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Atlas of Computational Cell Reprogramming

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L1 · Network-informed inverse design heuristics T

TRANSDIRE

Eguchi R, Hamano M, Iwata M, Nakamura T, Oki S, Yamanishi Y

2022 · Bioinformatics (Oxford, England)

MOTIVATION: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem…

Abstract

From the original paper, Bioinformatics (Oxford, England) · PubMed

MOTIVATION: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells). Cell differentiation by direct reprogramming is determined by two types of transcription factors (TFs): pioneer factors (PFs) and cooperative TFs. PFs have the distinct ability to open chromatin aggregations, assemble a collective of cooperative TFs and activate gene expression. The experimental determination of two types of TFs is extremely difficult and costly. RESULTS: In this study, we developed a novel computational method, TRANSDIRE (TRANS-omics-based approach for DIrect REprogramming), to predict the TFs that induce direct reprogramming in various human cell types using multiple omics data. In the algorithm, potential PFs were predicted based on low signal chromatin regions, and the cooperative TFs were predicted through a trans-omics analysis of genomic data (e.g. enhancers), transcriptome data (e.g. gene expression profiles in human cells), epigenome data (e.g. chromatin immunoprecipitation sequencing data) and interactome data. We applied the proposed methods to the reconstruction of TFs that induce direct reprogramming from fibroblasts to six other cell types: hepatocytes, cartilaginous cells, neurons, cardiomyocytes, pancreatic cells and Paneth cells. We demonstrated that the methods successfully predicted TFs for most cell conversions with high accuracy. Thus, the proposed methods are expected to be useful for various practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at the following website: http://figshare.com/s/b653781a5b9e6639972b. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary

Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how TRANSDIRE relates to the cross-modality inverse-design framework of the review.

Why this level

Level 1 because the method scores candidate interventions through static regulatory-network influence relative to a target GRN, without computing the post-intervention state P^S,u\hat P_{S,u}. Representation family is regulatory-network influence. Cited in §3.2 of the review. Editorial rationale pending review by the maintainer.

Classification

Level
L1
Representation
Regulatory-network influence
Modalities
T
Intervention
Transcription factors
Framework
Static network analysis

Software

Reproducibility
3 of 4
FAIR4RS
1 of 5

Last audited 2026-05-24

Citation

Eguchi R et al. (2022). TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach., Bioinformatics (Oxford, England).

DOI: 10.1093/bioinformatics/btac209

PMID: 35561200

BibTeX
@article{transdire2022,
  title  = {TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach.},
  author = {Eguchi R et al.},
  year   = {2022},
  journal = {Bioinformatics (Oxford, England)},
  pmid = {35561200},
  doi  = {10.1093/bioinformatics/btac209}
}