Imogene: identification of motifs and cis-regulatory modules underlying gene co-regulation.
Imogene: identification of motifs and cis-regulatory modules underlying gene co-regulation.
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DOI：
10.1093/nar/gku209
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发表时间：
2014-06
影响因子：
14.9
通讯作者：
Hakim V
中科院分区：
文献类型：
--
作者：
Rouault H;Santolini M;Schweisguth F;Hakim V
顺式调控模块（CRM）和基序在组织和条件特异性基因表达中起着核心作用。在这里，我们提出了Imogene，我们已经开发出的统计工具，以促进他们的识别和实施在一个公开的软件合奏。从一个小的训练集的哺乳动物或苍蝇标准物质，驱动类似的基因表达谱，Imogene确定从头顺式调控基序，这种共表达的基础。然后，它可以在全基因组范围内预测具有类似于训练集的调节潜力的其他CRM。Imogene基于开发的统计工具和转录因子结合位点进化的明确模型，通过集中使用多个物种的测序基因组提供的信息，绕过了对大型数据集进行统计分析的需求。我们在表征的组织特异性小鼠发育CRM上测试Imogene。它的能力，以确定CRM具有相同的特异性，其从头创建的基序的基础上，是以前评估的“基序盲”的方法。我们进一步表明，无论是在苍蝇和哺乳动物中，伊莫金从头产生的图案是足以区分标准物质相关的不同的发展计划。值得注意的是，纯粹依赖于序列数据，Imogene在该辨别任务中的表现与先前报道的基于多个发育阶段的多个转录因子的染色质免疫沉淀（ChIP）数据的学习算法一样好。
Cis-regulatory modules (CRMs) and motifs play a central role in tissue and condition-specific gene expression. Here we present Imogene, an ensemble of statistical tools that we have developed to facilitate their identification and implemented in a publicly available software. Starting from a small training set of mammalian or fly CRMs that drive similar gene expression profiles, Imogene determines de novo cis-regulatory motifs that underlie this co-expression. It can then predict on a genome-wide scale other CRMs with a regulatory potential similar to the training set. Imogene bypasses the need of large datasets for statistical analyses by making central use of the information provided by the sequenced genomes of multiple species, based on the developed statistical tools and explicit models for transcription factor binding site evolution. We test Imogene on characterized tissue-specific mouse developmental CRMs. Its ability to identify CRMs with the same specificity based on its de novo created motifs is comparable to that of previously evaluated ‘motif-blind’ methods. We further show, both in flies and in mammals, that Imogene de novo generated motifs are sufficient to discriminate CRMs related to different developmental programs. Notably, purely relying on sequence data, Imogene performs as well in this discrimination task as a previously reported learning algorithm based on Chromatin Immunoprecipitation (ChIP) data for multiple transcription factors at multiple developmental stages.
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影响因子：
64.8
作者：
Clark, Andrew G.;Eisen, Michael B.;MacCallum, Iain
通讯作者：
MacCallum, Iain
调节性DNA序列的某些统计特性及其在预测果蝇基因组中的调节区域的使用：蓬松的尾巴测试。
影响因子：
3
作者：
Abnizova, I;te Boekhorst, R;Walter, K;Gilks, WR
通讯作者：
Gilks, WR
DOI：
10.1073/pnas.231608898
发表时间：
2002-01-22
影响因子：
11.1
作者：
Berman, BP;Nibu, Y;Eisen, MB
通讯作者：
Eisen, MB
影响因子：
64.5
作者：
Hallikas, O;Palin, K;Taipale, J
通讯作者：
Taipale, J
影响因子：
3
作者：
Chan, BY;Kibler, D
通讯作者：
Kibler, D