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import pandas as pd
import numpy as np
import os
import subprocess
import sys
from Bio import SeqIO
import shutil
from fuson_plm.utils.logging import open_logfile, log_update
from fuson_plm.data.config import CLUSTER
def main():
# Read all the input args
LOG_PATH = "clustering_log.txt"
INPUT_PATH = CLUSTER.INPUT_PATH
MIN_SEQ_ID = CLUSTER.MIN_SEQ_ID
C = CLUSTER.C
COV_MODE = CLUSTER.COV_MODE
PATH_TO_MMSEQS = CLUSTER.PATH_TO_MMSEQS
MAX_SEQ_LENGTH = CLUSTER.MAX_SEQ_LENGTH
with open_logfile(LOG_PATH):
log_update("Input params from config.py:")
CLUSTER.print_config(indent='\t')
# Make a subfolder for clustering results, and direct MMSeqs2 outputs here
if not(os.path.exists("clustering")):
os.mkdir("clustering")
output_dir = "clustering/raw_output"
# Make fasta of input file
sequences = pd.read_csv(INPUT_PATH)
log_update(f"\nPreparing input data...\n\tInitial dataset size: {len(sequences)} sequences")
sequences = sequences.loc[sequences['aa_seq'].str.len() <= MAX_SEQ_LENGTH].reset_index(drop=True)
log_update(f"\tApplied length cutoff of {MAX_SEQ_LENGTH}AAs. New dataset size: {len(sequences)} sequences")
sequences = dict(zip(sequences['seq_id'],sequences['aa_seq']))
fasta_path = make_fasta(sequences, "clustering/input.fasta")
log_update(f"\tMade fasta of input sequences, saved at {fasta_path}")
run_mmseqs_clustering(fasta_path, output_dir, min_seq_id=MIN_SEQ_ID, c=C, cov_mode=COV_MODE, path_to_mmseqs=PATH_TO_MMSEQS)
# Brief read to preview results
clusters = analyze_clustering_result('clustering/input.fasta', 'clustering/raw_output/mmseqs_cluster.tsv')
# Save clusters
clusters.to_csv('clustering/mmseqs_full_results.csv',index=False)
log_update("Processed and combined mmseqs output. Wrote comprehensive results to clustering/mmseqs_full_results.csv")
cluster_summary(clusters)
if __name__ == "__main__":
main() |