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m6Adecom: Analysis of m⁶A profile matrix based on graph regularized non-negative matrix factorization.

Rucong Liu¹, Leibo Liu¹, Yuan Zhou²

¹Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China.

Methods (San Diego, Calif.)

|January 29, 2022

Summary

This study introduces a new method using graph regularized non-negative matrix factorization (GNMF) for analyzing epitranscriptomic m⁶A methylation profiles. The method effectively distinguishes functional characteristics of m⁶A sites and is available via the m6Adecom tool.

Keywords:

Epitranscriptomic modification Graph regularized non-negative matrix factorization RNA binding protein Webserver m6A methylation

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Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

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Published on: April 1, 2017

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Epitranscriptomic N6-methyladenosine (m⁶A) methylation plays a crucial role in gene regulation, mediated by RNA binding proteins (RBPs).
The increasing availability of m⁶A data necessitates advanced analytical approaches for functional context-aware profiling.

Purpose of the Study:

To develop and validate a novel computational method for analyzing and comparing m⁶A methylation profiles.
To incorporate functional context, specifically RBP binding preferences, into m⁶A profile analysis.

Main Methods:

Employed graph regularized non-negative matrix factorization (GNMF) to analyze m⁶A profiles.
Integrated RBP binding preferences as a graph constraint within the GNMF framework.
Developed m6Adecom, an online tool for m⁶A profile correlation and enrichment analysis.

Main Results:

GNMF demonstrated superior performance over standard non-negative matrix factorization (NMF) in capturing functional distinctions among m⁶A sites.
The method successfully identified differences in associated biological pathways and disease genes.
m6Adecom provides a user-friendly platform for analyzing m⁶A data and performing gene set enrichment analysis.

Conclusions:

GNMF is an effective approach for context-aware analysis of m⁶A methylation profiles.
The m6Adecom tool facilitates deeper insights into the functional roles of m⁶A modifications.
This work enhances the understanding of epitranscriptomic regulation and its link to biological processes and diseases.