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

The 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 biologic...

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Bibliographic Details
Published in:BMC genomics 2017-01, Vol.18 (Suppl 1), p.1044-1044, Article 1044
Main Authors: Zhang, Yao-Zhong, Yamaguchi, Rui, Imoto, Seiya, Miyano, Satoru
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bias
Bioinformatics
Computational Biology - methods
Computational Biology - standards
Deep learning
Gene expression
Gene Expression Profiling
Gene sequencing
Genomes
Genomics
Humans
Innovations
Learning algorithms
Machine learning
Models, Statistical
Neural networks
Neural Networks (Computer)
Nucleotide sequence
Nucleotides
Parameter estimation
Pipelines
Probability
Recurrent neural networks
Ribonucleic acid
RNA
RNA sequencing
Sequence Analysis, RNA - methods
Sequence Analysis, RNA - standards
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Summary:The 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. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.
ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-016-3262-5