Agent skill

bio-motif-search

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-motif-search

SKILL.md

name: bio-motif-search description: Find patterns, motifs, and subsequences in biological sequences using Biopython. Use when searching for transcription factor binding sites, regulatory elements, or any sequence pattern. For restriction enzyme analysis, use the restriction-analysis skill. tool_type: python primary_tool: Bio.motifs measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

Motif Search

Find patterns and motifs in biological sequences using Biopython and regex.

Required Imports

python
from Bio.Seq import Seq
from Bio import motifs
import re

Core Methods

find() - First Occurrence

python
seq = Seq('ATGCGAATTCGATCGAATTCGATC')
pos = seq.find('GAATTC')  # Returns 4 (first position)

Returns -1 if not found.

count() - Count Occurrences

python
seq = Seq('ATGCGAATTCGATCGAATTCGATC')
n = seq.count('GAATTC')  # Returns 2

find() with Start Position

python
seq = Seq('ATGCGAATTCGATCGAATTCGATC')
first = seq.find('GAATTC')        # 4
second = seq.find('GAATTC', 5)    # 14 (search from position 5)

Code Patterns

Find All Occurrences

python
def find_all(seq, pattern):
    pattern = str(pattern)
    seq_str = str(seq)
    positions = []
    pos = seq_str.find(pattern)
    while pos != -1:
        positions.append(pos)
        pos = seq_str.find(pattern, pos + 1)
    return positions

seq = Seq('ATGCGAATTCGATCGAATTCGATC')
positions = find_all(seq, 'GAATTC')  # [4, 14]

Search Both Strands

python
def find_both_strands(seq, pattern):
    results = []
    for pos in find_all(seq, pattern):
        results.append(('+', pos))
    rc = seq.reverse_complement()
    for pos in find_all(rc, pattern):
        results.append(('-', len(seq) - pos - len(pattern)))
    return results

Regex Pattern Search

For ambiguous or flexible patterns:

python
def regex_search(seq, pattern):
    seq_str = str(seq)
    return [(m.start(), m.group()) for m in re.finditer(pattern, seq_str)]

# Find all ATG start codons
matches = regex_search(seq, 'ATG')

# Find TATA box variants (TATAAA with possible variations)
matches = regex_search(seq, 'TATA[AT]A[AT]')

IUPAC Ambiguity Pattern

python
IUPAC_DNA = {
    'R': '[AG]', 'Y': '[CT]', 'S': '[GC]', 'W': '[AT]',
    'K': '[GT]', 'M': '[AC]', 'B': '[CGT]', 'D': '[AGT]',
    'H': '[ACT]', 'V': '[ACG]', 'N': '[ACGT]'
}

def iupac_to_regex(pattern):
    regex = ''
    for char in pattern:
        regex += IUPAC_DNA.get(char, char)
    return regex

# Search for pattern with ambiguous bases
pattern = 'GATNNTC'  # N = any base
regex = iupac_to_regex(pattern)  # 'GAT[ACGT][ACGT]TC'
matches = regex_search(seq, regex)

Find ORFs (Start to Stop)

python
def find_orfs(seq, start='ATG', stops=['TAA', 'TAG', 'TGA'], min_length=30):
    seq_str = str(seq)
    orfs = []
    start_positions = find_all(seq, start)
    for start_pos in start_positions:
        for frame_offset in range(3):
            if (start_pos - frame_offset) % 3 == 0:
                for stop in stops:
                    stop_pos = start_pos + 3
                    while stop_pos <= len(seq) - 3:
                        codon = seq_str[stop_pos:stop_pos + 3]
                        if codon == stop:
                            if stop_pos - start_pos >= min_length:
                                orfs.append((start_pos, stop_pos + 3, seq[start_pos:stop_pos + 3]))
                            break
                        stop_pos += 3
                break
    return orfs

Find Repeats

python
def find_tandem_repeats(seq, unit_length, min_copies=2):
    seq_str = str(seq)
    repeats = []
    for i in range(len(seq) - unit_length * min_copies + 1):
        unit = seq_str[i:i + unit_length]
        copies = 1
        pos = i + unit_length
        while pos <= len(seq) - unit_length and seq_str[pos:pos + unit_length] == unit:
            copies += 1
            pos += unit_length
        if copies >= min_copies:
            repeats.append((i, unit, copies))
    return repeats

seq = Seq('ATGCAGCAGCAGCAGTTT')
repeats = find_tandem_repeats(seq, 3, 2)  # Find CAG repeats

Bio.motifs Module

Create Motif from Instances

python
from Bio import motifs
from Bio.Seq import Seq

instances = [Seq('TACAA'), Seq('TACGA'), Seq('TACTA'), Seq('TGCAA')]
m = motifs.create(instances)

