Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Methods. 2013 Jun 15;61(3):219-26. doi: 10.1016/j.ymeth.2013.03.004. Epub 2013 Mar 13.
Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics.
基于质谱的蛋白质组学技术的进步提高了单一分析的速度和深度。用于评估这些高通量分析中肽鉴定准确性的计算工具并没有跟上技术进步的步伐；目前最常用的质量评估方法是基于对大规模数据集误报鉴定可能性的统计分析。虽然这些计算方法很有帮助，但它们没有考虑每个鉴定的准确性，因此给依赖这些数据来指导实验设计的生物学家带来了不稳定的局面。手动验证是确认数据库鉴定准确性的金标准方法，但非常耗时。为了缓解手动验证大型蛋白质组数据集所需的时间不断增加，我们提供了计算机辅助手动验证软件 (CAMV) 来加速该过程。相关谱图被收集、编目和预先标记，允许用户有效地判断每个鉴定的质量，并总结适用的定量信息。CAMV 显著减轻了手动验证的负担，并有望鼓励更广泛地采用基于质谱的蛋白质组学中的手动验证。
