S2-PepAnalyst:预测植物小信号肽的网络工具

Kelly L. Vomo-Donfack, Mariem Abaach, Ana M. Luna, Grégory Ginot, Verónica G. Doblas, Ian Morilla
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引用次数: 0

摘要

小信号肽(SSPs)在植物生长、发育和胁迫反应中发挥着至关重要的作用。然而,由于 SSPs 结构的多样性和当前预测工具的局限性,准确鉴定和描述 SSPs 仍然具有挑战性。在此,我们介绍 S2-PepAnalyst,这是一种新型网络工具,旨在加强对植物中 SSPs 的预测。通过将全面的植物特异性数据集整合到机器学习模型中,S2-PepAnalyst 提供了多功能性,与现有工具相比,准确率平均提高了 99.5%,并且具有低假阴性率的可靠性。S2-PepAnalyst 为植物生物学家提供了重要资源,促进了植物多肽信号的新发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
S2-PepAnalyst: A Web Tool for Predicting Plant Small Signalling Peptides
Small signalling peptides (SSPs) play crucial roles in plant growth, development, and stress responses. However, accurately identifying and characterising SSPs remains challenging due to their structural diversity and the limitations of current prediction tools. Here, we introduce S2-PepAnalyst, a novel web tool designed to enhance the prediction of SSPs in plants. By integrating comprehensive plant-specific datasets into a machine learning model, S2-PepAnalyst offers versatility, improved accuracy of 99.5% on average, and reliability with a low rate of false negatives compared to existing tools. S2-PepAnalyst provides essential resources for plant biologists and facilitates new discoveries in plant peptide signalling.
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