What time is it? Deep learning approaches for circadian rhythms.
What time is it? Deep learning approaches for circadian rhythms.
复制标题
现在是几奌？昼夜节律的深度学习方法。
DOI：
10.1093/bioinformatics/btw243
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发表时间：
2016-06-15
期刊：
影响因子：
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通讯作者：
Baldi P
中科院分区：
文献类型：
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作者：
Agostinelli F;Ceglia N;Shahbaba B;Sassone-Corsi P;Baldi P
动机：昼夜节律可以追溯到生命的起源，几乎存在于每个物种和每个细胞中，并在从新陈代谢到认知的各种功能中发挥着基本作用。现代高通量技术允许沿着昼夜节律周期测量转录本、代谢物和其他物种的浓度，这带来了新的计算挑战和机遇，包括推断给定物种是否以昼夜节律方式振荡的问题，以及推断进行一组测量的时间。结果：我们首先整理了几个大型的合成和生物时间序列数据集，其中包含周期和非周期信号的标签。然后，我们使用深度学习方法来开发和训练BIO_CYCLE，这是一个系统，用于稳健地估计高通量昼夜节律实验中哪些信号是周期性的，产生幅度，周期，阶段的估计，以及几个统计显著性措施。使用整理的数据，BIO_CYCLE与其他方法进行了比较，并显示出在多个指标上实现了最先进的性能。然后，我们使用深度学习方法来开发和训练BIO_CLOCK，以稳健地估计进行特定单时间点转录组实验的时间。在大多数情况下，BIO_CLOCK可以可靠地预测时间，大约在1小时内，只使用少数核心时钟基因的表达水平。BIO_CLOCK被证明在不同的组织类型中都能很好地工作，而且在不同的条件下通常只有很小的退化。BIO_CLOCK用于用推断的时间戳注释GEO数据库中发现的大多数小鼠实验。可用性和实施：所有数据和软件都可以在circadomics网站上公开获取：circadomics .igb.uci.edu/。联系方式：fagostin@uci.edu或pfbaldi@uci.edu补充信息：补充数据可在Bioinformatics在线获取。
Motivation: Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. Results: We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. Availability and Implementation: All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/. Contacts: fagostin@uci.edu or pfbaldi@uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.