Bayesian nonparametrics in protein remote homology search | Zendy

Mindaugas Margelevičius | Zendy

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Bayesian nonparametrics in protein remote homology search

Author(s) -

Mindaugas Margelevičius

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/btw213

Subject(s) - computer science , software , multiple sequence alignment , benchmark (surveying) , sensitivity (control systems) , context (archaeology) , data mining , sequence alignment , homology modeling , threading (protein sequence) , bayesian probability , process (computing) , python (programming language) , sequence (biology) , artificial intelligence , protein structure , peptide sequence , programming language , biology , engineering , paleontology , biochemistry , enzyme , genetics , geodesy , electronic engineering , gene , geography

Wide application of modeling of three-dimensional protein structures in biomedical research motivates developing protein sequence alignment computer tools featuring high alignment accuracy and sensitivity to remotely homologous proteins. In this paper, we aim at improving the quality of alignments between sequence profiles, encoded multiple sequence alignments. Modeling profile contexts, fixed-length profile fragments, is engaged to achieve this goal.

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