SDEAP: a splice graph based differential transcript expression analysis tool for population data
SDEAP: a splice graph based differential transcript expression analysis tool for population data
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DOI：
10.1093/bioinformatics/btw513
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发表时间：
2016-12
期刊：
影响因子：
5.8
通讯作者：
Ei-Wen Yang;Tao Jiang
中科院分区：
文献类型：
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作者：
Ei-Wen Yang;Tao Jiang
在没有预定条件的情况下进行差异转录表达（DTE）分析对于生物学研究至关重要。例如，它可用于发现生物标志物，将癌症样本分类为以前未知的亚型，以便为这些亚型开发更好的诊断和治疗方法。虽然已经发布了几种用于人口数据（即没有已知生物条件的数据）的DTE工具，但这些工具要么假设输入人口中的二元条件，要么要求将条件的数量作为输入的一部分。将条件数固定为二进制是不现实的，并且可能会扭曲DTE分析的结果。对于常规用户来说，估计群体中的正确状况数量也可能具有挑战性。此外，现有的工具只提供外显子的差异使用，这可能不足以解释跨样品的选择性剪接模式，并限制了许多生物学研究的工具的应用。结果我们提出了一种新的DTE分析算法，称为SDEAP，估计直接从输入样本的条件使用Dirichlet混合模型的数量，并发现选择性剪接事件使用一个新的图模块化分解算法。通过利用上述技术改进，SDEAP能够在我们对模拟数据和真实的数据进行的广泛实验中优于其他DTE分析方法，并进行qPCR验证。SDEAP的预测还使我们能够更准确地对癌症亚型和细胞周期阶段的样本进行分类。SDEAP的可用性和实施可在www.example.com上免费公开获得。联系方式：yyang027@cs.ucr.edu; jiang@cs.ucr.edu补充信息：补充数据可在生物信息学在线上获得。https://github.com/ewyang089/SDEAP/wiki
MOTIVATION Differential transcript expression (DTE) analysis without predefined conditions is critical to biological studies. For example, it can be used to discover biomarkers to classify cancer samples into previously unknown subtypes such that better diagnosis and therapy methods can be developed for the subtypes. Although several DTE tools for population data, i.e. data without known biological conditions, have been published, these tools either assume binary conditions in the input population or require the number of conditions as a part of the input. Fixing the number of conditions to binary is unrealistic and may distort the results of a DTE analysis. Estimating the correct number of conditions in a population could also be challenging for a routine user. Moreover, the existing tools only provide differential usages of exons, which may be insufficient to interpret the patterns of alternative splicing across samples and restrains the applications of the tools from many biology studies. RESULTS We propose a novel DTE analysis algorithm, called SDEAP, that estimates the number of conditions directly from the input samples using a Dirichlet mixture model and discovers alternative splicing events using a new graph modular decomposition algorithm. By taking advantage of the above technical improvement, SDEAP was able to outperform the other DTE analysis methods in our extensive experiments on simulated data and real data with qPCR validation. The prediction of SDEAP also allowed us to classify the samples of cancer subtypes and cell-cycle phases more accurately. AVAILABILITY AND IMPLEMENTATION SDEAP is publicly available for free at https://github.com/ewyang089/SDEAP/wiki CONTACT: yyang027@cs.ucr.edu; jiang@cs.ucr.eduSupplementary information: Supplementary data are available at Bioinformatics online.