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Navigating the landscape of direct cellular reprogramming with DiReG.

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Τίτλος: Navigating the landscape of direct cellular reprogramming with DiReG.
Συγγραφείς: Lauber M; Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany., List M; Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany. markus.list@tum.de.; Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany. markus.list@tum.de.
Πηγή: NPJ systems biology and applications [NPJ Syst Biol Appl] 2026 Feb 06; Vol. 12 (1). Date of Electronic Publication: 2026 Feb 06.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Nature Publishing Group in partnership with SBI, The Systems Biology Institute Country of Publication: England NLM ID: 101677786 Publication Model: Electronic Cited Medium: Internet ISSN: 2056-7189 (Electronic) Linking ISSN: 20567189 NLM ISO Abbreviation: NPJ Syst Biol Appl Subsets: MEDLINE
Imprint Name(s): Original Publication: [London] : Nature Publishing Group in partnership with SBI, The Systems Biology Institute, [2015]-
Ιατρικοί όροι (MeSH): Cellular Reprogramming*/genetics , Cellular Reprogramming*/physiology , Computational Biology*/methods, Transcription Factors/metabolism ; Transcription Factors/genetics ; Regenerative Medicine/methods ; Humans ; Animals ; Software
Περίληψη: Direct cellular reprogramming, converting one differentiated cell type directly into another, holds immense promise for regenerative medicine, developmental biology, and disease modeling. Identifying optimal transcription factor (TF) combinations to control this process remains complex and labor-intensive. Over the last decade, various computational tools emerged to infer TF sets for reprogramming. However, current methodologies possess critical limitations, and the absence of robust benchmarking standards makes it impossible to precisely validate and compare their performance. To address these challenges, we present a comprehensive analysis of existing computational methods for direct reprogramming and introduce a web application designed to support researchers in identifying and validating optimal TF sets. Our platform integrates predictions from established tools, incorporates a state-of-the-art Retrieval-Augmented Generation (RAG) system for efficient literature querying, and offers tools to further validate predictions. By providing a unified and interactive resource, our web application enhances the accessibility and efficiency of TF discovery for direct reprogramming. Furthermore, we discuss critical limitations shared by current methodologies and highlight the need for computational tools that can account for the complex regulatory dynamics of direct reprogramming. This work not only advances the toolkit available to researchers but also lays the groundwork for future innovations aimed at realizing the full potential of direct reprogramming.
(© 2026. The Author(s).)
Competing Interests: Competing interests: The authors declare no competing interests.
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Substance Nomenclature: 0 (Transcription Factors)
Entry Date(s): Date Created: 20260206 Date Completed: 20260707 Latest Revision: 20260707
Update Code: 20260707
PubMed Central ID: PMC12988218
DOI: 10.1038/s41540-026-00652-z
PMID: 41651868
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2056-7189
DOI:10.1038/s41540-026-00652-z