NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
J Proteomics. 2018 Sep 15;187:144-151. doi: 10.1016/j.jprot.2018.07.004. Epub 2018 Jul 23.
Study of single amino acid variations (SAVs) of proteins, resulting from single nucleotide polymorphisms, is of great importance for understanding the relationships between genotype and phenotype. In mass spectrometry based shotgun proteomics, identification of peptides with SAVs often suffers from high error rates on the variant sites detected. These site errors are due to multiple reasons and can be confirmed by manual inspection or genomic sequencing. Here, we present a software tool, named SAVControl, for site-level quality control of variant peptide identifications. It mainly includes strict false discovery rate control of variant peptide identifications and variant site verification by unrestrictive mass shift relocalization. SAVControl was validated on three colorectal adenocarcinoma cell line datasets with genomic sequencing evidences and tested on a colorectal cancer dataset from The Cancer Genome Atlas. The results show that SAVControl can effectively remove false detections of SAVs.
Protein sequence variations caused by single nucleotide polymorphisms (SNPs) are single amino acid variations (SAVs). The investigation of SAVs may provide a chance for understanding the relationships between genotype and phenotype. Mass spectrometry (MS) based proteomics provides a large-scale way to detect SAVs. However, using the current analysis strategy to detect SAVs may lead to high rate of false positives. The SAVControl we present here is a computational workflow and software tool for site-level quality control of SAVs detected by MS. It accesses the confidence of detected variant sites by relocating the mass shift responsible for an SAV to search for alternative interpretations. In addition, it uses a strict false discovery rate control method for variant peptide identifications. The advantages of SAVControl were demonstrated on three colorectal adenocarcinoma cell line datasets and a colorectal cancer dataset. We believe that SAVControl will be a powerful tool for computational proteomics and proteogenomics.
研究蛋白质中由单核苷酸多态性引起的单个氨基酸变异（SAVs）对于理解基因型和表型之间的关系非常重要。在基于质谱的鸟枪法蛋白质组学中，对具有 SAV 的肽的鉴定通常会在检测到的变异位点上遭受高错误率。这些位点错误有多种原因，可以通过手动检查或基因组测序来确认。在这里，我们提出了一种名为 SAVControl 的软件工具，用于变体肽鉴定的位点级质量控制。它主要包括严格控制变体肽鉴定的错误发现率，以及通过无限制的质量位移重新定位来验证变体位点。SAVControl 在具有基因组测序证据的三个结直肠腺癌细胞系数据集上进行了验证，并在来自癌症基因组图谱的结直肠癌数据集上进行了测试。结果表明，SAVControl 可以有效地消除 SAV 的假检测。
由单核苷酸多态性（SNP）引起的蛋白质序列变异是单个氨基酸变异（SAVs）。对 SAVs 的研究可能为理解基因型和表型之间的关系提供机会。基于质谱（MS）的蛋白质组学提供了一种大规模检测 SAVs 的方法。然而，使用当前的分析策略来检测 SAVs 可能会导致假阳性率很高。我们在这里提出的 SAVControl 是一种用于 MS 检测的 SAV 位点级质量控制的计算工作流程和软件工具。它通过重新定位负责 SAV 的质量位移来访问检测到的变异位点的置信度，以寻找替代解释。此外，它还使用严格的错误发现率控制方法来控制变体肽的鉴定。SAVControl 在三个结直肠腺癌细胞系数据集和一个结直肠癌数据集上的优势得到了证明。我们相信 SAVControl 将成为计算蛋白质组学和蛋白质基因组学的有力工具。
