DACE: a scalable DP-means algorithm for clustering extremely large sequence data | Zendy

Linhao Jiang | Zendy; Yichao Dong | Zendy; Ning Chen | Zendy; Ting Chen | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

DACE: a scalable DP-means algorithm for clustering extremely large sequence data

Author(s) -

Linhao Jiang,

Yichao Dong,

Ning Chen,

Ting Chen

Publication year - 2016

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/btw722

Subject(s) - cluster analysis , metagenomics , scalability , dna sequencing , computational biology , data mining , biology , gene , computer science , genetics , artificial intelligence , database

Advancements in next-generation sequencing technology have produced large amounts of reads at low cost in a short time. In metagenomics, 16S and 18S rRNA gene have been widely used as marker genes to profile diversity of microorganisms in environmental samples. Through clustering of sequencing reads we can determine both number of OTUs and their relative abundance. In many applications, clustering of very large sequencing data with high efficiency and accuracy is essential for downstream analysis.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research