Fondazione Bruno Kessler, Via Sommarive, 18, Trento, 38123, Italy.
ICT International Doctoral School, Via Sommarive, 9, Trento, 38123, Italy.
BMC Bioinformatics. 2018 Nov 20;19(Suppl 14):418. doi: 10.1186/s12859-018-2386-9.
Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation.
In this work, we propose a deep learning model for nucleosome identification. Our model stacks convolutional layers and Long Short-term Memories to automatically extract features from short- and long-range dependencies in a sequence. Using this model we are able to avoid the feature extraction and selection steps while improving the classification performances.
Results computed on eleven data sets of five different organisms, from Yeast to Human, show the superiority of the proposed method with respect to the state of the art recently presented in the literature.
核小体是 DNA-组蛋白复合物，每个核小体包裹约 150 对双链 DNA。其功能对于染色质的主要功能之一即真核细胞的 DNA 包装到细胞核中是基本的。几项生物学研究表明，核小体定位影响细胞类型特异性基因活性的调节。此外，计算研究表明，关于包裹在核小体中的 DNA 片段存在序列特异性的证据，这明显受到特定 DNA 亚序列的组织的影响。因此，通过使用序列特征表示的计算方法已经成功地在基因组范围内识别核小体。
在这项工作中，我们提出了一种用于核小体识别的深度学习模型。我们的模型堆叠卷积层和长短期记忆来自动从序列中的短程和长程依赖关系中提取特征。使用该模型，我们能够避免特征提取和选择步骤，同时提高分类性能。
基于来自酵母到人类的五个不同生物体的十一个数据集计算的结果表明，与文献中最近提出的最新方法相比，该方法具有优越性。
