Abstract：
Proteomics, the study of proteins within biological systems, has seen remarkable advancements in recent years, with protein isoform detection emerging as one of the next major frontiers. One of the primary challenges is achieving the necessary peptide and protein coverage to confidently differentiate isoforms as a result of the protein inference problem and protein false discovery rate estimation challenge in large data. In this chapter, we describe the application of artificial intelligence-assisted peptide property prediction for database search engine rescoring by Oktoberfest, an approach that has proven effective, particularly for complex samples and extensive search spaces, which can greatly increase peptide coverage. Further, it illustrates a method for increasing isoform coverage by the PickedGroupFDR approach that is designed to excel when applied on large data. Real-world examples are provided to illustrate the utility of the tools in the context of rescoring, protein grouping, and false discovery rate estimation. By implementing these cutting-edge techniques, researchers can achieve a substantial increase in both peptide and isoform coverage, thus unlocking the potential of protein isoform detection in their studies and shedding light on their roles and functions in biological processes.
摘要：
蛋白质组学,研究生物系统中的蛋白质，近年来取得了显著进步，随着蛋白质同工型检测成为下一个主要领域之一。主要挑战之一是由于大数据中的蛋白质推断问题和蛋白质错误发现率估计挑战，实现必要的肽和蛋白质覆盖以自信地区分同种型。在这一章中,我们描述了人工智能辅助肽属性预测在Oktoberfest数据库搜索引擎评分中的应用，一种被证明有效的方法，特别是对于复杂的样本和广泛的搜索空间，这可以大大提高肽的覆盖率。Further,它说明了一种通过PickedGroupFDR方法增加同工型覆盖率的方法，该方法旨在应用于大型数据时表现出色。提供了真实世界的例子来说明工具在重新评分的背景下的效用，蛋白质分组，和错误发现率估计。通过实施这些尖端技术，研究人员可以实现肽和同工型覆盖率的大幅增加，从而在他们的研究中释放了蛋白质同工型检测的潜力，并揭示了它们在生物过程中的作用和功能。
