期刊
JOURNAL OF PROTEOME RESEARCH
卷 12, 期 12, 页码 5730-5741出版社
AMER CHEMICAL SOC
DOI: 10.1021/pr400705q
关键词
bioinformatics; mass spectrometry; computational proteomics; shotgun proteomics; mass fingerprinting; retention time prediction
资金
- Swedish Research Council
- U.S. National Science Foundation (MRI) [0923536]
- American Recovery and Reinvestment Act (ARRA) funds [R01 HG005805]
- National Institute of General Medical Sciences/Center for Systems Biology [2P50 GM076547]
- Luxembourg Centre for Systems Biomedicine
- University of Luxembourg
- Div Of Biological Infrastructure
- Direct For Biological Sciences [0923536] Funding Source: National Science Foundation
In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据