mimosa-tool 1.3.2

Model-Independent Motif Similarity Assessment tool

MIMOSA

MIMOSA (mimosa-tool) is a Python package and CLI for model-independent motif similarity assessment. It supports two complementary comparison strategies:

• profile: compare score profiles, either from precomputed score tracks or from motif scans on sequences
• motif: compare motif representations directly, with optional PFM reconstruction for incompatible representations

The current package version is 1.3.1.

Introduction

Transcription factors (TFs) are central regulators of gene expression. They modulate transcription by binding specific DNA segments in regulatory regions such as promoters and enhancers [1]. The DNA segment recognized by a TF is called a transcription factor binding site (TFBS). Binding sites for the same TF are similar but not identical, so they are commonly described as motifs that summarize the variability of recognized sequences [2].

Modern high-throughput assays such as ChIP-seq, HT-SELEX, and DAP-seq have produced large collections of TFBS motifs [3-5]. Position weight matrices (PWMs) and position frequency matrices (PFMs) remain the most common representation and are produced by widely used discovery tools such as MEME, STREME, and HOMER [6-8]. At the same time, many motif models now encode dependencies beyond independent nucleotide columns, including Bayesian Markov models, InMoDe, DIMONT, SiteGA-style dinucleotide models, and neural network predictors [9-21].

Motif comparison is required to decide whether a discovered pattern represents a known specificity, to cluster redundant motifs across experiments, and to infer functional relationships between TFs from similar binding preferences. Existing tools such as Tomtom, STAMP, MACRO-APE, and MoSBAT address this problem with matrix alignment, correlation, Jaccard-like, or affinity-profile approaches [22-25]. A practical limitation is that many tools assume PFM/PWM-like matrices, which makes heterogeneous comparisons harder and may lose information when dependency-aware models must be reduced to simple matrices.

MIMOSA addresses this by separating motif comparison into two workflows. The profile workflow compares recognition profiles, either supplied directly as score tracks or generated by scanning sequences with motif models. This is the most model-independent workflow and is conceptually close to affinity-profile comparison [25]. The motif workflow aligns motif matrices or tensors directly when representations are compatible; for heterogeneous inputs, it reconstructs PFMs from sequence hits and then applies the same alignment logic, similar in spirit to Tomtom-style matrix comparison [22, 26].

Methodology

Profile Comparison

profile mode converts motif behavior into positional score profiles. If inputs are motif models, MIMOSA scans the supplied FASTA sequences on both strands. If raw scores must be normalized, they are mapped to empirical -log10(FPR) values using either --background sequences or the comparison sequences themselves. This makes scores from different model families more comparable.

The workflow then selects anchor sites from the first model: either one best-scoring site per sequence, or all sites above --min-logfpr. For each anchor, MIMOSA extracts a site-centered window, evaluates compatible windows from the second model across the requested shift range and strand orientations, scores the aligned windows, and reports the best orientation and offset. The offset is target_start - query_start in the oriented alignment, so positive values mean the target starts to the right of the query. The four orientation labels are ++, +-, -+, and --.

Motif Matrix/Tensor Comparison

motif mode aligns motif representations directly. For compatible model families, the native matrix or tensor is normalized and aligned without sequence scanning. Reverse-complement candidates are evaluated, and only alignments with at least half of the shorter motif overlapping are considered.

When model families differ, or when --pfm-mode is enabled, MIMOSA reconstructs a PFM for each input model:

1. Each model is scanned on the provided or generated sequences.
2. The best site per sequence is collected across position and strand.
3. Sites are ranked by score and the top --pfm-top-fraction fraction is retained.
4. A position count matrix is built from retained sites and converted to a PFM with additive pseudocount smoothing.
5. The reconstructed PFMs are compared with the same alignment and orientation search used for native matrices.

Similarity Metrics

MIMOSA reports higher scores as better matches for all metrics. The profile metrics operate on non-negative normalized score windows. Let $v_1$ and $v_2$ be two aligned profile vectors.

