Faster mass spectrometry-based protein inference: junction trees are more efficient than sampling and marginalization by enumeration.
Faster mass spectrometry-based protein inference: junction trees are more efficient than sampling and marginalization by enumeration.
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DOI：
10.1109/tcbb.2012.26
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
Noble WS
中科院分区：
文献类型：
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作者：
Serang O;Noble WS
使用串联质谱法识别复杂混合物中的蛋白质的问题可以被框定为将肽连接到蛋白质的图上的推理问题。几种现有的蛋白质鉴定方法利用图形模型的统计推断方法，包括期望最大化、马尔可夫链蒙特卡罗和完全边缘化与近似。我们发现，对于这个问题，大多数的推理成本通常来自于一些高度连接的子图。此外，我们评估了三种不同的统计推断方法，使用一个共同的图形模型，我们表明，连接树推理大大提高了收敛速度相比，现有的方法。本文使用的python代码可以在http://noble.gs.washington.edu/proj/fido上找到。
The problem of identifying the proteins in a complex mixture using tandem mass spectrometry can be framed as an inference problem on a graph that connects peptides to proteins. Several existing protein identification methods make use of statistical inference methods for graphical models, including expectation maximization, Markov chain Monte Carlo, and full marginalization coupled with approximation heuristics. We show that, for this problem, the majority of the cost of inference usually comes from a few highly connected subgraphs. Furthermore, we evaluate three different statistical inference methods using a common graphical model, and we demonstrate that junction tree inference substantially improves rates of convergence compared to existing methods. The python code used for this paper is available at http://noble.gs.washington.edu/proj/fido.