Pre-release

This release is a pre-release and may not be stable for production use.

PRE-RELEASE

The code in this repository is under active development and testing prior to an official release. Until that time, the project should be considered unstable and liable to change without warning.

SSE_for_Py — sequence scrambling and mutation tools

SSE_for_Py provides tools for scrambling and mutating RNA/DNA genome sequences while holding chosen properties of those sequences constant — for example, preserving the encoded protein while randomizing codon usage, or nudging a sequence's dinucleotide composition (e.g., CpG content) toward a target value. These operations are used to build null-model or control sequences for studying compositional selection pressures in viral and other genomes.

The scrambling and mutation algorithms were originally written in PowerBasic. That code was translated into C, and Python bindings were added on top of the C extension, all written by Claude, Anthropic's AI coding agent. The C code was checked (via the Python bindings) for equivalence with the original PowerBasic implementation (see Testing below).

Methods

The following methods are implemented identically in the PowerBasic original and the C code (for which Python bindings are provided). They are described here in a language-independent way; see Implementations below for how to call them from either language.

All methods operate on a nucleotide sequence (A, C, G, T). Methods whose description mentions "codons" require the sequence length to be a multiple of 3 and treat it as a coding sequence read in one frame, starting at the first base.

NDR (Nucleotide Distribution Randomization) — repeatedly swaps pairs of individual nucleotides, choosing swaps that preserve the sequence's n-tuple frequency distribution. Six method names select a fixed n-tuple size and swap style: NDR, NTR, NQR (n-tuple 2, 3, 4; random swaps) and NDS, NTS, NQS (n-tuple 2, 3, 4; sequential swaps). To scramble with any other n-tuple size, call the underlying scramble_NDR function directly (see Library functions below).
CDLR (Codon Distribution at Leucine and aRginine) — swaps nucleotides between synonymous codons so as to preserve dinucleotide frequencies while leaving the encoded protein sequence unchanged.
NOR (Nucleotide Only Randomization) — shuffles all nucleotides in the sequence with no constraints, other than leaving gap (-) and ambiguous (N) characters in their original positions. This destroys both the reading frame and the encoded protein.
COR (Codon Only Randomization) — shuffles whole codons to new positions with no constraints, preserving the overall codon composition but not the encoded protein sequence or codon-to-position mapping.
CLR (Codon Like Randomization) — shuffles synonymous codons (those encoding the same amino acid) among the positions that encode that amino acid, preserving the protein sequence while randomizing synonymous codon usage.
CLS (Codon Like Swap) — like CLR, but performs the randomization as a series of pairwise swaps between synonymous codons, rather than a full shuffle.
CLM (Codon Like Maximal) — for each codon position, independently draws a new, random synonymous codon, maximally randomizing synonymous codon usage while preserving the protein sequence.

All of the codon-aware methods (CDLR, COR, CLR, CLS) accept a genetic code number (1–23, following the NCBI genetic code tables) so that non-standard codon tables can be used.

In addition to scrambling, a mutation method nudges a sequence toward a target frequency for a chosen dinucleotide (e.g., CpG or ApT), expressed as a ratio relative to the frequency expected from the sequence's base composition. It can preserve the encoded protein, optionally preserve overall mononucleotide frequencies while it mutates, and can be told to leave specific other (named) dinucleotides unchanged.

Implementations

Python

Installation

SSE_for_Py ships as a compiled Python package (the C code is built into a Python extension module), with prebuilt wheels for common platforms.

Using pip:

pip install SSE_for_Py

Using uv:

uv add SSE_for_Py

Command-line tools

Installing SSE_for_Py provides the following commands:

scramble — scramble sequences in a FASTA file (or from stdin, or a single sequence given directly) using any of the methods described above.