Continuous Overlap (co)

The continuous overlap coefficient, also known as the Szymkiewicz-Simpson coefficient, measures how much the smaller profile is contained in the larger one:

$$ \mathrm{CO}(v_1, v_2) = \frac{\sum_i \min(v_1^i, v_2^i)} {\min\left(\sum_i v_1^i, \sum_i v_2^i\right)} $$

Continuous Dice (dice)

The Dice coefficient normalizes the shared mass by the total mass of both profiles:

$$ \mathrm{Dice}(v_1, v_2) = \frac{2 \sum_i \min(v_1^i, v_2^i)} {\sum_i v_1^i + \sum_i v_2^i} $$

Cosine Similarity (cosine)

Cosine similarity compares the angle between two vectors:

$$ \mathrm{Cosine}(v_1, v_2) = \frac{\sum_i v_1^i v_2^i} {\sqrt{\sum_i (v_1^i)^2}\sqrt{\sum_i (v_2^i)^2}} $$

profile mode also exposes co_rowwise and dice_rowwise. These compute one score per selected window and then average finite window scores, instead of pooling all selected windows into one flattened vector. Rowwise variants are useful when many anchors are selected and each anchor should contribute more evenly.

The motif metrics operate column-by-column on aligned matrices or flattened tensors. Let $u_t$ and $v_t$ be two aligned columns at position $t$.

Pearson Correlation (pcc)

$$ \mathrm{PCC}(u_t, v_t) = \frac{\sum_k (u_{k,t} - \bar{u}t)(v{k,t} - \bar{v}t)} {\sqrt{\sum_k (u{k,t} - \bar{u}t)^2} \sqrt{\sum_k (v{k,t} - \bar{v}_t)^2}} $$

Euclidean Distance (ed)

$$ \mathrm{ED}(u_t, v_t) = \lVert u_t - v_t \rVert_2 $$

For ed, MIMOSA returns the negative mean Euclidean distance across aligned columns so that larger values still represent more similar motifs.

Column Cosine (cosine)

For motif matrices, cosine is computed column-wise and averaged across the aligned overlap:

$$ \mathrm{Cosine}(u_t, v_t) = \frac{\sum_k u_{k,t} v_{k,t}} {\sqrt{\sum_k u_{k,t}^2}\sqrt{\sum_k v_{k,t}^2}} $$

Null Hypothesis and Significance

MIMOSA uses a stored pooled null distribution. A null distribution file is built ahead of time from a motif collection split into groups, such as transcription-factor families or classes. The group table defines which motifs are treated as unrelated: for each eligible query motif, null targets are loaded motifs that are present in the group table and have a different group label. Motifs from the same group are not used as null targets for each other.

For PWM/PFM collections, the input collection can also be prepared as a shuffled control set before running MIMOSA, for example by permuting motif positions and nucleotide weights within positions. mimosa build-null does not shuffle motifs internally; it treats the supplied shuffled set as the motif collection and still applies the group table to choose unrelated query-target pairs.

MIMOSA compares all eligible query-target pairs selected by these group relations using the selected strategy and metric. Each score is computed after the usual optimization over shifts and orientations. The resulting scores are pooled into one shared null sample:

$$ S_{\text{null}} = {s(q, t): q \ne t,; group(q) \ne group(t)} $$

For an observed score $S_{\text{obs}}$, significance is computed as an upper-tail survival probability because all reported metrics are oriented so that larger scores mean stronger similarity:

$$ p = P(S_{\text{null}} \ge S_{\text{obs}}) $$

The pooled distribution is fitted with SciPy's generalized extreme value distribution:

$$ \theta = \operatorname{genextreme.fit}(S_{\text{null}}), \quad p = \operatorname{genextreme.sf}(S_{\text{obs}}; \theta) $$

Null distribution files are produced by mimosa build-null and stored as trusted .joblib files. Each file contains one pooled distribution across all eligible query-target comparisons. For each eligible query motif, MIMOSA compares that query against unrelated target motifs selected from different groups, appends those scores to the shared null sample, and then fits a GEV survival estimator to the pooled scores. The file records:

• comparison strategy and metric
• FASTA and background fingerprints, stored as none when sequence-derived scoring is not used
• motif collection fingerprint
• relation input fingerprint
• null-format version
• the GEV estimator parameters
• raw, unsorted null scores in the same order as the stored pairs
• query-target motif-name pairs that contributed scores

At comparison time, --pvalue loads either the explicit --null-distribution file or the first compatible file found in --null-search-dir paths and the user cache (~/.cache/mimosa/nulls). Compatibility is checked against the null-format version, strategy, metric, and sequence/background fingerprints. An explicit incompatible file raises an error; a search with no compatible file returns the score-only result and logs a warning.

