Discovering differential genome sequence activity with interpretable and efficient deep learning.
Discovering differential genome sequence activity with interpretable and efficient deep learning.
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DOI：
10.1371/journal.pcbi.1009282
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发表时间：
2021-08
影响因子：
4.3
通讯作者：
Gifford DK
中科院分区：
文献类型：
--
作者：
Hammelman J;Gifford DK
发现不同的序列特征将细胞定向到不同的命运，这是理解细胞发育和疾病相关突变后果的关键。我们介绍了预期模式效应和差异预期模式效应，这两种黑盒方法可以解释特定细胞类型或特定条件的模式的基因组调控序列。我们表明，这些方法识别相关的转录因子基序和间距，可以预测细胞状态特异性染色质的可及性。最后，我们将这些方法集成到框架中，非专家可以很容易地访问该框架，并且可以作为二进制文件下载或通过https://cgs.csail.mit.edu/deepaccess-package/.上的PYPI或BIOCONDA安装基因组中有构建构成人体的所有细胞类型的指令。然而，理解这些指令以及这些指令在癌症或基因遗传性疾病中如何以及何时出错是一个悬而未决的问题。深度神经网络提供了强大的模型来了解DNA序列与许多不同细胞类型的功能结果之间的关系，例如特定的DNA片段是否可访问，该区域的基因是否可以表达，因此基因是否处于非活动状态。尽管有这些进展，深度学习中的一个主要挫折是，很难理解深度学习模型已经学会了将哪些DNA序列模式与特定的基因组功能相关联，这些模式是否重要，以及如何确定这些模式是特定细胞类型的特定模式，还是跨多种细胞类型发挥作用的一般“管家”模式。我们引入了预期模式效应和差异预期模式效应，这两种方法允许我们评估DNA序列特征的特定模式在被训练来预测多种细胞类型的功能的模型上的重要性，并将其应用于转录因子结合和跨多种细胞类型的DNA可及性问题。
Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/. Within the genome are the instructions to build all the cell types that make up the human body. However, understanding these instructions and how and when these instructions go wrong in cancer or genetically inherited disease is an open problem. Deep neural networks provide powerful models to learn the relationship between DNA sequence and functional consequence across many different cell types, such as whether a particular stretch of DNA is accessible and genes in that region can be expressed or is inaccessible and therefore genes are inactive. Despite these advances, a major setback in deep learning is that it is challenging to understand what patterns of DNA sequence that a deep learning model has learned to associate with a particular genomic function, whether these patterns are significant, and how to determine whether these patterns are specific to a particular cell type or are general “housekeeping” patterns that function across many cell types. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two methods which allow us to evaluate the significance of particular patterns of DNA sequence features on models trained to predict function across multiple cell types, and apply this to problems of transcription factor binding and DNA accessibility across multiple cell types.
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影响因子：
8.1
作者：
Bastidas-Ponce A;Roscioni SS;Burtscher I;Bader E;Sterr M;Bakhti M;Lickert H
通讯作者：
Lickert H
影响因子：
8.8
作者：
Genga, Ryan M. J.;Kernfeld, Eric M.;Maehr, Rene
通讯作者：
Maehr, Rene
影响因子：
12.3
作者：
Jha, Anupama;Aicher, Joseph K.;Barash, Yoseph
通讯作者：
Barash, Yoseph
影响因子：
4.3
作者：
Guo Y;Mahony S;Gifford DK
通讯作者：
Gifford DK
影响因子：
64.5
作者：
Dasen, JS;Tice, BC;Jessell, TM
通讯作者：
Jessell, TM