# Scramble a FASTA file using the CDLR method, with genetic code 1
scramble CDLR input.fasta --genetic-code 1 --output scrambled.fasta

# Scramble a single sequence using NOR, with a given random seed
scramble NOR --sequence ACGTACGTACGT --seed 42

# Read from stdin, scramble with NTR (n-tuple 3, random swaps)
scramble NTR < input.fasta > scrambled.fasta

mutate — mutate sequences in a FASTA file (or from stdin, or a single sequence) toward a target dinucleotide frequency ratio.

# Reduce/increase CpG toward a 2x target ratio (the default)
mutate input.fasta --output mutated.fasta

# Target ApT at a different ratio, with a fixed seed
mutate input.fasta --dinuc-mutate ApT --diverge 1.5 --seed 123

encode-alignment — convert a FASTA alignment into the internal, digit-encoded alignment format used elsewhere in the package.
```
encode-alignment alignment.fasta --output alignment.enc
```

All of the FASTA-processing commands (scramble, mutate) share a common set of options, including --seed (for reproducibility), --output, --wrap (output line wrapping), --repeat (apply the operation more than once per input sequence), and --checksum (print an Adler-32 checksum of the output, used when validating against the PowerBasic reference — see Testing).

Library functions

The high-level, recommended entry points are SSE_for_Py.scramble.scramble (a generator that dispatches to any scrambling method) and SSE_for_Py.mutate.mutate:

from SSE_for_Py.scramble import scramble

# Scramble a single sequence directly
for seq_id, scrambled in scramble("CDLR", sequence="ATGCGTAAATAG", genetic_code=1):
    print(scrambled)

# Scramble every sequence in a FASTA file, using NTR (n-tuple 3, random swaps)
for seq_id, scrambled in scramble("NTR", "input.fasta"):
    print(f"{seq_id}: {scrambled}")

from SSE_for_Py.mutate import mutate

result = mutate("ATGCGTAAATAG", dinuc_mutate="CpG", dinuc_diverge=2.0)

Each scrambling method is also available directly as its own function, if you don't want to go through the scramble dispatcher:

from SSE_for_Py.scramble_NDR import scramble_NDR
from SSE_for_Py.scramble_CDLR import scramble_CDLR
from SSE_for_Py.scramble_NOR import scramble_NOR
from SSE_for_Py.scramble_COR import scramble_COR
from SSE_for_Py.scramble_CLR import scramble_CLR
from SSE_for_Py.scramble_CLS import scramble_CLS
from SSE_for_Py.scramble_CLM import scramble_CLM

scramble_NDR("ACGTACGTACGT", s_method=2, no_swaps=10, n_tuple=3)
scramble_CDLR("ATGCGTAAATAG", genetic_code=1)
scramble_NOR("ATGCGTAAATAG")
scramble_COR("ATGCGTAAATAG", genetic_code=1)
scramble_CLR("ATGCGTAAATAG", genetic_code=1)
scramble_CLS("ATGCGTAAATAG", genetic_code=1)
scramble_CLM("ATGCGTAAATAG")

Invalid arguments (e.g., a sequence whose length isn't a multiple of 3 for a codon-aware method) raise ValueError. Failures in the underlying C code, or a scramble that could not make enough progress to be considered meaningful (see the min_swap_fraction argument on scramble_NDR and scramble_CDLR), raise SSE_for_Py.exceptions.ScrambleError.

C library

The algorithms themselves live in a small, dependency-free C library under src/c/, with one source file per method (scramble_NDR.c, scramble_CDLR.c, scramble_NOR.c, scramble_COR.c, scramble_CLR.c, scramble_CLS.c, scramble_CLM.c), plus mutate_sequence.c for the mutation method and lib.c/lib.h for shared utilities (codon table setup and translation, RNG seeding).

All of that is compiled into a single Python extension module, SSE_for_Py._combined, built from combined_module.c plus the files above. Everything is built as one extension, rather than one per method, so that every method shares a single C-level RNG state — important for reproducibility when a script calls more than one scrambling method with the same seed.

The C code has no dependencies beyond the C standard library and libm, and can also be compiled and used standalone, outside of Python — see the benchmark-bin target in the Makefile for an example of linking it into a plain C binary.

