Bioinformatics Unit, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland.
Bioinformatics. 2018 Feb 15;34(4):693-694. doi: 10.1093/bioinformatics/btx573.
Global centering-based normalization is a commonly used normalization approach in mass spectrometry-based label-free proteomics. It scales the peptide abundances to have the same median intensities, based on an assumption that the majority of abundances remain the same across the samples. However, especially in phosphoproteomics, this assumption can introduce bias, as the samples are enriched during sample preparation which can mask the underlying biological changes. To address this possible bias, phosphopeptides quantified in both enriched and non-enriched samples can be used to calculate factors that mitigate the bias.
We present an R package phosphonormalizer for normalizing enriched samples in label-free mass spectrometry-based phosphoproteomics.
The phosphonormalizer package is freely available under GPL ( > =2) license from Bioconductor (https://bioconductor.org/packages/phosphonormalizer).
sohrab.saraei@utu.fi or laura.elo@utu.fi.
Supplementary data are available at Bioinformatics online.
基于全局中心化的标准化是基于质谱的无标记蛋白质组学中常用的标准化方法。它根据大多数丰度在样本之间保持不变的假设，将肽丰度缩放到具有相同中位数强度。然而，特别是在磷酸化蛋白质组学中，这种假设可能会引入偏差，因为在样品制备过程中会富集样品，这可能会掩盖潜在的生物学变化。为了解决这种可能的偏差，可以使用在富集和非富集样品中定量的磷酸肽来计算减轻偏差的因素。
我们提出了一个用于无标记基于质谱的磷酸化蛋白质组学中富集样品的 R 包 phosphonormalizer。
phosphonormalizer 包根据 GPL（>=2）许可证免费提供，可从 Bioconductor（https://bioconductor.org/packages/phosphonormalizer）获得。
sohrab.saraei@utu.fi 或 laura.elo@utu.fi。
补充数据可在 Bioinformatics 在线获得。
