Otto-Warburg-Laboratory, RNA Bioinformatics, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany.
Bioinformatics. 2018 Sep 1;34(17):3035-3037. doi: 10.1093/bioinformatics/bty222.
Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily, we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment. The package runs seamlessly on CPU and GPU and provides a simple interface to train and evaluate a network with a handful lines of code. Using an RNA A-to-I editing dataset and cross-linking immunoprecipitation (CLIP)-seq binding site sequences, we demonstrate that pysster classifies sequences with higher accuracy than previous methods, such as GraphProt or ssHMM, and is able to recover known sequence and structure motifs.
pysster is freely available at https://github.com/budach/pysster.
Supplementary data are available at Bioinformatics online.
卷积神经网络 (CNN) 在各种任务中表现出色，包括生物序列分类。然而，现有的实现通常针对特定任务进行了优化，难以重用。为了使研究人员更轻松地利用这些网络，我们实现了 pysster，这是一个用于在生物序列数据上训练 CNN 的 Python 包。通过学习序列和结构基序对序列进行分类，该包提供了自动超参数优化过程以及可视化学习基序以及有关其位置和类富集的信息的选项。该包在 CPU 和 GPU 上无缝运行，并提供了一个简单的接口，只需几行代码即可训练和评估网络。使用 RNA A-to-I 编辑数据集和交联免疫沉淀 (CLIP)-seq 结合位点序列，我们证明了 pysster 比以前的方法（如 GraphProt 或 ssHMM）更准确地对序列进行分类，并能够恢复已知的序列和结构基序。
pysster 可在 https://github.com/budach/pysster 上免费获得。
补充数据可在 Bioinformatics 在线获得。
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