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name: bio-motif-search description: Find sequence motifs, degenerate IUPAC patterns, and transcription-factor binding sites in DNA/RNA using Biopython and regex, including position weight matrix (PWM/PSSM) scoring. Use when locating regulatory elements, counting overlapping motif occurrences, scanning for binding-site matches above a significance threshold, or reading motif matrices from JASPAR/MEME/TRANSFAC files. For restriction enzyme sites, use restriction-analysis/restriction-sites. tool_type: python primary_tool: Bio.motifs

Version Compatibility

Reference examples tested with: BioPython 1.83+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Motif Search

"Search for a sequence motif or binding-site pattern" -> Scan sequences for a fixed motif, a degenerate IUPAC consensus, or a probabilistic PWM, on one or both strands, and locate transcription-factor binding sites, regulatory elements, or custom patterns.

• Python: Bio.SeqUtils.nt_search (IUPAC + overlaps), re (regex/lookahead), Bio.motifs (PWM/PSSM scoring + matrix file parsing)

The Governing Principle

Two silent failures dominate motif searching; both return a plausible-but-wrong answer with no error:

1. Overlapping matches are dropped. str.count, str.find, and re.findall consume the string left to right, so a motif that overlaps its own next occurrence is undercounted. Target AAGCGCGCGAA, motif GCGC: str.count returns 1, the true answer is 2 (starts at positions 2 and 4). Use a zero-width lookahead re.finditer(r'(?=(GCGC))', target) or Bio.SeqUtils.nt_search, both of which report overlaps.
2. A PSSM score is a likelihood in bits, not a probability. pssm.calculate returns log2-odds versus background. A "high-looking" threshold chosen by eye is arbitrary and non-reproducible; derive the threshold from the score distribution at a chosen false-positive rate. And a PSSM scans only the strand it is given, so scoring just the forward strand silently misses roughly half of real sites on double-stranded DNA.

Which Approach for Which Question

Question	Tool
Position of first exact hit	str.find / Seq.find (returns -1 if absent)
All exact hits, possibly overlapping	re.finditer(r'(?=(motif))', seq)
Degenerate IUPAC consensus (e.g. GATNNTC), with overlaps	Bio.SeqUtils.nt_search(seq, motif)
Flexible / repeat / variable-spacer pattern	re with explicit character classes and quantifiers
Graded match to many aligned sites (binding sites)	Bio.motifs PWM -> PSSM, score and threshold
Match significance / false-positive control	pssm.distribution(...).threshold_fpr(fpr)
Restriction enzyme recognition sites	restriction-analysis/restriction-sites

IUPAC Degenerate Motifs

A degenerate motif expands each ambiguity code to a regex character class:

Code	Class	Code	Class	Code	Class
N	[ACGT]	R	[AG]	Y	[CT]
W	[AT]	S	[GC]	K	[GT]
M	[AC]	B	[CGT]	D	[AGT]
H	[ACT]	V	[ACG]

B, D, H, V each exclude A, C, G, T respectively (the code preceding the one they drop is a mnemonic).

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

def iupac_to_regex(pattern):
    return ''.join(IUPAC_DNA.get(base, base) for base in pattern)

# 'GATNNTC' -> 'GAT[ACGT][ACGT]TC'

Bio.SeqUtils.nt_search expands IUPAC ambiguity in the query motif automatically and reports overlapping hits, so it is the shortest correct path for a degenerate consensus.

Overlapping Matches (the count trap)

Goal: Report every start position of a motif, including self-overlapping occurrences.

Approach: Use a zero-width lookahead so the regex engine never consumes the matched text; recover the match string from the inner capture group. For IUPAC motifs, prefer nt_search, which both expands ambiguity and reports overlaps.

Reference (BioPython 1.83+):

import re
from Bio.SeqUtils import nt_search

target = 'AAGCGCGCGAA'

starts = [match.start(1) for match in re.finditer(r'(?=(GCGC))', target)]  # [2, 4]
hits = [(match.start(1), match.group(1)) for match in re.finditer(r'(?=([AG]CG[CT]))', target)]

result = nt_search(target, 'GCGC')  # ['GCGC', 2, 4]
pattern, positions = result[0], result[1:]

nt_search returns a heterogeneous list: result[0] is the (expanded) pattern string and result[1:] are the 0-based start positions. When there are no hits it returns just [pattern] (length 1), so test len(result) > 1 before indexing rather than truthiness.

Bio.motifs PWM / PSSM Pipeline

Goal: Build a probabilistic model from a set of aligned binding sites and score a target sequence for graded matches.

Approach: Create a motif from instances or a matrix file, set pseudocounts and background, read the recomputed PSSM, then scan. The count matrix m.counts['A', 0] is indexed [base, position].

Reference (BioPython 1.83+):

from Bio import motifs
from Bio.Seq import Seq

m = motifs.create([Seq('TACAA'), Seq('TACGA'), Seq('TACTA'), Seq('TGCAA')])  # alphabet defaults to ACGT

m.pseudocounts = 0.5        # set BEFORE reading m.pssm (see trap below)
m.background = None         # None gives uniform 0.25; or pass a dict of base frequencies

pwm = m.pwm                 # normalized frequencies (property)
pssm = m.pssm               # log2-odds vs background (property; RECOMPUTED on each access)

m.consensus                 # most frequent base per column
m.degenerate_consensus      # IUPAC-degenerate consensus

m.counts.normalize(pseudocounts=0.5) returns a position weight matrix and pwm.log_odds() returns a PSSM; these are equivalent to reading m.pwm / m.pssm after setting m.pseudocounts.

