Sperschneider Jana, Dodds Peter N, Taylor Jennifer M, Duplessis Sébastien
Centre for Environmental and Life Sciences, CSIRO Agriculture and Food, Underwood Avenue, Floreat, WA, Australia.
Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, Australia.
Methods Mol Biol. 2017;1659:73-83. doi: 10.1007/978-1-4939-7249-4_7.
Lower costs and improved sequencing technologies have led to a large number of high-quality rust pathogen genomes and deeper characterization of gene expression profiles during early and late infection stages. However, the set of secreted proteins expressed during infection is too large for experimental investigations and contains not only effectors but also proteins that play a role in niche colonization or in fighting off competing microbes. Therefore, accurate computational prediction is essential for identifying high-priority rust effector candidates from secretomes.
成本的降低和测序技术的改进，已产生了大量高质量的锈菌病原体基因组，并对早期和晚期感染阶段的基因表达谱进行了更深入的表征。然而，感染过程中表达的分泌蛋白集对于实验研究来说太大了，并且不仅包含效应子，还包含在生态位定殖或抵御竞争性微生物中起作用的蛋白质。因此，准确的计算预测对于从分泌蛋白组中识别高优先级的锈菌效应子候选物至关重要。