Annotated results include p-value, adj.p-value, E-value, null_id, null_n, and null_estimator. When more than one comparison is annotated, adj.p-value is computed with SciPy's stats.false_discovery_control; for a single comparison it equals p-value. The E-value is p-value * effective_number_of_targets; pass --effective-number-of-targets to override the default target count.

Supported Inputs

Family	CLI key	Typical inputs
Precomputed score profiles	scores	FASTA-like file with numeric rows
PWM / PFM	pwm	.meme, .pfm, or compatible joblib-pickled GenericModel
BaMM	bamm	.ihbcp or a basename resolvable to .ihbcp
SiteGA	sitega	.mat or compatible joblib-pickled GenericModel
DiMotif / Dimont	dimont	.xml or compatible joblib-pickled GenericModel
Slim	slim	.xml or compatible joblib-pickled GenericModel

Notes:

• profile supports all six types above, including direct scores vs scores
• motif supports all motif families except scores
• heterogeneous motif comparisons automatically switch to sequence-driven PFM reconstruction
• build-null accepts directory motif collections for all motif families and multi-motif MEME collections for pwm
• BaMM loading uses a uniform background model; a separate background file is not required

Security note

MIMOSA can load .pkl model files and .joblib null distribution files. These formats use Python pickle/joblib serialization and may execute arbitrary code when loaded. Only load such files from trusted sources.

Installation

MIMOSA requires Python 3.10+.

Install from PyPI:

uv pip install mimosa-tool

or:

pip install mimosa-tool

Install from source:

git clone https://github.com/ubercomrade/mimosa.git
cd mimosa
uv sync --group dev
uv pip install -e . --no-build-isolation

Main runtime dependencies are declared in pyproject.toml: numpy, scipy, pandas, joblib, numba, and tqdm.

Numba is the only CPU parallel execution mechanism. Target collections are traversed sequentially, while sufficiently large numerical profile-alignment kernels distribute independent profile rows across Numba threads. Small workloads automatically use the serial kernel to avoid scheduling overhead. --jobs 1 forces serial numerical execution, --jobs -1 uses the Numba runtime maximum, and the Python API value n_jobs=None preserves the current Numba thread mask. The previous thread mask is restored after every public comparison call, including failed calls.

joblib is not used to schedule comparisons. It remains a dependency only for compatible model and trusted null distribution serialization; loading pickle/joblib data from untrusted sources is unsafe.

Quick Start

Compare two precomputed score profiles:

mimosa profile examples/scores_1.fasta examples/scores_2.fasta \
  --model1-type scores \
  --model2-type scores \
  --metric cosine

Compare two motifs through normalized score profiles:

mimosa profile examples/gata2.meme examples/gata4.meme \
  --model1-type pwm \
  --model2-type pwm \
  --fasta examples/foreground.fa \
  --background examples/background.fa \
  --metric co \
  --min-logfpr 2 \
  --window-radius 6

Compare two motifs directly:

mimosa motif examples/pif4.meme examples/gata2.meme \
  --model1-type pwm \
  --model2-type pwm \
  --metric pcc

Compare motifs from different model families in motif mode:

mimosa motif examples/sitega_stat6.mat examples/pif4.meme \
  --model1-type sitega \
  --model2-type pwm \
  --metric pcc \
  --pfm-mode

Build and use a stored null distribution file:

mimosa build-null motifs.meme \
  --model-type pwm \
  --groups groups.tsv \
  --strategy motif \
  --metric pcc \
  --progress \
  --output motifs-pcc.null.joblib

mimosa motif examples/gata2.meme examples/gata4.meme \
  --model1-type pwm \
  --model2-type pwm \
  --metric pcc \
  --pvalue \
  --null-distribution motifs-pcc.null.joblib