Testing

Clone the repository and install the development dependencies with uv:

uv sync

Run the test suite:

make test

which is equivalent to uv run pytest. To run the tests across every supported Python version (currently 3.10 through 3.14), use:

make nox

Validating against the PowerBasic reference

Because the C code is a translation of the original PowerBasic implementation, the C extension can optionally be compiled with a simplified, deterministic random number generator instead of the platform's real one. Building with this test RNG (SSE_TEST_RNG=1) makes the C code draw from the exact same pseudo-random integer sequence as the PowerBasic code, given the same seed. This allows the two implementations' outputs be compared directly rather than only statistically.

That comparison is done by running the same input through both implementations and comparing an Adler-32 checksum of their output (the --checksum option on the scramble and mutate commands prints this). A checksum match on a given input and seed is strong evidence that the C translation faithfully reproduces the PowerBasic original's logic for that case. This technique was used throughout development to verify the C translation against the PowerBasic reference implementation, and is exercised by the test suite via:

make nox-with-rng-checksum

which builds the extension with both SSE_TEST_RNG=1 (the shared deterministic RNG) and PREVENT_REVERSIONS=1 (an extra guard against a specific class of double-free bug) before running the tests.

Benchmarking

Benchmarking is a developer-only tool — it's not installed as a command when you pip install/uv add this package, since it needs the C sources and Makefile from a source checkout to build its standalone-C comparison binary.

make benchmark

builds the C benchmark binary and the Python extension, then runs the benchmark module (python -m SSE_for_Py.cli.benchmark) to compare performance across methods and implementations.

benchmark results

SSE benchmark results

Sequence lengths: 100, 1000, 10000
Sequences per length: 100
Length SD: 0.0
Methods: CDLR, CLM, CLR, CLS, COR, NDR, NOR, translate
Repeats per sequence: 3
Warmup calls per sequence: 0
Seed: 1
Generated: 2026-08-24 16:08:09 UTC

Nucleotides per second

Sequence length 100

Method	C nt/s	Python nt/s	C / Python
CDLR	3,169,342	3,139,684	1.0x
CLM	197,419,355	164,881,017	1.2x
CLR	8,913,487	8,502,869	1.0x
CLS	9,161,677	8,994,179	1.0x
COR	53,310,105	45,940,360	1.2x
NDR	3,846,154	3,892,160	1.0x
NOR	25,478,768	24,327,750	1.0x
translate	209,589,041	225,625,582	0.9x

Sequence length 1000

Method	C nt/s	Python nt/s	C / Python
CDLR	6,378,915	6,357,758	1.0x
CLM	229,115,854	210,912,714	1.1x
CLR	8,733,548	8,519,392	1.0x
CLS	10,308,288	9,736,246	1.1x
COR	41,582,515	40,226,251	1.0x
NDR	10,796,638	10,718,490	1.0x
NOR	18,030,230	18,298,231	1.0x
translate	136,202,990	128,342,080	1.1x

Sequence length 10000

Method	C nt/s	Python nt/s	C / Python
CDLR	6,449,089	6,439,097	1.0x
CLM	231,563,513	210,873,606	1.1x
CLR	8,285,014	8,196,008	1.0x
CLS	10,424,397	10,263,846	1.0x
COR	35,344,421	34,134,994	1.0x
NDR	11,555,925	11,540,557	1.0x
NOR	14,413,142	13,958,460	1.0x
translate	26,553,512	26,086,465	1.0x

