生物标志物发现
质谱法
蛋白质组学
生物标志物
管道(软件)
无标记量化
选择性反应监测
串联质谱法
计算生物学
蛋白质组
计算机科学
数据挖掘
生物信息学
化学
定量蛋白质组学
色谱法
生物
生物化学
基因
程序设计语言
作者
Qin Fu,Manasa Vegesna,Niveda Sundararaman,Eugen Damoc,Tabiwang N. Arrey,Anna Pashkova,Emebet Mengesha,Philip Debbas,Sandy Joung,Dalin Li,Susan Cheng,Jonathan Braun,Dermot Govern,Christopher I. Murray,Yue Xuan,Jennifer E. Van Eyk
标识
DOI:10.1002/anie.202409446
摘要
Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data‐independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA‐MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra‐protein correlation based on 8‐ or 11‐point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.
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