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The Bio.motifs module provides tools for analyzing and manipulating sequence motifs, including reading motif files, calculating position weight matrices (PWMs), and searching sequences for motif occurrences.

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Main Functions

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create()

Create a Motif object from sequence instances. 
from Bio import motifs

motif = motifs.create(
    ["TACAA", "TACGC", "TACAC", "TACCC"],
    alphabet="ACGT"
)
print(motif.consensus)
Parameters:
  • instances: List of sequence strings or SeqRecord objects
  • alphabet (str, optional): Alphabet to use (default: “ACGT”)
Returns: Motif object Source: Bio/motifs/__init__.py:34

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parse()

Parse an output file from a motif finding program. 
from Bio import motifs

with open("motifs.txt") as handle:
    for motif in motifs.parse(handle, "meme"):
        print(motif.consensus)
Parameters:
  • handle: File handle to parse
  • fmt (str): Format name (case-insensitive)
  • strict (bool, optional): Enforce strict format compliance (default: True)
Returns: List of Motif objects Supported Formats:
  • alignace: AlignAce output
  • clusterbuster: Cluster Buster position frequency matrix
  • jaspar: JASPAR multiple PFM format
  • meme: MEME output
  • minimal: MINIMAL MEME output
  • mast: MAST output
  • pfm: JASPAR-style position-frequency matrix
  • pfm-four-columns: Generic 4-column PFM format
  • pfm-four-rows: Generic 4-row PFM format
  • sites: JASPAR-style sites file
  • transfac: TRANSFAC database format
  • xms: XMS matrix format
Source: Bio/motifs/__init__.py:40

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read()

Read a single motif from a file. 
from Bio import motifs

with open("single_motif.pfm") as handle:
    motif = motifs.read(handle, "pfm")
    print(motif.consensus)
Parameters:
  • handle: File handle to read
  • fmt (str): Format name (case-insensitive)
  • strict (bool, optional): Enforce strict format compliance (default: True)
Returns: Single Motif object Raises: ValueError if file contains zero or more than one motif Source: Bio/motifs/__init__.py:129

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write()

Return string representation of motifs in specified format. 
from Bio import motifs

motifs_list = [motif1, motif2]
output = motifs.write(motifs_list, "transfac")
Parameters:
  • motifs: List of Motif objects
  • fmt (str): Output format (“clusterbuster”, “pfm”, “jaspar”, “transfac”)
  • **kwargs: Format-specific keyword arguments
Returns: String representation Source: Bio/motifs/__init__.py:615

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Motif Class

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Motif

A class representing sequence motifs. 
from Bio import motifs

motif = motifs.create(["AACGCCA", "ACCGCCC", "AACTCCG"])
print(motif.consensus)
print(motif.pwm)
Attributes:
  • name (str): Motif name
  • alphabet (str): Alphabet used
  • length (int): Length of motif
  • counts: FrequencyPositionMatrix object with nucleotide counts
  • alignment: Alignment object with sequences
  • pseudocounts (dict): Pseudocounts for each letter
  • background (dict): Background frequencies
  • mask: Mask for motif positions
Properties:
  • pwm: Position Weight Matrix (normalized frequencies)
  • pssm: Position-Specific Scoring Matrix (log-odds)
  • consensus (Seq): Consensus sequence
  • anticonsensus (Seq): Anticonsensus sequence
  • degenerate_consensus (Seq): Degenerate consensus using IUPAC codes
  • relative_entropy (array): Information content per position
Methods:
  • reverse_complement(): Return reverse complement of motif
  • weblogo(fname, fmt): Download and save weblogo image
  • format(format_spec): Format motif for output
Source: Bio/motifs/__init__.py:187

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Position Weight Matrix (PWM)

Normalized frequency matrix. 
motif = motifs.create(["TACGC", "TACCC"])
pwm = motif.pwm
print(pwm)
Methods:
  • log_odds(background): Calculate PSSM from PWM
  • search(sequence, threshold): Search for motif in sequence
  • calculate(sequence): Calculate PWM score for sequence
Source: Bio/motifs/matrix.py

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Position-Specific Scoring Matrix (PSSM)

Log-odds scoring matrix. 
motif = motifs.create(["TACGC", "TACCC"])
motif.pseudocounts = 0.5
motif.background = {'A': 0.3, 'C': 0.2, 'G': 0.2, 'T': 0.3}
pssm = motif.pssm
print(pssm)
Methods:
  • search(sequence, threshold): Search sequence for high-scoring matches
  • calculate(sequence): Calculate PSSM score for sequence
  • max_score(): Maximum possible score
  • min_score(): Minimum possible score
  • mean(): Mean score across positions
  • std(): Standard deviation across positions
Source: Bio/motifs/matrix.py

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Searching Sequences

from Bio import motifs
from Bio.Seq import Seq

# Create motif
motif = motifs.create(["TACGC", "TACCC", "TACAC"])

# Set parameters
motif.pseudocounts = 0.5
motif.background = {'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}

# Search using PSSM
sequence = Seq("ATACGCTACCCTAGGGG")
for position, score in motif.pssm.search(sequence, threshold=5.0):
    print(f"Position {position}: score {score:.2f}")

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Setting Pseudocounts and Background

from Bio import motifs

motif = motifs.create(["TACGC", "TACCC"])

# Set pseudocounts (uniform)
motif.pseudocounts = 0.5

# Set pseudocounts (per-letter)
motif.pseudocounts = {'A': 0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}

# Set background (GC content for DNA)
motif.background = 0.5  # 50% GC

# Set background (per-letter)
motif.background = {'A': 0.3, 'C': 0.2, 'G': 0.2, 'T': 0.3}

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Example

from Bio import motifs
from Bio.Seq import Seq

# Create motif from sequences
instances = [
    "TACGC",
    "TACCC",
    "TACAC",
    "TAGGC",
    "TACGA"
]

motif = motifs.create(instances, alphabet="ACGT")
print(f"Consensus: {motif.consensus}")
print(f"Length: {motif.length}")
print(f"\nCounts:")
print(motif.counts)

# Set parameters for scoring
motif.pseudocounts = 0.5
motif.background = {'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}

# Get PWM and PSSM
print(f"\nPWM:")
print(motif.pwm)
print(f"\nPSSM:")
print(motif.pssm)

# Search for motif in sequence
sequence = Seq("ATACGCTACCCTAGGGGCGTATACGA")
print(f"\nSearching in: {sequence}")
for position, score in motif.pssm.search(sequence, threshold=3.0):
    subseq = sequence[position:position+motif.length]
    print(f"Position {position}: {subseq} (score {score:.2f})")

# Get reverse complement
rc_motif = motif.reverse_complement()
print(f"\nReverse complement consensus: {rc_motif.consensus}")

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