Clear cached normalized profiles:

mimosa cache clear --cache-dir .mimosa-cache

Python API

Use the top-level mimosa package for library integration. The stable public API includes model loading, scanning, comparison, and model-handler registration:

from mimosa import compare_one_to_one, read_model, scan_model

query = read_model("query.meme", "pwm")
target = read_model("target.xml", "dimont")

scores = scan_model(query, sequences, strand="best")
result = compare_one_to_one(query, target, strategy="profile", sequences=sequences)

MIMOSA accepts either paths plus model-type keys or preloaded GenericModel instances:

from mimosa import GenericModel, compare_one_to_many

query = GenericModel("pwm", "query", representation, length, {"kmer": 1, "_source_pfm": pfm})
results = compare_one_to_many(query, targets, strategy="profile", sequences=sequences)

For long one-vs-many runs, pass progress=True to render a tqdm progress bar on stderr without changing the returned results:

results = compare_one_to_many(query, targets, strategy="profile", sequences=sequences, progress=True)

External model families should be integrated through register_model_handler. A handler is an adapter bundle with scan, optional scan_both, load, write, and score_bounds callables. Functions in mimosa.handlers whose names start with _ are internal implementation details and should not be imported by downstream projects.

from mimosa import GenericModel, register_model_handler


def load_custom(path: str, kwargs: dict) -> GenericModel:
    return GenericModel("custom", "custom_name", representation, length, {"kmer": 1})


register_model_handler(
    "custom",
    scan=scan_custom,
    scan_both=None,
    load=load_custom,
    write=write_custom,
    score_bounds=custom_score_bounds,
)

CLI Overview

The CLI exposes four top-level commands:

• mimosa profile
• mimosa motif
• mimosa build-null
• mimosa cache clear

Comparison, null-building, and cache maintenance commands print one JSON object to stdout. Logs and progress bars are written to stderr, so JSON output remains safe to pipe into tools such as jq.

Progress bars are controlled by --progress and --no-progress. The default is automatic: progress is shown only when stderr is an interactive terminal. Use --progress to force bars for long local runs, or --no-progress for CI and log files. -v/--verbose enables additional logging; when progress is active, log lines are emitted through tqdm.write(...) so they do not corrupt the active bar.

Typical profile result:

{
  "query": "model_or_profile_1",
  "target": "model_or_profile_2",
  "score": 0.0,
  "offset": 0,
  "orientation": "++",
  "metric": "co",
  "n_sites": 0
}

Typical motif result:

{
  "query": "model_1",
  "target": "model_2",
  "score": 0.0,
  "offset": 0,
  "orientation": "++",
  "metric": "pcc"
}

If --pvalue is used with a compatible null distribution file, the result may also include:

• p-value
• adj.p-value
• E-value
• null_id
• null_n
• null_estimator

orientation is one of ++, +-, -+, --. offset is target_start - query_start in the selected oriented alignment for both profile and motif modes.

profile Mode

profile is the general-purpose workflow for comparing positional score signals.

It supports:

• direct scores vs scores comparisons
• motif-vs-motif comparisons through sequence scanning
• empirical score normalization with background calibration
• optional on-disk caching of normalized profile bundles
• p-value annotation from compatible stored null distribution files

If motif scanning is required and --fasta is omitted, MIMOSA generates random A/C/G/T sequences with:

• --num-sequences default 1000
• --seq-length default 200

If --background is provided, normalization is fitted on those sequences and applied to the comparison set. If omitted, normalization is fitted on the same sequence set used for comparison. The hidden legacy alias --promoters is still accepted for compatibility, but --background is the documented name.

