inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences

TL;DR

inMOTIFin is a lightweight, flexible Python software tool designed for simulating and modifying DNA regulatory sequences, aiding in the development and benchmarking of bioinformatics methods for transcriptional regulation analysis.

Contribution

It introduces a modular, user-friendly simulation platform that allows precise control over motif placement and sequence modification, filling gaps in existing tools.

Findings

Enables efficient simulation of regulatory sequences for method testing

Supports analysis of transcription factor cooperativity

Facilitates explainability in deep learning models for genomics

Abstract

The accurate development, assessment, interpretation, and benchmarking of bioinformatics frameworks for analyzing transcriptional regulatory grammars rely on controlled simulations to validate the underlying methods. However, existing simulators often lack end-to-end flexibility or ease of integration, which limits their practical use. We present inMOTIFin, a lightweight, modular, and user-friendly Python-based software that addresses these gaps by providing versatile and efficient simulation and modification of DNA regulatory sequences. inMOTIFin enables users to simulate or modify regulatory sequences efficiently for the customizable generation of motifs and insertion of motif instances with precise control over their positions, co-occurrences, and spacing, as well as direct modification of real sequences, facilitating a comprehensive evaluation of motif-based methods and interpretation tools. We demonstrate inMOTIFin applications for the assessment of de novo motif discovery prediction, the analysis of transcription factor cooperativity, and the support of explainability analyses for deep learning models. inMOTIFin ensures robust and reproducible analyses for studying transcriptional regulatory grammars. inMOTIFin is available at PyPI https://pypi.org/project/inMOTIFin/ and Docker Hub https://hub.docker.com/r/cbgr/inmotifin. Detailed documentation is available at https://inmotifin.readthedocs.io/en/latest/. The code for use case analyses is available at https://bitbucket.org/CBGR/inmotifin_evaluation/src/main/.