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Atlas of Computational Cell Reprogramming

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L1 · Network-informed inverse design heuristics I T

Taiji-reprogram

Wang J, Liu C, Chen Y, Wang W

2021 · NAR genomics and bioinformatics

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies.

Abstract

From the original paper, NAR genomics and bioinformatics · PubMed

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies. Identification of the key regulators defining the cell type specificity is pivotal to devising reprogramming cocktails for successful cell conversion but remains a great challenge. Here, we present a systems biology approach called Taiji-reprogram to efficiently uncover transcription factor (TF) combinations for conversion between 154 diverse cell types or tissues. This method integrates the transcriptomic and epigenomic data to construct cell-type specific genetic networks and assess the global importance of TFs in the network. Comparative analysis across cell types revealed TFs that are specifically important in a particular cell type and often tightly associated with cell-type specific functions. A systematic search of TFs with differential importance in the source and target cell types uncovered TF combinations for desired cell conversion. We have shown that Taiji-reprogram outperformed the existing methods to better recover the TFs in the experimentally validated reprogramming cocktails. This work not only provides a comprehensive catalog of TFs defining cell specialization but also suggests TF combinations for direct cell conversion.

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 Taiji-reprogram 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
I, T
Intervention
Transcription factors
Framework
Static network analysis

Software

Reproducibility
2 of 4
FAIR4RS
0 of 5

Last audited 2026-05-24

Citation

Wang J et al. (2021). Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails., NAR genomics and bioinformatics.

DOI: 10.1093/nargab/lqab100

PMID: 34761218

BibTeX
@article{taiji-reprogram2021,
  title  = {Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails.},
  author = {Wang J et al.},
  year   = {2021},
  journal = {NAR genomics and bioinformatics},
  pmid = {34761218},
  doi  = {10.1093/nargab/lqab100}
}