ArticleNature methods2022
Ranking reprogramming factors for cell differentiation.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- Transcription factor collaboration enables precise T cell state engineering.bioRxiv : the preprint server for biology · 2026Article
- ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.Cell genomics · 2026Article
- Navigating the landscape of direct cellular reprogramming with DiReG.NPJ systems biology and applications · 2026Article
- A convenient single-cell assay for the newly synthesized transcriptome reveals FLI1 regulon downregulation during T cell activation.Cell reports methods · 2026Article
- Chromatin-dependent motif syntax defines differentiation trajectories.Molecular cell · 2025Article
- TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data.Genome research · 2025Article
- Transcriptome Analysis Suggests PKD3 Regulates Proliferative Glucose Metabolism, Calcium Homeostasis and Microtubule Dynamics After MEF Spontaneous Immortalization.International journal of molecular sciences · 2025Article
- Multimodal Hox5 activity generates motor neuron diversity.Communications biology · 2024Article
- Transcriptome Profiling of Mouse Embryonic Fibroblast Spontaneous Immortalization: A Comparative Analysis.International journal of molecular sciences · 2024Article
- A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.eLife · 2024Article
- Transcriptomic signatures of progressive and regressive liver fibrosis and portal hypertension.iScience · 2024Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Denervation alters the secretome of myofibers and thereby affects muscle stem cell lineage progression and functionality.NPJ Regenerative medicine · 2024Article
- Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology.Development (Cambridge, England) · 2024Article
- Article
- The Clock:Cycle complex is a major transcriptional regulator of Drosophila photoreceptors that protects the eye from retinal degeneration and oxidative stress.PLoS genetics · 2022Article
- Benchmarking of ATAC Sequencing Data From BGI's Low-Cost DNBSEQ-G400 Instrument for Identification of Open and Occupied Chromatin Regions.Frontiers in molecular biosciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.