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

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Accurate analysis of transcriptional regulatory grammars requires controlled simulations for method validation.
• Existing simulation tools often lack flexibility and integration ease, hindering practical application.

Purpose of the Study:

• To introduce inMOTIFin, a user-friendly Python software for simulating and modifying DNA regulatory sequences.
• To provide a versatile and efficient tool for developing and assessing bioinformatics frameworks for transcriptional regulatory grammar analysis.

Main Methods:

• inMOTIFin offers modular and flexible simulation of regulatory sequences.
• It allows customizable generation of motifs, precise control over motif instances (position, co-occurrence, spacing), and direct modification of real sequences.
• The software is implemented in Python and available via PyPI and Docker Hub.

Main Results:

• inMOTIFin facilitates the creation of controlled datasets for evaluating motif discovery algorithms.
• It supports the analysis of transcription factor cooperativity and aids in explaining deep learning models for gene regulation.
• The software ensures robust and reproducible analyses of transcriptional regulatory grammars.

Conclusions:

• inMOTIFin addresses limitations of existing simulators, offering enhanced flexibility and ease of use.
• It is a valuable tool for the accurate development, assessment, and interpretation of bioinformatics frameworks for transcriptional regulatory grammars.
• The software promotes robust and reproducible research in the field.