基于贝叶斯的质谱数据分析方法
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摘要
伴随着人类基因组计划发展起来的基因组学为人类探索生命的原理起来划时代的重要作用。但是在其发展的同时,人们慢慢认识到只从基因水平上去探索生命的本质是完全不够的,需要从更根本的本质上去研究揭示生命现象,这样蛋白质组学应运而生。质谱作为一种有效的工具为科学家们研究蛋白质提供了很大的帮助。
本文首先介绍了目前主流的基于质谱的蛋白质分析流程和技术,并介绍了一些常用的基于质谱的蛋白质的算法,包括SEQUEST、MASCOT、X! Tandom中的算法。总结了蛋白质定量分析的两种策略同位素标记方法和无标记定量技术,并分析了他们的区别和各自的优点,介绍了目前基于质谱的蛋白质翻译后修饰发现与鉴定的常用算法。
现有的基于质谱的蛋白质鉴定算法各有千秋,各有各的优点。我们尝试利用机器学习并结合朴素贝叶斯理论对现有的算法进行整合。选取的机器学习方法包括SVM、LDA、logistic回归、KNN、贝叶斯置信网络、人工神经网络等方法。选取的分类特征包括SEQUEST算法中提供的多种参数。训练数据来自于18组已知的混合蛋白的质谱数据。通过机器学习的方法得到分类器的分界面,并计算阴阳极样本在分类器分类函数作用下的条件分布。利用阴阳极的条件分布和新样本在分类器下的特征得分,在均匀先验的条件下通过朴素贝叶斯的方法就可以计算出蛋白质鉴定结果的后验概率。通过交叉验证的结果表明我们的算法的正确率在80%-90%,同时可以保证召回率达到40%-50%,具有加好的实用价值。
蛋白质翻译后修饰的鉴定一直是蛋白质组研究里面一个重要的领域。通常的基于质谱的蛋白质翻译后修饰的鉴定的方法是机器学习和直接与已知数据库对比。与已知数据库对比的算法时间复杂度较高,同时因为比对的次数很多算法的假阳性率较高。我们尝试利用基于投影距离的聚类算法来对质谱数据先进行聚类分析,然后再在此基础上进行翻译后修饰的识别,这样不仅降低了算法的时间复杂度,而且也提高了精度。投影方向是利用已知样本基于LDA和SVM计算出来的,使得在投影方向上类内距离尽可能的小,类间的距离尽可能大。得到投影方向之后在通过对未知样本两两之间进行投影距离的计算得到距离矩阵。通过利用距离矩阵和常用的聚类算法对数据直接进行聚类分析。得到的聚类结果中的每一个类可能就是同一肽段的不同的翻译后修饰的实例,通过比较同一类内的结果可以快速高效的发现可能存在的翻译后修饰。在已知数据的交叉验证下算法的正确率和召回率都在70%左右
自从Google提出了云计算的概念,各种基于云计算应用层出不穷,蛋白质质谱数据分析具有高通量和可并行化的特点,可以方便的部署到云计算平台上。我们提出了两种部署策略并比较了两种策略的优点和不足。
The genomics, developing with the Human Genome Project (HGP), plays an imp ortant role in exploring the element of life. During this process, people realize that it i s inadequate to know the life on the genetic level, and then the proteomics grows up. The mass spectra technology is a kind of effective tools for scientists to study.
The main pipelines and technologies for analyzing proteins are introduced in this article and also the common algorithms for mass spectra are discussed, such as SEQUEST, MASCOT, X!Tandom. We also summarize the distinction and merit of two stratigies for quantitive analysis of proteins, the isotopic labeling and lable free method. Moreover, the common methods for discovering and identifing the post-translational modifications of proteins are depicted in this paper.
Becasue different algorithms for identifing proteins have their own qulities, we attempt to indegrate the existing methods by machine learning combining with naive bayes theory. The approaches of machine learing include SVM, LDA, Logistic Regression, KNN, Bayesian Network and Artificial Neural Networks. We choose the parameters used in SEQUEST as the characters for classification. The traing data sets come from the mass specture of known protein mixture divided by18teams. By machine learning, we acquire the interface of classifier and calculate the conditional distribution of positive and negtive samples with the classifing function. By the conditional distribution and the scores of features, we could count the posterior probabilities of idntification, utilizing the bayes methods on the priori homogeneous distribution. From the cross validation, the accuracy of our method could achieve between80%and90%and the recall could be between40%and50%, which accounts for the utility value of the novel method.
How to identify the post-translational modifications of proteins is always a key problem in proteomics. The troditional algorithm for identifing the unknown proteins by mass spectra is to search the protein database employing the methods of machine learning. However, it is time-consuming and the false positive rate will increase. We try to classify the mass spectra using the projection distance and then discover the post-translational modifications, which could not only decrease the time complexity, but also improve the accuracy. The projecting direction is calculated by LDA and SVM, which making the distance in classter smaller and out classter bigger. We get the distance matrix by projection and perform the classification with certain classtering algorithms. The peptides in the same class may derive from the same protein with different post-translational modifications. Comparing the peptides in the same class, we could find the latent post-translational modifications more quickly and efficiently. By cross validation, the accuracy and recall could both reach about70%.