Supported metrics:

• co
• co_rowwise
• dice
• dice_rowwise
• cosine

Important arguments:

Flag	Meaning
--model1-type, --model2-type	scores, pwm, bamm, sitega, dimont, slim
--fasta	FASTA sequences used to scan motif inputs
--background	FASTA used to calibrate empirical profile normalization
--num-sequences	number of random sequences when --fasta is omitted
--seq-length	random sequence length when --fasta is omitted
--metric	co, co_rowwise, dice, dice_rowwise, or cosine
--search-range	maximum motif shift explored, default 10
--window-radius	site-centered half-window size, default 10
--realign-window	local target-anchor realignment half-width, default 3
--min-logfpr	empirical log-tail threshold for anchors; omitted or 0 uses one best anchor per sequence
--cache	off or on
--cache-dir	cache directory, default .mimosa-cache
--pvalue	annotate using a compatible stored null distribution file
--null-distribution	explicit trusted null distribution file path from mimosa build-null
--null-search-dir	repeatable additional null distribution file search directory
--effective-number-of-targets	override E-value target count
--seed	random seed, default 127
--jobs	Numba threads per numerical kernel; 1 is serial and -1 is the runtime maximum
--progress, --no-progress	show or suppress progress bars on stderr; default auto-detects terminals
-v, --verbose	verbose logging

Example with two motif models:

mimosa profile examples/myog.ihbcp examples/pif4.meme \
  --model1-type bamm \
  --model2-type pwm \
  --metric co

motif Mode

motif compares motif representations directly.

Supported metrics:

• pcc
• ed
• cosine

Execution rules:

• if model types match and --pfm-mode is not enabled, MIMOSA aligns native matrices or tensors directly
• if model types differ, MIMOSA reconstructs PFMs from sequences automatically
• --pfm-mode forces PFM reconstruction even when both inputs use the same model family

If PFM reconstruction is required and --fasta is omitted, MIMOSA generates random A/C/G/T sequences with:

• --num-sequences default 20000
• --seq-length default 100

Important arguments:

Flag	Meaning
--model1-type, --model2-type	pwm, bamm, sitega, dimont, slim
--fasta	optional FASTA for PFM reconstruction
--num-sequences	number of random sequences when --fasta is omitted
--seq-length	random sequence length when --fasta is omitted
--metric	pcc, ed, or cosine
--pfm-mode	force sequence-driven PFM reconstruction
--pfm-top-fraction	top fraction of reconstructed hits used for PFM building, default 0.05
--pvalue	annotate using a compatible stored null distribution file
--null-distribution	explicit trusted null distribution file path from mimosa build-null
--null-search-dir	repeatable additional null distribution file search directory
--effective-number-of-targets	override E-value target count
--seed	random seed, default 127
--jobs	Numba threads per numerical kernel; direct motif alignment remains serial
--progress, --no-progress	show or suppress progress bars on stderr; default auto-detects terminals
-v, --verbose	verbose logging

Example:

mimosa motif examples/sitega_gata2.mat examples/pif4.meme \
  --model1-type sitega \
  --model2-type pwm \
  --metric ed \
  --pfm-mode

build-null Mode

mimosa build-null creates a trusted joblib null distribution file with one pooled null distribution from a motif collection and a relation input. Directory collections are loaded deterministically for any motif type when --model-type is provided; multi-motif MEME collections are supported for --model-type pwm. Motif names must be unique.

A group relation table is required. --groups must point to a table with motif-name and group columns; targets from different groups are used as null targets.

For --strategy profile, build-null uses FASTA sequences or generates random sequences with the profile defaults (--num-sequences 1000, --seq-length 200). For --strategy motif, direct same-family matrix/tensor comparison does not need sequences. If --pfm-mode is enabled, sequence-driven PFM reconstruction uses the same --fasta or random sequence options.

Important arguments:

Flag	Meaning
motifs	directory collection or multi-motif MEME file
--model-type	pwm, bamm, sitega, dimont, or slim
--pattern	optional glob for directory collections
--groups	TSV/CSV motif-to-group table
--name-column, --group-column	column names for --groups, defaults motif and group
--ignore-missing-relations	ignore relation names absent from the loaded collection
--strategy	profile or motif
--metric	defaults to co for profile and pcc for motif
--fasta, --background	sequence inputs for profile scoring or --pfm-mode reconstruction
--search-range, --window-radius, --realign-window, --min-logfpr	profile comparator options
--pfm-mode, --pfm-top-fraction	PFM reconstruction options for motif nulls
--cache, --cache-dir	profile cache options during null building
--output	output .joblib null distribution file path
--install-cache	also copy the null distribution file into ~/.cache/mimosa/nulls
--strict	fail when a query has too few null targets
--min-null-targets	minimum number of null targets per query, default 1
--seed, --jobs	random seed and Numba thread budget
--progress, --no-progress	show or suppress query and target progress bars on stderr; default auto-detects terminals

