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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Tech Univ Munich TUM Sch Life Sci Computat Mass Spectrometry Freising Weihenstephan Germany Tech Univ Munich Chair Prote & Bioanalyt TUM Sch Life Sci Freising Weihenstephan Germany Tech Univ Munich TUM Sch Life Sci Computat Mass Spectrometry D-85354 Freising Weihenstephan Germany
出 版 物:《PROTEOMICS》 (蛋白质组学)
年 卷 期:2024年第24卷第8期
页 面:2300112-2300112页
核心收录:
学科分类:0710[理学-生物学] 08[工学] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种]
基 金:Open access funding enabled and organized by Projekt DEAL
主 题:bioinformatics bottom-up proteomics data processing and analysis mass spectrometry LC-MS/MS technology
摘 要:Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub () and can easily be installed locally through the cross-platform PyPI Python package.