
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python3 /home/omic/ConPLex/conplex.py --fastas \$workdir/all_protein.fasta --smis \$workdir/smiles.smi --screening_batch_size ${params.screening_batch_size} --outdir \$workdir --split_write_df 'no'
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#### this part is use to test all alternative transcripts (this part can be skipped for now, cuz we will use it for tissue specific analysis later)
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##get fasta for 2. round transcripts for proteins above threshold
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#python3 /home/omic/ConPLex/get_round_2.py --threshold ${params.threshold} --workdir \$workdir --round 1
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##conplex all transcipt for protrin above threshold
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#python3 /home/omic/ConPLex/conplex.py --fastas \$workdir/round_2.fasta --smis \$workdir/smiles.smi --screening_batch_size ${params.screening_batch_size} --outdir \$workdir --split_write_df 'no'
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####
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#get data for second run
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python3 /home/omic/ConPLex/get_round_2.py --threshold ${params.threshold} --workdir \$workdir --round 2
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"""
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}
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process NETWORK_ENRICHMENT {
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container "${params.container_conplex}"
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containerOptions "${params.containerOptions}"
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publishDir "${params.outdir}/${params.project_name}/receptors", mode: 'copy'
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debug true
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maxForks 5
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input:
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path interactions
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output:
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path "*_network_enrichment.tsv", emit: network_enrichment
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path "*_network_interactions.tsv", emit: network_interactions
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script:
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"""
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#!/opt/conda/envs/conplex-dti/bin/python
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import pandas as pd
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import requests
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interaction_data = pd.read_csv('$interactions', sep = '\\t')
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interaction_data = interaction_data.sort_values('conplex_score', ascending=False)
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#get list of enst above threshold
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protein_network_list = '%0d'.join(list(pd.unique(interaction_data[interaction_data['conplex_score'] > ${params.protein_network_threshold}]['transcipt'])))
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#get urls
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url_network = f'https://string-db.org/api/tsv/network?identifiers={protein_network_list}&species=9606'
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url_enrichment = f'https://string-db.org/api/tsv/enrichment?identifiers={protein_network_list}&species=9606'
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#get enrichment dataframe
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r = requests.get(url_enrichment)
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lines = r.text.split('\\n') # pull the text from the response object and split based on new lines
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data = [l.split('\\t') for l in lines] # split each line into its components based on tabs
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enrichment_df = pd.DataFrame(data[1:-1], columns = data[0]) # convert to dataframe using the first row as the column names; drop empty, final row
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#get protein interaction dataframe
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r = requests.get(url_network)
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lines = r.text.split('\\n') # pull the text from the response object and split based on new lines
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data = [l.split('\\t') for l in lines] # split each line into its components based on tabs
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# convert to dataframe using the first row as the column names; drop empty, final row
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interactions_df = pd.DataFrame(data[1:-1], columns = data[0])
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# dataframe with the preferred names of the two proteins and the score of the interaction
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#interactions_df = interactions_df[['preferredName_A', 'preferredName_B', 'score']]
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#output name
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output_name = '$interactions'.split('/')[-1].split('_significant_interactions.tsv')[0]
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#save
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enrichment_df.to_csv(f'{output_name }_network_enrichment.tsv',sep='\\t')
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interactions_df.to_csv(f'{output_name }_network_interactions.tsv',sep='\\t')
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"""
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}
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