Biclustering as a method for RNA local multiple sequence alignment | Zendy

Shu Wang | Zendy; Robin R. Gutell | Zendy; Daniel P. Miranker | Zendy

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Biclustering as a method for RNA local multiple sequence alignment

Author(s) -

Shu Wang,

Robin R. Gutell,

Daniel P. Miranker

Publication year - 2007

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/btm485

Subject(s) - biclustering , multiple sequence alignment , benchmark (surveying) , cluster analysis , computer science , sequence alignment , suite , sequence (biology) , hierarchical clustering , data mining , set (abstract data type) , alignment free sequence analysis , smith–waterman algorithm , artificial intelligence , biology , correlation clustering , genetics , cartography , cure data clustering algorithm , history , archaeology , peptide sequence , gene , programming language , geography

Biclustering is a clustering method that simultaneously clusters both the domain and range of a relation. A challenge in multiple sequence alignment (MSA) is that the alignment of sequences is often intended to reveal groups of conserved functional subsequences. Simultaneously, the grouping of the sequences can impact the alignment; precisely the kind of dual situation biclustering is intended to address.

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