BLIND ordering of large-scale transcriptomic developmental timecourses
BLIND ordering of large-scale transcriptomic developmental timecourses
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DOI：
10.1242/dev.105288
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发表时间：
2014-03-01
期刊：
影响因子：
4.6
通讯作者：
Yanai, Itai
中科院分区：
文献类型：
--
作者：
Anavy, Leon;Levin, Michal;Yanai, Itai
RNA-Seq能够从少量材料中对许多样品进行有效的转录组测序，但这些数据的分析仍然具有挑战性。特别是，在发育研究中，RNA-Seq受到样品（如胚胎）的形态分期的挑战，因为这些样品在任何特定阶段都缺乏明确的标记。在这种情况下，自动识别样品的阶段将使以前不可行的实验设计成为可能。在这里，我们提出了“基本线性指数测定转录组”（BLIND）的方法，用于订购样品，包括不同的发展阶段。该方法是旅行商算法的实现，以根据由主成分分析定义的转录组之间的相互关系对转录组进行排序。为了建立有序样本的方向，我们表明，一个适当的指标是转录组基因表达水平的熵，随着发育时间的推移而增加。使用BLIND，我们正确地恢复了先前发表的青蛙，蚊子，苍蝇和斑马鱼的胚胎转录组时间过程的注释顺序。我们通过收集59个海绵Amphimedon queenslandica的胚胎并根据发育阶段对其转录组进行排序来进一步证明BLIND的功效。因此，BLIND在建立大型数据集内样本的时间顺序方面是有用的，并且对于具有异步发育的生物体的研究以及当形态分期困难时特别相关。
RNA-Seq enables the efficient transcriptome sequencing of many samples from small amounts of material, but the analysis of these data remains challenging. In particular, in developmental studies, RNA-Seq is challenged by the morphological staging of samples, such as embryos, since these often lack clear markers at any particular stage. In such cases, the automatic identification of the stage of a sample would enable previously infeasible experimental designs. Here we present the 'basic linear index determination of transcriptomes' (BLIND) method for ordering samples comprising different developmental stages. The method is an implementation of a traveling salesman algorithm to order the transcriptomes according to their inter-relationships as defined by principal components analysis. To establish the direction of the ordered samples, we show that an appropriate indicator is the entropy of transcriptomic gene expression levels, which increases over developmental time. Using BLIND, we correctly recover the annotated order of previously published embryonic transcriptomic timecourses for frog, mosquito, fly and zebrafish. We further demonstrate the efficacy of BLIND by collecting 59 embryos of the sponge Amphimedon queenslandica and ordering their transcriptomes according to developmental stage. BLIND is thus useful in establishing the temporal order of samples within large datasets and is of particular relevance to the study of organisms with asynchronous development and when morphological staging is difficult.