Mining gene functional networks to improve mass-spectrometry-based protein identification.
Mining gene functional networks to improve mass-spectrometry-based protein identification.
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DOI：
10.1093/bioinformatics/btp461
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发表时间：
2009-11-15
期刊：
影响因子：
--
通讯作者：
Miranker DP
中科院分区：
文献类型：
--
作者：
Ramakrishnan SR;Vogel C;Kwon T;Penalva LO;Marcotte EM;Miranker DP
动机：基于串联质谱（MS/MS）的高通量蛋白质鉴定实验通常遭受低灵敏度和低置信度的蛋白质鉴定。在一个典型的鸟枪蛋白质组学实验中，假设所有蛋白质都同样可能存在。然而，通常有其他证据表明存在蛋白质，并且可以相应地更新单个蛋白质鉴定的置信度。结果：我们开发了一种在细胞中活跃的生物过程的更大背景下分析MS/MS实验的方法。我们的方法，MSNet，提高蛋白质鉴定鸟枪蛋白质组学实验考虑从基因功能网络的功能协会的信息。MSNet在给定的错误率下大大增加了样本中识别的蛋白质数量。当应用于在不同的MS/MS仪器上分析的不同实验条件下生长的酵母时，我们识别出比原始MS实验多8-29%的蛋白质，并且在人类样品中多37%的蛋白质。我们在酵母中验证了高达94%的识别结果，这些识别结果存在于地面真实参考集中。可用性和实施：软件和数据集可在http://aug.csres.utexas.edu/msnet上获得联系：miranker@cs.utexas.edu，marcotte@icmb.utexas.edu补充信息：补充数据可在生物信息学在线上获得。
Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly. Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8–29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets. Availability and Implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnet Contact: miranker@cs.utexas.edu, marcotte@icmb.utexas.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
4.3
作者：
Guan Y;Myers CL;Lu R;Lemischka IR;Bult CJ;Troyanskaya OG
通讯作者：
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影响因子：
12.3
作者：
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通讯作者：
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DOI：
10.1073/pnas.0607084104
发表时间：
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影响因子：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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