ArticleBriefings in bioinformatics2024
PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- From binding to networks: methods for identifying transcription factor targets in plant systems.Frontiers in plant science · 2026Review
- PTF-Vāc: An explainable and generative deep co-learning encoder-decoder system for ab initio discovery of plant transcription factor binding sites.Plant communications · 2025Article
- DMRU: generative deep learning to unravel condition-specific cytosine methylation in plants.Briefings in bioinformatics · 2025Article
- Genome-wide identification, characterization, and expression pattern analysis of the glyoxalase gene family in Phyllostachys pubescens during abiotic stresses.BMC plant biology · 2025Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
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Authors and funding
5 authors.
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Abstract
Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitation has resulted into underdevelopment of plant regulatory research and rampant use of Arabidopsis-like model species, generating misleading results. Here, we report a revolutionary transformers-based deep-learning approach, PTFSpot, which learns from TF structures and their binding regions' co-variability to bring a universal TF-DNA interaction model to detect TFBR with complete freedom from TF and species-specific models' limitations. During a series of extensive benchmarking studies over multiple experimentally validated data, it not only outperformed the existing software by >30% lead but also delivered consistently >90% accuracy even for those species and TF families that were never encountered during the model-building process. PTFSpot makes it possible now to accurately annotate TFBRs across any plant genome even in the total lack of any TF information, completely free from the bottlenecks of species and TF-specific models.
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