Call/timing details

Sequence length 100

Method	Language	Calls	Total nt	Total seconds	Min (s)	Max (s)	StdDev (s)
CDLR	C	300	30,600	0.0097	0.000025	0.000101	0.000005
CDLR	Python	300	30,600	0.0097	0.000025	0.000112	0.000007
CLM	C	300	30,600	0.0002	0.000000	0.000001	0.000000
CLM	Python	300	30,600	0.0002	0.000001	0.000010	0.000001
CLR	C	300	30,600	0.0034	0.000010	0.000023	0.000001
CLR	Python	300	30,600	0.0036	0.000011	0.000022	0.000002
CLS	C	300	30,600	0.0033	0.000009	0.000016	0.000001
CLS	Python	300	30,600	0.0034	0.000010	0.000019	0.000001
COR	C	300	30,600	0.0006	0.000001	0.000008	0.000001
COR	Python	300	30,600	0.0007	0.000002	0.000012	0.000001
NDR	C	300	30,600	0.0080	0.000017	0.000059	0.000006
NDR	Python	300	30,600	0.0079	0.000016	0.000070	0.000006
NOR	C	300	30,600	0.0012	0.000003	0.000012	0.000001
NOR	Python	300	30,600	0.0013	0.000004	0.000008	0.000001
translate	C	300	30,600	0.0001	0.000000	0.000009	0.000001
translate	Python	300	30,600	0.0001	0.000000	0.000003	0.000000

Sequence length 1000

Method	Language	Calls	Total nt	Total seconds	Min (s)	Max (s)	StdDev (s)
CDLR	C	300	300,600	0.0471	0.000140	0.000271	0.000014
CDLR	Python	300	300,600	0.0473	0.000138	0.000226	0.000012
CLM	C	300	300,600	0.0013	0.000003	0.000015	0.000002
CLM	Python	300	300,600	0.0014	0.000005	0.000013	0.000001
CLR	C	300	300,600	0.0344	0.000106	0.000213	0.000010
CLR	Python	300	300,600	0.0353	0.000107	0.000174	0.000008
CLS	C	300	300,600	0.0292	0.000089	0.000158	0.000008
CLS	Python	300	300,600	0.0309	0.000091	0.000135	0.000007
COR	C	300	300,600	0.0072	0.000021	0.000038	0.000003
COR	Python	300	300,600	0.0075	0.000022	0.000046	0.000004
NDR	C	300	300,600	0.0278	0.000082	0.000187	0.000010
NDR	Python	300	300,600	0.0280	0.000082	0.000229	0.000013
NOR	C	300	300,600	0.0167	0.000050	0.000090	0.000005
NOR	Python	300	300,600	0.0164	0.000050	0.000081	0.000005
translate	C	300	300,600	0.0022	0.000006	0.000014	0.000001
translate	Python	300	300,600	0.0023	0.000007	0.000018	0.000002

Sequence length 10000

Method	Language	Calls	Total nt	Total seconds	Min (s)	Max (s)	StdDev (s)
CDLR	C	300	3,000,600	0.4653	0.001424	0.001747	0.000067
CDLR	Python	300	3,000,600	0.4660	0.001408	0.002351	0.000094
CLM	C	300	3,000,600	0.0130	0.000038	0.000059	0.000004
CLM	Python	300	3,000,600	0.0142	0.000042	0.000064	0.000004
CLR	C	300	3,000,600	0.3622	0.001108	0.001386	0.000055
CLR	Python	300	3,000,600	0.3661	0.001108	0.001386	0.000056
CLS	C	300	3,000,600	0.2878	0.000873	0.001066	0.000041
CLS	Python	300	3,000,600	0.2923	0.000885	0.001074	0.000044
COR	C	300	3,000,600	0.0849	0.000263	0.000405	0.000020
COR	Python	300	3,000,600	0.0879	0.000265	0.000389	0.000019
NDR	C	300	3,000,600	0.2597	0.000783	0.001266	0.000054
NDR	Python	300	3,000,600	0.2600	0.000789	0.001362	0.000065
NOR	C	300	3,000,600	0.2082	0.000644	0.000829	0.000032
NOR	Python	300	3,000,600	0.2150	0.000646	0.000852	0.000039
translate	C	300	3,000,600	0.1130	0.000346	0.000446	0.000020
translate	Python	300	3,000,600	0.1150	0.000351	0.000488	0.000023

