蛋白质组学
温柔
计算生物学
生物标志物发现
鉴定(生物学)
生物标志物
基因本体论
生物
生物信息学
计算机科学
基因
遗传学
食品科学
生态学
基因表达
作者
Mohammed Gagaoua,Claudia Terlouw,Anne Maria Mullen,Daniel Franco,Robyn D. Warner,José M. Lorenzo,Peter P. Purslow,David E. Gerrard,David Hopkins,D.J. Troy,Brigitte Picard
出处
期刊:Meat Science
[Elsevier BV]
日期:2020-09-20
卷期号:172: 108311-108311
被引量:120
标识
DOI:10.1016/j.meatsci.2020.108311
摘要
Over the last two decades, proteomics have been employed to decipher the underlying factors contributing to variation in the quality of muscle foods, including beef tenderness. One such approach is the application of high-throughput protein analytical platforms in the identification of meat quality biomarkers. To broaden our understanding about the biological mechanisms underpinning meat tenderization across a large number of studies, an integromics study was performed to review the current status of protein biomarker discovery targeting beef tenderness. This meta-analysis is the first to gather and propose a comprehensive list of 124 putative protein biomarkers derived from 28 independent proteomics-based experiments, from which 33 robust candidates were identified worthy of evaluation using targeted or untargeted data-independent acquisition proteomic methods. We further provide an overview of the interconnectedness of the main biological pathways impacting tenderness determination after multistep analyses including Gene Ontology annotations, pathway and process enrichment and literature mining, and specifically discuss the major proteins and pathways most often reported in proteomics research.
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