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SENSE: Siamese neural network for sequence embedding and alignment-free comparison
- Source :
- Bioinformatics
- Publication Year :
- 2018
- Publisher :
- Oxford University Press (OUP), 2018.
-
Abstract
- Motivation Sequence analysis is arguably a foundation of modern biology. Classic approaches to sequence analysis are based on sequence alignment, which is limited when dealing with large-scale sequence data. A dozen of alignment-free approaches have been developed to provide computationally efficient alternatives to alignment-based approaches. However, existing methods define sequence similarity based on various heuristics and can only provide rough approximations to alignment distances. Results In this article, we developed a new approach, referred to as SENSE (SiamEse Neural network for Sequence Embedding), for efficient and accurate alignment-free sequence comparison. The basic idea is to use a deep neural network to learn an explicit embedding function based on a small training dataset to project sequences into an embedding space so that the mean square error between alignment distances and pairwise distances defined in the embedding space is minimized. To the best of our knowledge, this is the first attempt to use deep learning for alignment-free sequence analysis. A large-scale experiment was performed that demonstrated that our method significantly outperformed the state-of-the-art alignment-free methods in terms of both efficiency and accuracy. Availability and implementation Open-source software for the proposed method is developed and freely available at https://www.acsu.buffalo.edu/∼yijunsun/lab/SENSE.html. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects :
- Statistics and Probability
Similarity (geometry)
Theoretical computer science
Biochemistry
03 medical and health sciences
Software
Molecular Biology
030304 developmental biology
0303 health sciences
Sequence
Artificial neural network
business.industry
Deep learning
030302 biochemistry & molecular biology
Computational Biology
Original Papers
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Embedding
Pairwise comparison
Neural Networks, Computer
Artificial intelligence
business
Heuristics
Sequence Alignment
Algorithms
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....5865672fb79c5a343af8c12ad1bfec94