Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.
Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.
复制标题
DOI：
10.1371/journal.pcbi.1004871
复制
发表时间：
2016-05
影响因子：
4.3
通讯作者：
Schliep A
中科院分区：
文献类型：
--
作者：
Wiedenhoeft J;Brugel E;Schliep A
通过整合Haar小波与隐马尔可夫模型，我们实现了大大减少运行时间的贝叶斯推理使用前向-后向吉布斯采样。我们表明，这提高了检测基因组拷贝数变异（CNV）阵列CGH实验相比，国家的最先进的，包括标准吉布斯采样。该方法通过动态和自适应地重新计算可能共享拷贝数的连续观测块，将计算工作集中在难以调用的染色体片段上。这使得常规的诊断使用和重新分析遗留数据收集的可行性;为此，我们还提出了一个有效的自动先验。我们的方法的开源软件实现可在http://schlieplab.org/Software/HaMMLET/（DOI：10.5281/zenodo.46262）上获得。这篇论文被选为RECOMB 2016的口头报告，摘要发表在会议记录中。
By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.
影响因子：
14.9
作者：
Chen CH;Lee HC;Ling Q;Chen HR;Ko YA;Tsou TS;Wang SC;Wu LC;Lee HC
通讯作者：
Lee HC