High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics
摘要：
In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.
展开
DOI：
10.1016/j.porgcoat.2008.08.028
被引量：
年份：
2004
通过文献互助平台发起求助，成功后即可免费获取论文全文。
相似文献
参考文献
引证文献
来源期刊
引用走势
辅助模式
引用
文献可以批量引用啦~
欢迎点我试用！