The Pseudocount / -inf Trap (silent)

A column where some base has count 0 gives that base frequency 0 and a log-odds of negative infinity; any target carrying that base at that position then scores -inf and is unmatchable. This is common with short motifs or few instances. Setting m.pseudocounts (a flat 0.5, or sqrt(N) with N the number of instances; scalar or per-base dict) makes every cell finite by shrinking toward background.

Critically, m.pssm is recomputed from m.pseudocounts and m.background on every access. Set both BEFORE reading m.pssm (or pwm.log_odds()), or the matrix is silently wrong.

Score, Threshold, and P-value

pssm.calculate(seq) returns the log2-odds score in bits for each window (a relative likelihood, not a probability). pssm.search(seq, threshold=...) yields (position, score) pairs at or above the threshold.

To convert a bit score into a false-positive rate, build the null distribution and ask it for a threshold:

dist = pssm.distribution(background=m.background, precision=10**4)
threshold = dist.threshold_fpr(0.01)        # 1% false-positive rate
threshold = dist.threshold_fnr(0.1)         # 10% false-negative rate
threshold = dist.threshold_balanced(1000)   # rate_proportion = FNR:FPR ratio (FNR = FPR x rate_proportion), NOT a sequence length; default 1.0 gives FPR=FNR

Choosing a threshold "because it looks high" is the classic non-reproducible error. Higher precision gives finer threshold resolution at the cost of memory.

Both Strands

pssm.calculate scans only the strand it is handed. pssm.search defaults to both=True, scanning both strands in one call; with both=True a hit at negative position p lies on the reverse strand and its forward-coordinate start is len(seq) + p. To handle strands separately, set both=False and scan the reverse-complemented PSSM explicitly:

combined = list(pssm.search(seq, threshold=3.0))  # both strands; reverse hits have NEGATIVE positions

rc_pssm = pssm.reverse_complement()
forward = list(pssm.search(seq, threshold=3.0, both=False))
reverse = list(rc_pssm.search(seq, threshold=3.0, both=False))

Reading Motif Matrix Files

motifs.read(handle, fmt) reads exactly one motif; motifs.parse(handle, fmt) returns an iterator over many. The format string must match the file layout exactly.

fmt string	File type / source
jaspar	multi-motif JASPAR PFM collection (use parse)
pfm	single JASPAR-style PFM (use read)
pfm-four-columns	CIS-BP, HOMER, HOCOMOCO (A C G T as columns)
pfm-four-rows	ScerTF, YeTFaSCo (A C G T as rows)
sites	JASPAR sites file (use read)
meme	MEME program output (use parse)
minimal	MEME minimal text format
transfac	TRANSFAC matrices
mast, alignace, clusterbuster, xms	respective tool outputs

'cisbp', 'homer', and 'hocomoco' are NOT valid strings; those databases use pfm-four-columns. The four-columns versus four-rows distinction is the most common mix-up: a 4-column matrix read as pfm-four-rows parses without error but produces a meaningless transposed motif.

from Bio import motifs

with open('collection.jaspar') as handle:
    for m in motifs.parse(handle, 'jaspar'):
        print(m.matrix_id, m.name, m.consensus)

m.format('jaspar')      # serialize back out
m.format('transfac')

Common Motif Patterns

Motif	Pattern	Description
Start codon	ATG	Translation initiation
Kozak	[AG]CCATGG	Eukaryotic translation initiation
TATA box	TATA[AT]A[AT]	Core promoter element
GC box (Sp1)	GGGCGG	Promoter element
CAAT box	CCAAT	Promoter element
Poly-A signal	AATAAA	mRNA polyadenylation
E-box (bHLH)	CA[ACGT]{2}TG	bHLH TF binding

Common Errors

Symptom	Cause	Fix
Count is too low	str.count/re.findall skip overlaps	re.finditer(r'(?=(motif))', seq) or nt_search
IndexError on nt_search result	No hits returns [pattern] (length 1)	Test len(result) > 1 before reading result[1:]
Every target scores -inf	Count-0 cell gives -inf log-odds	Set m.pseudocounts (0.5 or sqrt(N)) before reading m.pssm
PSSM scores look wrong	Pseudocounts/background set after reading m.pssm	Set them first; m.pssm is recomputed on each access
Roughly half of sites missed	Only forward strand scanned	Score pssm.reverse_complement() or pass both=True
Threshold not reproducible	Cutoff chosen by eye	pssm.distribution(...).threshold_fpr(fpr)
ValueError parsing matrix	Wrong fmt (4-column vs 4-row, jaspar vs pfm)	Match fmt to the actual layout
No matches	Case or strand mismatch	.upper() both; check reverse complement

Related Skills

• seq-objects - Create Seq objects for searching
• reverse-complement - Reverse-complement the target to search the opposite strand
• transcription-translation - ORF and codon-context motifs in coding sequences
• sequence-properties - GC content and per-sequence properties around hits
• restriction-analysis/restriction-sites - Restriction enzyme recognition sites
• chip-seq/motif-analysis - De novo motif discovery and enrichment in peak sets
• database-access/entrez-fetch - Download motif matrices from JASPAR/NCBI