Group-table example:

mimosa build-null H14CORE_meme_format.meme \
  --model-type pwm \
  --groups H14CORE_annotation.tsv \
  --name-column motif \
  --group-column family \
  --strategy motif \
  --metric pcc \
  --progress \
  --output hocomoco-pwm-motif-pcc.null.joblib

Using the null distribution file:

mimosa motif examples/gata2.meme examples/gata4.meme \
  --model1-type pwm \
  --model2-type pwm \
  --metric pcc \
  --pvalue \
  --null-distribution hocomoco-pwm-motif-pcc.null.joblib

If --install-cache was used while building the null distribution file, later comparisons can omit --null-distribution and search the user cache automatically when --pvalue is enabled. Use --null-search-dir for additional project-local file directories.

The build command prints a JSON summary with the null distribution file path, optional cache path, number of motifs loaded, number of queries used in the pooled distribution, skipped queries, and total comparisons run.

cache Command

mimosa cache clear removes derived profile artifacts created by mimosa profile --cache on.

mimosa cache clear --cache-dir .mimosa-cache

The command prints:

{
  "cache_dir": ".mimosa-cache",
  "removed": 0
}

Python API

The public API is exported from src/mimosa/init.py.

High-level comparison helpers:

• compare_one_to_one(...)
• compare_one_to_many(...)
• create_one_to_one_config(...)
• create_one_to_many_config(...)
• run_one_to_one(...)
• run_one_to_many(...)
• create_comparator_config(...)
• compare(...)
• validate_metric(...)

Model and scanning helpers:

• read_model(...)
• scan_model(...)
• get_scores(...)
• get_frequencies(...)
• get_sites(...)
• get_pfm(...)
• register_model_handler(...)

Strand-aware scanning and site helpers accept strand="best", "+", "-", or "both". "best" collapses the two strands by taking the best score per position, while "both" keeps + and - predictions as separate observations for threshold calibration and site extraction. Threshold-table calibration and site/PFM reconstruction use "both" by default.

Types and utility exports:

• GenericModel
• StrandMode
• ComparisonResult
• OneToOneConfig
• OneToManyConfig
• ComparatorConfig
• clear_cache(...)
• read_models(...)
• build_null_distributions(...)
• load_null_distribution_file(...)
• save_null_distribution_file(...)
• relation parser: parse_group_relations(...)

Single comparison example:

from mimosa import compare_one_to_one

result = compare_one_to_one(
    "examples/gata2.meme",
    "examples/gata4.meme",
    query_type="pwm",
    target_type="pwm",
    strategy="profile",
    sequences="examples/foreground.fa",
    background="examples/background.fa",
    metric="co",
    min_logfpr=2.0,
)

One-vs-many example:

from mimosa import compare_one_to_many

results = compare_one_to_many(
    "examples/pif4.meme",
    [
        "examples/gata2.meme",
        "examples/foxa2.meme",
    ],
    query_type="pwm",
    target_type="pwm",
    strategy="motif",
    metric="pcc",
    progress=True,
)

progress=True is a runtime-only API option. It does not become part of ComparatorConfig, cache keys, or null distribution compatibility signatures.

Site extraction and PFM reconstruction example:

from mimosa import get_pfm, get_sites, read_model
from mimosa.io import read_fasta

model = read_model("examples/pif4.meme", "pwm")
sequences = read_fasta("examples/foreground.fa")

sites = get_sites(model, sequences, mode="best", strand="both")
pfm = get_pfm(model, sequences, mode="best", strand="both", top_fraction=0.05)

Extension Hooks

Custom model families can be registered at runtime through register_model_handler(...). A handler bundle provides:

• load
• scan
• optional scan_both
• write
• score_bounds

This makes it possible to integrate new motif representations without changing the public comparison API.

Development

Common local commands:

uv sync --group dev
uv run pytest
uv run ruff check .
uv run ruff format --check .

Focused test commands:

uv run pytest tests/test_unit.py
uv run pytest tests/test_integration.py

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