miRNA Target Prediction
miRanda Algorithm
# Run miRanda for target prediction
miranda miRNA.fa UTRs.fa \
-sc 140 \
-en -20 \
-out predictions.txt
# Options:
# -sc 140: Minimum alignment score (default 140)
# -en -20: Maximum free energy threshold (kcal/mol)
# Higher score and lower energy = stronger prediction
Parse miRanda Output
import pandas as pd
def parse_miranda(output_file):
'''Parse miRanda output file'''
results = []
with open(output_file) as f:
for line in f:
if line.startswith('>'):
parts = line.strip().split('\t')
if len(parts) >= 5:
results.append({
'mirna': parts[0].lstrip('>'),
'target': parts[1],
'score': float(parts[2]),
'energy': float(parts[3]),
'position': parts[4]
})
return pd.DataFrame(results)
TargetScan Database Lookup
import requests
import pandas as pd
def query_targetscan(mirna_family):
'''Query TargetScan for predicted targets
Note: TargetScan uses miRNA family names (e.g., miR-21-5p)
'''
# TargetScan provides downloadable files
# For human: https://www.targetscan.org/vert_80/vert_80_data_download/
targetscan_file = 'Predicted_Targets_Context_Scores.txt'
df = pd.read_csv(targetscan_file, sep='\t')
targets = df[df['miRNA family'] == mirna_family]
return targets.sort_values('context++ score')
miRDB Database Lookup
def query_mirdb(mirna_id):
'''Query miRDB for target predictions
miRDB uses machine learning for target prediction
Score > 80 indicates high confidence
'''
# Download from http://mirdb.org/download.html
mirdb_file = 'miRDB_v6.0_prediction_result.txt'
df = pd.read_csv(mirdb_file, sep='\t', header=None,
names=['mirna', 'target', 'score'])
targets = df[df['mirna'] == mirna_id]
return targets[targets['score'] >= 80].sort_values('score', ascending=False)
Combine Multiple Databases
def consensus_targets(mirna, min_databases=2):
'''Find targets predicted by multiple databases
More reliable targets are predicted by multiple algorithms
'''
miranda_targets = set(query_miranda_targets(mirna))
targetscan_targets = set(query_targetscan_targets(mirna))
mirdb_targets = set(query_mirdb_targets(mirna))
# Count predictions per target
all_targets = miranda_targets | targetscan_targets | mirdb_targets
consensus = []
for target in all_targets:
count = sum([
target in miranda_targets,
target in targetscan_targets,
target in mirdb_targets
])
if count >= min_databases:
consensus.append({
'target': target,
'n_databases': count,
'miranda': target in miranda_targets,
'targetscan': target in targetscan_targets,
'mirdb': target in mirdb_targets
})
return pd.DataFrame(consensus).sort_values('n_databases', ascending=False)
Python miRNA Target Prediction
# Using mirtarbase package for validated targets
def get_validated_targets(mirna):
'''Get experimentally validated targets from miRTarBase'''
# Download from https://mirtarbase.cuhk.edu.cn/
mirtarbase_file = 'miRTarBase_MTI.xlsx'
df = pd.read_excel(mirtarbase_file)
validated = df[df['miRNA'] == mirna]
return validated[['Target Gene', 'Experiments', 'Support Type']]
Seed Match Analysis
from Bio.Seq import Seq
def find_seed_matches(mirna_seq, utr_seq):
'''Find seed matches in UTR sequence
Seed region: positions 2-8 of miRNA (7-mer)
'''
mirna = Seq(mirna_seq)
utr = Seq(utr_seq)
# Get seed (positions 2-8, 0-indexed: 1-7)
seed = str(mirna[1:8])
seed_rc = str(Seq(seed).reverse_complement())
matches = []
start = 0
while True:
pos = str(utr).find(seed_rc, start)
if pos == -1:
break
matches.append(pos)
start = pos + 1
return matches
Functional Enrichment of Targets
def enrich_target_genes(targets, background=None):
'''Run GO enrichment on predicted target genes'''
import gseapy as gp
enr = gp.enrichr(
gene_list=targets,
gene_sets=['GO_Biological_Process_2021', 'KEGG_2021_Human'],
organism='Human'
)
return enr.results
Related Skills
- differential-mirna - Get DE miRNAs for target prediction
- pathway-analysis/go-enrichment - Enrich target gene functions
- database-access/entrez-fetch - Query biological databases
FridrichMethod