Sequence similarity alignment algorithm in bioinformatics : techniques and challenges

Liu, Yuren and Yan, Yijun and Ren, Jinchang; Ren, Jinchang and Hussain, Amir and Zhao, Huimin and Huang, Kaizhu and Zheng, Jiangbin and Cai, Jun and Chen, Rongjun and Xiao, Yinyin, eds. (2020) Sequence similarity alignment algorithm in bioinformatics : techniques and challenges. In: Advances in Brain Inspired Cognitive Systems. Springer, CHN, pp. 550-560. ISBN 9783030394318 (https://doi.org/10.1007/978-3-030-39431-8_53)

[thumbnail of Liu-etal-ABICS-2020-Sequence-similarity-alignment-algorithm-in-bioinformatics-techniques-and-challenges]
Preview
 Text. Filename: Liu_etal_ABICS_2020_Sequence_similarity_alignment_algorithm_in_bioinformatics_techniques_and_challenges.pdf
Accepted Author Manuscript
Download (413kB)| Preview

Abstract

Sequence similarity alignment is a basic information processing method in bioinformatics. It is very important for discovering the information of function, structure and evolution in biological sequences. The main idea is to use a specific mathematical model or algorithm to find the maximum matching base or residual number between two or more sequences. The results of alignment reflect to what extent the algorithm reflects the similarity relationship between sequences and their biological characteristics. Therefore, the simple and effective algorithm of sequence similarity alignment in bioinformatics has always been a concern of biologists. This paper reviews some widely used sequence alignment algorithms including double-sequence alignment and multi-sequence alignment, simultaneously, introduces a method to call genetic variants from next-generation gene sequence data.

ORCID iDs

Liu, Yuren, Yan, Yijun ORCID logoORCID: https://orcid.org/0000-0003-0224-0078 and Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194; Ren, Jinchang, Hussain, Amir, Zhao, Huimin, Huang, Kaizhu, Zheng, Jiangbin, Cai, Jun, Chen, Rongjun and Xiao, Yinyin
CORE (COnnecting REpositories)