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Algorithm	Hash digest
SHA256	`f8483e1070c1494a2d287c0f50aae0241647e75db792013d15856c98e93dcdf1`
MD5	`c93ba7e1f3dc14b5a0d2b915fdef089d`
BLAKE2b-256	`6ffc64cb5195f2fff1fd75a5426ac31ac6a16b9eb07555df7d1d9cbe9940284e`

Algorithm	Hash digest
SHA256	`f24f32f1e1dc6bc29c292eac87b4a86470397cfcaecc0f91044de4e44109f1a0`
MD5	`5a4ca5626926bd3b0058ef4d508cf3f6`
BLAKE2b-256	`473d04ef072ae3f2ffef30b09f862f48d5a90a8c66a2747e9e60eae858c6e785`

Algorithm	Hash digest
SHA256	`5cd68bc62e22d12de5b6e60c4df65c5f9f9ec6073990957d5c5162bfd22a6844`
MD5	`a352123317f7975cba917ca893cb31d7`
BLAKE2b-256	`364960d147a4315c7a50299cd05cdcdb82a7ac92ac65916ff21dfe9cacf16da7`

Algorithm	Hash digest
SHA256	`ecf95a23dea7c7a0819e4173a42cfa731a12f33f27326819629d9c0347231e04`
MD5	`0af40666e9b09fbc6b157bab0e40b860`
BLAKE2b-256	`7bb8c401e02222a385333e98b31ffd80f620a71aedb8c155cdc9adf5ca04581f`

Algorithm	Hash digest
SHA256	`d190481de2c98ed5081afef6a5c5ee7b87d5593326c302a429253fd6fd54b9be`
MD5	`cd44f7b01b2e892cff20b8e3e63ccf64`
BLAKE2b-256	`9b99b5c08f8ad6b173886543f865ab6e293942fd6f47df62a9da029dd83b180e`

Algorithm	Hash digest
SHA256	`084e5a489854f84ff3e25c9536355b1db4f12dfc317a9c9a742baab970906bf5`
MD5	`3d895f9ba146352223241fe45d19c09c`
BLAKE2b-256	`feac32a1e843c582131d68db7b0e20d1eac2139e9254c6532565c155acd4eec7`

Algorithm	Hash digest
SHA256	`f07b47b9bf625f249a266ac97b2b1b14ad966130428810ff4356f2acaac2abe2`
MD5	`a31b2da9ba3be0de9ff77fea28a6d8bd`
BLAKE2b-256	`fbfe79e1c03018f47276b9e31fbb0cb7d787cd367fa85c8e58ed299db1067574`

Algorithm	Hash digest
SHA256	`3f7d25df62f895448441d9098afc9311ce8ba4fa6dbd337b181ef9171f92869d`
MD5	`649d778c9835bf94f7a55c0dae5efc9e`
BLAKE2b-256	`2d0e856994827a07e34afc072b97b55a4ec43aa075e3833f705d100a92f8d3ab`

Algorithm	Hash digest
SHA256	`605e9ff074ff1af16977c93d711196bfa97c8a2a8ffab256074ceef3228fd275`
MD5	`70b5bacdfbb3ece5da4a85725243c9c4`
BLAKE2b-256	`71bb435a611eaa4745056410acc4439a223b9409b0119c3a931ffaf62d37e3be`

Algorithm	Hash digest
SHA256	`4e07913abc97f0df6878eda21232b68d924c608f989a4711107b91bf1f6d09df`
MD5	`9e1065cc68849412369514ef34ebf2d1`
BLAKE2b-256	`10d16d6c0e04a5a1e9be4b7bab9206820c4fe74570cb4747a208e62f419a9bab`

Algorithm	Hash digest
SHA256	`5c31d5e0f736ac23e6044e4041d8846abfbc6a0e1657fd62d509c9c68a1982a2`
MD5	`23c0644081317921686a73a46feb37e8`
BLAKE2b-256	`e9897a6d79b70ff06764de28b3701b2c7d04afbfd6ac3698c8da55ad29d1f0ef`

