mutagenize_scan

Slide a mutagenesis window of fixed length across the sequence (or a named region) and, at each position, apply random point mutations within that window. Bases outside the window are returned unchanged, enabling systematic scanning for position-specific mutational sensitivity.

import poolparty as pp
pp.init()

Parameters

Parameter	Type	Default	Description
`pool`	`Pool \| str`	(required)	The Pool to scan. Can also be a plain sequence string.
`mutagenize_length`	`int`	(required)	Width of the mutagenesis window in bases. A sequence of length L produces L - `mutagenize_length` + 1 window positions.
`num_mutations`	`int \| None`	`None`	Fixed number of point mutations introduced per window draw. Mutually exclusive with `mutation_rate`.
`mutation_rate`	`float \| None`	`None`	Per-base probability of mutation within the window. Each base is independently mutated with this probability. Mutually exclusive with `num_mutations`.
`positions`	`list[int] \| None`	`None`	Explicit list of window start positions. `None` = all valid positions.
`region`	`str \| None`	`None`	Name of a tagged region to restrict the scan to. Flanks are never modified.
`style`	`str \| None`	`None`	Named display style applied to mutated bases (e.g., `'red'`, `'blue bold'`).
`prefix`	`str \| None`	`None`	Prefix for auto-generated sequence names.
`mode`	`str \| tuple`	`'random'`	`'sequential'` or `'random'`. A scalar is broadcast to both the scan (position) and mutagenize (mutation) sub-operations. A 2-tuple `(scan_mode, mut_mode)` controls each independently.
`num_states`	`int \| tuple \| None`	`None`	Number of output states. A scalar is broadcast to both sub-operations. A 2-tuple `(scan_states, mut_states)` sets each independently; total states = product of the two.
`iter_order`	`int \| None`	`None`	Dimension-name ordering for downstream multi-pool iteration.

Note

Only the most commonly used parameters are shown above. For the full parameter list, see mutagenize_scan() in the API Reference.

Examples

Unlike mutagenize(), which mutates across the entire sequence, mutagenize_scan confines mutations to a fixed-width window that slides along the sequence. This makes it a two-dimensional operation: one dimension controls where the window sits (position), and the other controls what mutations occur inside it. Both mode and num_states accept 2-tuples (position_value, mutation_value) for independent control of each dimension.

Full sequential enumeration

mode=("sequential", "sequential") systematically enumerates every window position and every single-base variant within each window. A 3-base window over an 8-mer with num_mutations=1 yields 6 positions × (3 bases × 3 non-wild-type substitutions) = 54 total states.

wt   = pp.from_seq("ACGTACGT")
scan = wt.mutagenize_scan(mutagenize_length=3, num_mutations=1,
                          mode=("sequential", "sequential"),
                          style="red")
scan.print_library()

scan: seq_length=8, num_states=54 CCGTACGT
GCGTACGT
TCGTACGT
AAGTACGT
AGGTACGT ... (54 total)

Random scan with multiple draws

mode=("random", "random") samples both window position and mutation randomly. num_states=(5, 1) draws 5 random positions with one random mutation at each, for 5 total states.

wt   = pp.from_seq("ACGTACGTACGT")
scan = wt.mutagenize_scan(mutagenize_length=4, num_mutations=1,
                          mode=("random", "random"),
                          num_states=(5, 1), style="red")
scan.print_library()

scan: seq_length=12, num_states=5 ACGTACGCACGT
ACGTACGTATGT
ACGTCCGTACGT
AGGTACGTACGT
ACCTACGTACGT

Multiple mutations per window

num_mutations=2 introduces two substitutions, both confined to the 4-base window. Bases outside the window are always unchanged — this is the key difference from mutagenize(), which can place mutations anywhere in the sequence.

wt   = pp.from_seq("ACGTACGTACGT")
scan = wt.mutagenize_scan(mutagenize_length=4, num_mutations=2,
                          mode=("random", "random"),
                          num_states=(5, 1), style="red")
scan.print_library()

scan: seq_length=12, num_states=5 ACGTGCATACGT
ACGTACGTCTGT
ACGTGCGAACGT
AAGCACGTACGT
ACATTCGTACGT

Deep scan: random positions, all mutations

mode=("random", "sequential") picks random window positions but enumerates every single-base variant at each — useful for deeply profiling a few candidate regions. num_states=(3, None) selects 3 random positions; None lets the mutation dimension auto-enumerate all variants (12 per position → 36 total).

wt   = pp.from_seq("ACGTACGTACGT")
scan = wt.mutagenize_scan(mutagenize_length=4, num_mutations=1,
                          mode=("random", "sequential"),
                          num_states=(3, None), style="red")
scan.print_library()

scan: seq_length=12, num_states=36 ACGTCCGTACGT
ACGTGCGTACGT
ACGTTCGTACGT
ACGTAAGTACGT
ACGTAGGTACGT ... (36 total)

Broad scan: all positions, sampled mutations

mode=("sequential", "random") visits every window position systematically but draws only a few random mutations at each — a cost-effective way to scan the entire sequence. num_states=(None, 3) auto-enumerates all 9 positions and samples 3 mutations per window (27 total).

wt   = pp.from_seq("ACGTACGTACGT")
scan = wt.mutagenize_scan(mutagenize_length=4, num_mutations=1,
                          mode=("sequential", "random"),
                          num_states=(None, 3), style="red")
scan.print_library()

scan: seq_length=12, num_states=27 ACGCACGTACGT
ACGGACGTACGT
CCGTACGTACGT
ACGTGCGTACGT
ACGTTCGTACGT ... (27 total)

Per-base mutation rate

mutation_rate=0.5 mutates each base in the window independently with 50 % probability; the number of mutations per draw varies (including zero). num_states=(5, 1) samples 5 random window positions with one stochastic mutation draw per position.

wt   = pp.from_seq("ACGTACGTACGT")
scan = wt.mutagenize_scan(mutagenize_length=4, mutation_rate=0.5,
                          mode=("random", "random"),
                          num_states=(5, 1), style="red")
scan.print_library()

scan: seq_length=12, num_states=5 ACGTAGGTACGT
ACGTACATAAGT
ACGTACGTACGT
ACCTCCGTACGT
ACAACCGTACGT

Scan within a named region

Restrict the scan to the cre region; flanking sequences are never modified. Here mode=("sequential", "random") visits every position within the region and draws 3 random mutations at each.

wt   = pp.from_seq("AAAA<cre>ATCGATCGATCG</cre>TTTT")
scan = wt.mutagenize_scan(mutagenize_length=4, num_mutations=1,
                          region="cre",
                          mode=("sequential", "random"),
                          num_states=(None, 3), style="red")
scan.print_library()

scan: seq_length=20, num_states=27 AAAA<cre>ATCCATCGATCG</cre>TTTT
AAAA<cre>ATCTATCGATCG</cre>TTTT
AAAA<cre>CTCGATCGATCG</cre>TTTT
AAAA<cre>ATCGGTCGATCG</cre>TTTT
AAAA<cre>ATCGTTCGATCG</cre>TTTT ... (27 total)

See mutagenize_scan().