噬菌体免疫沉淀测序数据中抗体反应性的检测。
Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data.
机构信息
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, USA.
出版信息
BMC Genomics. 2022 Sep 15;23(1):654. doi: 10.1186/s12864-022-08869-y.
Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR (Robinson et al., Bioinformatics 26(1):139-140, 2010) for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments (Bayesian Enrichment Estimation in R, BEER).
噬菌体免疫沉淀测序（PhIP-Seq）是一种最近开发的技术，用于评估抗体反应性，定量针对数十万候选表位的抗体结合。PhIP-Seq 实验的输出是读计数矩阵，类似于 RNA-Seq 数据；然而，确实存在一些重要的差异。在本手稿中，我们研究了公开可用的方法 edgeR（Robinson 等人，Bioinformatics 26(1):139-140, 2010）是否也适用于 PhIP-Seq 数据用于 RNA-Seq 数据的归一化和分析。我们发现 edgeR 非常有效，但可以进行改进，并引入了一个专门针对 PhIP-Seq 实验数据的贝叶斯框架（Bayesian Enrichment Estimation in R，BEER）。
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