Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721, United States.
J Proteome Res. 2011 Apr 1;10(4):1593-602. doi: 10.1021/pr100959y. Epub 2011 Feb 23.
To interpret LC-MS/MS data in proteomics, most popular protein identification algorithms primarily use predicted fragment m/z values to assign peptide sequences to fragmentation spectra. The intensity information is often undervalued, because it is not as easy to predict and incorporate into algorithms. Nevertheless, the use of intensity to assist peptide identification is an attractive prospect and can potentially improve the confidence of matches and generate more identifications. On the basis of our previously reported study of fragmentation intensity patterns, we developed a protein identification algorithm, SeQuence IDentfication (SQID), that makes use of the coarse intensity from a statistical analysis. The scoring scheme was validated by comparing with Sequest and X!Tandem using three data sets, and the results indicate an improvement in the number of identified peptides, including unique peptides that are not identified by Sequest or X!Tandem. The software and source code are available under the GNU GPL license at http://quiz2.chem.arizona.edu/wysocki/bioinformatics.htm.
在蛋白质组学中解释 LC-MS/MS 数据时，大多数流行的蛋白质鉴定算法主要使用预测的片段 m/z 值将肽序列分配给碎片谱。强度信息通常被低估，因为它不容易预测，也不容易纳入算法中。然而，利用强度来辅助肽鉴定是一个有吸引力的前景，有可能提高匹配的置信度，并产生更多的鉴定结果。基于我们之前报道的关于碎片强度模式的研究，我们开发了一种蛋白质鉴定算法 SeQuence IDentfication（SQID），该算法利用了来自统计分析的粗略强度。通过使用三个数据集与 Sequest 和 X!Tandem 进行比较，验证了评分方案，结果表明鉴定的肽数量有所增加，包括 Sequest 或 X!Tandem 未鉴定的独特肽。软件和源代码可在 http://quiz2.chem.arizona.edu/wysocki/bioinformatics.htm 下根据 GNU GPL 许可证获得。