Algorithm	Hash digest
SHA256	`f437118ad7ada58f6e1210fac29318c56383207df26b78e4861ed30868b991b3`
MD5	`798dce73c7cf9f98a03ac50b9c4e4cba`
BLAKE2b-256	`7a6ccc1e4b0207aeae30b9e587c40c1fec414c4ce85e4d86d0c3c7795b0adfd6`

Algorithm	Hash digest
SHA256	`e831225eeb7fbc3e55d463648e53669f62c7a4d5f59b5ae925076b3504fde6a1`
MD5	`b695baa76d239fd06d745deaf34462ad`
BLAKE2b-256	`d15db144477b64385fc2f50db6bb2c08220b1128336256728a14092dcad8fc3c`

Algorithm	Hash digest
SHA256	`19a6b9e5e0a5d184dce740b847140503475168988819bfdd956928695ec95e42`
MD5	`84335cf28342ed59d426c5a2c00f433d`
BLAKE2b-256	`2f9dfd6274e2a52946d61857e0b45eb0a36f5f7c2a0a4d332842893cb2f53206`

Algorithm	Hash digest
SHA256	`7b7e30a6f34edc19170d5d76a28e84ed18c2b4f90aa6ab04de4d82891d4234ee`
MD5	`2edc223633c963f9e13ea42fc14c6902`
BLAKE2b-256	`709846a0eba21a476bbb9f3150394d18b14df7e20615c2f7cc298a5f3a76b080`

SSE-for-Py 0.1.0rc12

PRE-RELEASE

SSE_for_Py — sequence scrambling and mutation tools

Methods

Implementations

Python

Installation

Command-line tools

Library functions

C library

Testing

Validating against the PowerBasic reference

Benchmarking

benchmark results

SSE benchmark results

Nucleotides per second

Sequence length 100

Sequence length 1000

Sequence length 10000

Sequence length 100

Sequence length 1000

Sequence length 10000

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License expression

Requires

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Download files

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Algorithm	Hash digest
SHA256	`90dbf3b291fb912cc177092ee1ae3e29595a06c8a72135754f76b15ba3b2b8d4`
MD5	`5f78e791ca4339dc03f4c1b5cae2402e`
BLAKE2b-256	`69c1e95d4a8479945e0e86f51de5a52a49ac06c403c615f4cdf648bf0dbfc11a`

Algorithm	Hash digest
SHA256	`e0bbd5fb44952e147460fc01e3571e6171054476a5c6b032e2b7c734a4f7747d`
MD5	`621d347d03d428ee07ceaca4315b0609`
BLAKE2b-256	`8ad10eb3ade78593662c28d3078ea6e73ec451bb9af8c67b539ca3b26aac39af`

Algorithm	Hash digest
SHA256	`23bf6f41fe9e2935c31f83ca1f22ffb369738d62973b7905e0f390c99e4ced5b`
MD5	`0128a40d93e24e4495faf1da88c0d9c5`
BLAKE2b-256	`09ac4aa17fe1b7debb43684154cc101f5933a6ed145fec408c68847900024912`

Algorithm	Hash digest
SHA256	`80b35be5cac7fc92264d11b34c653ec51598bc84a5b1a944e907c995a54ba5da`
MD5	`656b3f729e6234320a535cdad088ef09`
BLAKE2b-256	`e4963083d7ffdd653413e6af10404a70273be61b4f12c552c69d5631d6b593d2`

Algorithm	Hash digest
SHA256	`a85fdfec1134a59f5dd16de544a974df17901d20b429c26daa8dc7d35f6d01ab`
MD5	`b6909d41ae24db28b39a0a717266407a`
BLAKE2b-256	`9389fe2612cb8c5ac4c309466fab17115992ee40d71dfe3759e8c55a9753598a`