Sequence-specific bias correction for RNA-seq data using recurrent neural networks

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• 作者：Yao-zhong Zhang ; Rui Yamaguchi ; Seiya Imoto ; Satoru Miyano
• 关键词：RNA ; seq ; Recurrent neural network ; Sequence ; specific bias ; Gene expression analysis
• 刊名：BMC Genomics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：18
• 期：1-supp
• 全文大小：270KB
• 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
• 出版者：BioMed Central
• ISSN：1471-2164
• 卷排序：18

文摘

BackgroundThe recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance.