Open AccessSENSE: Siamese neural network for sequence embedding and alignment-free comparison
Author(s) -
Wei Zheng,
Le Yang,
Robert J. Genco,
Jean WactawskiWende,
Michael Buck,
Yijun Sun
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty887
Subject(s) - computer science , embedding , sequence (biology) , pairwise comparison , artificial intelligence , alignment free sequence analysis , artificial neural network , multiple sequence alignment , heuristics , sequence alignment , similarity (geometry) , deep learning , algorithm , pattern recognition (psychology) , theoretical computer science , machine learning , biochemistry , genetics , gene , peptide sequence , image (mathematics) , biology , operating system , chemistry
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.