small-rna-seq-target-prediction

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.

# 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

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 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

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
    '''
    # 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

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))

    # 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

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.

# 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

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)

    # 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

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