SENSE: Siamese neural network for sequence embedding and alignment-free comparison.

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 t

SUBMITTER: Zheng W 

PROVIDER: S-EPMC7963080 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature