Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems | Zendy

Catherine S. Grasso | Zendy; Christopher Lee | Zendy

Open Access

Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems

Author(s) -

Catherine S. Grasso,

Christopher Lee

Publication year - 2004

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/bth126

Subject(s) - multiple sequence alignment , sequence alignment , computer science , alignment free sequence analysis , structural alignment , scalability , smith–waterman algorithm , similarity (geometry) , sequence (biology) , pattern recognition (psychology) , artificial intelligence , data mining , algorithm , biology , genetics , peptide sequence , image (mathematics) , database , gene

Partial order alignment (POA) has been proposed as a new approach to multiple sequence alignment (MSA), which can be combined with existing methods such as progressive alignment. This is important for addressing problems both in the original version of POA (such as order sensitivity) and in standard progressive alignment programs (such as information loss in complex alignments, especially surrounding gap regions).

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