Since the concept of cloud computing is put forward by google company, many kinds of distributed cloud computings have emerged. Due to the characters of high flux and parallelism, the calculation of proteomics could assign to the cloud computing platform. So we suggest two kinds of strategies and tell the advantages and defects of them.
本文首先介绍了目前主流的基于质谱的蛋白质分析流程和技术,并介绍了一些常用的基于质谱的蛋白质的算法,包括SEQUEST、MASCOT、X! Tandom中的算法。总结了蛋白质定量分析的两种策略同位素标记方法和无标记定量技术,并分析了他们的区别和各自的优点,介绍了目前基于质谱的蛋白质翻译后修饰发现与鉴定的常用算法。
现有的基于质谱的蛋白质鉴定算法各有千秋,各有各的优点。我们尝试利用机器学习并结合朴素贝叶斯理论对现有的算法进行整合。选取的机器学习方法包括SVM、LDA、logistic回归、KNN、贝叶斯置信网络、人工神经网络等方法。选取的分类特征包括SEQUEST算法中提供的多种参数。训练数据来自于18组已知的混合蛋白的质谱数据。通过机器学习的方法得到分类器的分界面,并计算阴阳极样本在分类器分类函数作用下的条件分布。利用阴阳极的条件分布和新样本在分类器下的特征得分,在均匀先验的条件下通过朴素贝叶斯的方法就可以计算出蛋白质鉴定结果的后验概率。通过交叉验证的结果表明我们的算法的正确率在80%-90%,同时可以保证召回率达到40%-50%,具有加好的实用价值。
蛋白质翻译后修饰的鉴定一直是蛋白质组研究里面一个重要的领域。通常的基于质谱的蛋白质翻译后修饰的鉴定的方法是机器学习和直接与已知数据库对比。与已知数据库对比的算法时间复杂度较高,同时因为比对的次数很多算法的假阳性率较高。我们尝试利用基于投影距离的聚类算法来对质谱数据先进行聚类分析,然后再在此基础上进行翻译后修饰的识别,这样不仅降低了算法的时间复杂度,而且也提高了精度。投影方向是利用已知样本基于LDA和SVM计算出来的,使得在投影方向上类内距离尽可能的小,类间的距离尽可能大。得到投影方向之后在通过对未知样本两两之间进行投影距离的计算得到距离矩阵。通过利用距离矩阵和常用的聚类算法对数据直接进行聚类分析。得到的聚类结果中的每一个类可能就是同一肽段的不同的翻译后修饰的实例,通过比较同一类内的结果可以快速高效的发现可能存在的翻译后修饰。在已知数据的交叉验证下算法的正确率和召回率都在70%左右
自从Google提出了云计算的概念,各种基于云计算应用层出不穷,蛋白质质谱数据分析具有高通量和可并行化的特点,可以方便的部署到云计算平台上。我们提出了两种部署策略并比较了两种策略的优点和不足。
The genomics, developing with the Human Genome Project (HGP), plays an imp ortant role in exploring the element of life. During this process, people realize that it i s inadequate to know the life on the genetic level, and then the proteomics grows up. The mass spectra technology is a kind of effective tools for scientists to study.
The main pipelines and technologies for analyzing proteins are introduced in this article and also the common algorithms for mass spectra are discussed, such as SEQUEST, MASCOT, X!Tandom. We also summarize the distinction and merit of two stratigies for quantitive analysis of proteins, the isotopic labeling and lable free method. Moreover, the common methods for discovering and identifing the post-translational modifications of proteins are depicted in this paper.
Becasue different algorithms for identifing proteins have their own qulities, we attempt to indegrate the existing methods by machine learning combining with naive bayes theory. The approaches of machine learing include SVM, LDA, Logistic Regression, KNN, Bayesian Network and Artificial Neural Networks. We choose the parameters used in SEQUEST as the characters for classification. The traing data sets come from the mass specture of known protein mixture divided by18teams. By machine learning, we acquire the interface of classifier and calculate the conditional distribution of positive and negtive samples with the classifing function. By the conditional distribution and the scores of features, we could count the posterior probabilities of idntification, utilizing the bayes methods on the priori homogeneous distribution. From the cross validation, the accuracy of our method could achieve between80%and90%and the recall could be between40%and50%, which accounts for the utility value of the novel method.
How to identify the post-translational modifications of proteins is always a key problem in proteomics. The troditional algorithm for identifing the unknown proteins by mass spectra is to search the protein database employing the methods of machine learning. However, it is time-consuming and the false positive rate will increase. We try to classify the mass spectra using the projection distance and then discover the post-translational modifications, which could not only decrease the time complexity, but also improve the accuracy. The projecting direction is calculated by LDA and SVM, which making the distance in classter smaller and out classter bigger. We get the distance matrix by projection and perform the classification with certain classtering algorithms. The peptides in the same class may derive from the same protein with different post-translational modifications. Comparing the peptides in the same class, we could find the latent post-translational modifications more quickly and efficiently. By cross validation, the accuracy and recall could both reach about70%.
Since the concept of cloud computing is put forward by google company, many kinds of distributed cloud computings have emerged. Due to the characters of high flux and parallelism, the calculation of proteomics could assign to the cloud computing platform. So we suggest two kinds of strategies and tell the advantages and defects of them.
引文
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