Results

Vertical and horizontal gene transfer shaped plant colonization and biomass degradation in the fungal genus Armillaria
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Complete Genome sequence of Burkholderia phymatum STM815T, a broad host range and efficient nitrogen-fixing symbiont of Mimosa species
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A comparative genomics screen identifies a Sinorhizobium meliloti 1021 sodM-like gene strongly expressed within host plant nodules
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Analysis of twelve genomes of the bacterium Kerstersia gyiorum from brown-throated sloths (Bradypus variegatus), the first from a non-human host.
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Annotation survey and life-cycle transcriptomics of transcription factors in rust fungi (Pucciniales) identify a possible role for cold shock proteins in dormancy exit
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JGI Scientists Pen Genome Watch Articles
Microbiology column last year — and there’s more to come in 2020. Researchers at the U.S. Department of Energy (DOE) … And it makes sense to Woyke why JGI would have this role. “We work with leaders in environmental genomics from … researcher Sara Calhoun, data scientist Stephen Mondo, and  Fungal and Algal program lead Igor Grigoriev took readers on …
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Bacteria and Fungi Divvy Up the Work in Forest Floor
… dataset was particularly important for characterizing fungal genes, because, unlike bacteria, they often carry … , the team found that fungi are playing a bigger role in soil decomposition processes than expected. The … already suspected: these microbial groups play different roles in a healthy, forest soil ecosystem. Bacteria are …
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Intrinsic and Extrinsic Programming of Product Chain Length and Release Mode in Fungal Collaborating Iterative Polyketide Synthases
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Identification of Cellulose-Responsive Bacterial and Fungal Communities in Geographically and Edaphically Different Soils by Using Stable Isotope Probing
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Genetic Bases of Fungal White Rot Wood Decay Predicted by Phylogenomic Analysis of Correlated Gene-Phenotype Evolution
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Modulation of Plant and Fungal Gene Expression Upon Cd Exposure and Symbiosis in Ericoid Mycorrhizal Vaccinium myrtillus
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Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits
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