bio-motif-search

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

Motif Search

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

Required Imports

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

Core Methods

find() - First Occurrence

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

Returns -1 if not found.

count() - Count Occurrences

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

find() with Start Position

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

Code Patterns

Find All Occurrences

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

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:

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

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)

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

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

from Bio import motifs
from Bio.Seq import Seq

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

Motif Properties

# 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

# 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

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

# 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

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

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

TRANSFAC Format

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

Write Motifs

# 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