Enhanced statistics for local alignment of multiple alignments improves prediction of protein function and structure | Zendy

Milana FrenkelMorgenstern | Zendy; Hillary Voet | Zendy; Shmuel Pietrokovski | Zendy

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Enhanced statistics for local alignment of multiple alignments improves prediction of protein function and structure

Author(s) -

Milana FrenkelMorgenstern,

Hillary Voet,

Shmuel Pietrokovski

Publication year - 2005

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/bti462

Subject(s) - computer science , sequence (biology) , measure (data warehouse) , consistency (knowledge bases) , sequence alignment , block (permutation group theory) , multiple sequence alignment , smith–waterman algorithm , algorithm , pattern recognition (psychology) , data mining , artificial intelligence , statistics , mathematics , biology , peptide sequence , biochemistry , genetics , geometry , gene

Improved comparisons of multiple sequence alignments (profiles) with other profiles can identify subtle relationships between protein families and motifs significantly beyond the resolution of sequence-based comparisons.

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