| name | small-rna-seq-target-prediction |
| description | Predict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs. |
| tool_type | mixed |
| primary_tool | miRanda |
Version Compatibility
Reference examples tested with: BioPython 1.83+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package> then help(module.function) to check signatures
- CLI:
<tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
miRNA Target Prediction
"Predict target genes for my miRNAs" -> Identify potential mRNA targets of miRNAs using sequence-based thermodynamic alignment and database lookups from validated interaction repositories.
- CLI:
miranda miRNA.fa UTR.fa -sc 140 -en -20 for de novo prediction
- Python: API queries to miRTarBase, TargetScan for validated targets
miRanda Algorithm
Goal: Predict miRNA-mRNA target interactions using thermodynamic alignment scoring.
Approach: Run miRanda to align miRNA sequences against 3' UTR sequences with minimum score and energy thresholds.
miranda miRNA.fa UTRs.fa \
-sc 140 \
-en -20 \
-out predictions.txt
Parse miRanda Output
Goal: Extract miRNA-target interaction records from miRanda output into a structured DataFrame.
Approach: Parse the tab-delimited output lines starting with '>' to extract miRNA, target, score, energy, and position fields.
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
Goal: Retrieve conserved miRNA target predictions from the TargetScan database.
Approach: Query the downloadable TargetScan context++ score file by miRNA family name and rank by prediction score.
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_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
Goal: Retrieve machine-learning-based miRNA target predictions from miRDB.
Approach: Query the miRDB prediction file by miRNA ID and filter for high-confidence targets (score >= 80).
def query_mirdb(mirna_id):
'''Query miRDB for target predictions
miRDB uses machine learning for target prediction
Score > 80 indicates high confidence
'''
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
Goal: Identify high-confidence miRNA targets predicted by multiple independent algorithms.
Approach: Compute the intersection of predictions across miRanda, TargetScan, and miRDB, keeping targets found in at least N 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))
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
Goal: Retrieve experimentally validated miRNA-target interactions from miRTarBase.
Approach: Load the miRTarBase Excel download and filter by miRNA name to get validated targets with experimental evidence types.
def get_validated_targets(mirna):
'''Get experimentally validated targets from miRTarBase'''
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
Goal: Find miRNA seed region complementary matches within a 3' UTR sequence.
Approach: Extract the 7-mer seed (positions 2-8), compute its reverse complement, and scan the UTR for all occurrences.
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)
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
Goal: Identify biological functions enriched among predicted miRNA target genes.
Approach: Run GO and KEGG enrichment analysis on the target gene list using Enrichr via gseapy.
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
