ArticleBMC bioinformatics2026
A probabilistic approach for predicting indole-3-acetic acid synthesis in bacteria using genomic data.
Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIndole-3-acetic acid (IAA) is a crucial plant hormone for growth and development. Among bacteria capable of IAA synthesis, tryptophan-dependent pathways are the primary pathway for IAA production. Identifying IAA-producing bacteria is essential for understanding plant–microbe interactions and developing sustainable agricultural practices. With the advancements in sequencing technologies which enable the acquisition of genomic data in a short amount of time at a reduced cost. This study developed a probabilistic framework to predict the likelihood of IAA synthesis in bacteria using their genomes, which offers a cost-effective screening approach compared to traditional wet-lab methods.
resultsThe prediction framework incorporates four tryptophan-dependent pathways and consists of two models. The Binary Model provides a binary prediction of IAA synthesis, and the Logistic Model ranks bacteria based on their synthesis potential. The models were validated against a comprehensive dataset of more than 15,000 bacterial genomes, and the results suggest that only 1.7% of the bacteria have the potential to synthesize IAA. The most promising phyla are Actinobacteria, Proteobacteria, and Firmicutes. The Logistic Model shows that the top-ranked genera include Streptomyces, Paraburkholderia, Nonomuraea, Rhodococcus, and Nocardia. The model also suggests that the potential for IAA synthesis is likely to be strain-specific, emphasizing the importance of analyzing individual strains within a genus.
conclusionThe proposed probabilistic prediction framework is a cost-effective in-silico approach for identifying bacteria with a high potential for IAA synthesis. This serves as a pre-screening tool for resource-intensive downstream wet-lab validation, significantly reducing the time and effort required to identify potential plant growth-promoting bacteria. Furthermore, the model can be customized for other pathways of interest, and making it a versatile framework to explore various bacterial metabolic processes. The source code of the model is freely available at https://github.com/ComputationalAgronomy/bcpip
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