Motif Properties

python
# Consensus sequences
m.consensus              # Most common base at each position
m.degenerate_consensus   # IUPAC degenerate consensus
m.anticonsensus          # Least likely sequence

# Counts and matrices
m.counts                 # Position frequency matrix (counts)
pwm = m.counts.normalize(pseudocounts=0.5)  # Position weight matrix
pssm = pwm.log_odds()    # Position-specific scoring matrix

Information Content

python
# Per-position information content
pwm = m.counts.normalize(pseudocounts=0.5)
pssm = pwm.log_odds()

# Mean information content (bits)
mean_ic = pssm.mean()

# Score range
max_score = pssm.max
min_score = pssm.min

# Relative entropy
print(f'Mean IC: {mean_ic:.3f} bits')
print(f'Max score: {max_score:.3f}')
print(f'Min score: {min_score:.3f}')

PSSM Search

python
seq = Seq('ATGCTACAAGCTACGATACTA')

# Search with threshold
for position, score in pssm.search(seq, threshold=3.0):
    match = seq[position:position + len(m.consensus)]
    print(f'Position {position}: {match} (score: {score:.2f})')

# Search both strands
for position, score in pssm.search(seq, threshold=3.0, both=True):
    print(f'Position {position}: score {score:.2f}')

Calculate Threshold from Distribution

python
# Calculate score distribution from PSSM
sd = pssm.distribution()

# Get threshold for specific false positive rate
threshold = sd.threshold_fpr(0.01)  # 1% FPR

# Get threshold for specific false negative rate
threshold = sd.threshold_fnr(0.1)   # 10% FNR

# Balanced threshold
threshold = sd.threshold_balanced(1000)  # For sequence of length 1000

Reading Motif Files

JASPAR Format

python
from Bio import motifs

with open('motif.jaspar') as f:
    m = motifs.read(f, 'jaspar')
print(f'Name: {m.name}')
print(f'Matrix ID: {m.matrix_id}')
print(m.counts)

MEME Format

python
with open('meme.txt') as f:
    record = motifs.parse(f, 'meme')
for m in record:
    print(f'{m.name}: {m.consensus}')

TRANSFAC Format

python
with open('motif.transfac') as f:
    record = motifs.parse(f, 'transfac')
for m in record:
    print(f'{m.name}: {m.consensus}')

Write Motifs

python
# Write to JASPAR format
with open('output.jaspar', 'w') as f:
    f.write(m.format('jaspar'))

# Write to TRANSFAC format
with open('output.transfac', 'w') as f:
    f.write(m.format('transfac'))

Common Motif Patterns

Motif Pattern Description
Start codon ATG Translation initiation
Stop codons TAA|TAG|TGA Translation termination
Kozak [AG]CCATGG Eukaryotic translation initiation
TATA box TATA[AT]A[AT] Promoter element
GC box GGGCGG Promoter element (Sp1)
CAAT box CCAAT Promoter element
Poly-A signal AATAAA mRNA polyadenylation
E-box CA[ACGT]{2}TG bHLH TF binding
CpG island High CG density Promoter regions

Common Errors

Error Cause Solution
No matches found Case mismatch Use .upper() on both
Missing matches Pattern on opposite strand Search reverse complement too
TypeError Mixing Seq and string Use str() conversion
ValueError parsing motif Wrong format specified Check file format

Decision Tree

Need to find patterns in sequence?
├── Exact match?
│   ├── Just need position of first? → seq.find()
│   ├── Need count? → seq.count()
│   └── Need all positions? → loop with find()
├── Fuzzy/ambiguous pattern?
│   └── Use regex with re.finditer()
├── IUPAC pattern?
│   └── Convert to regex, then search
├── Both strands?
│   └── Search original and reverse_complement
├── Probabilistic (PWM/PSSM)?
│   └── Use Bio.motifs
│       ├── Create from instances → motifs.create()
│       ├── Read from file → motifs.read() / parse()
│       ├── Get consensus → m.consensus, m.degenerate_consensus
│       ├── Search sequence → pssm.search()
│       └── Calculate threshold → distribution.threshold_fpr()
└── Restriction sites?
    └── Use restriction-analysis skill (Bio.Restriction)

Related Skills

  • seq-objects - Create Seq objects for searching
  • reverse-complement - Search both strands for motifs
  • sequence-io/filter-sequences - Filter sequences that contain specific motifs
  • restriction-analysis/restriction-sites - For restriction enzyme site searching
  • database-access - Download motif databases from NCBI/JASPAR

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results