DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment

BMC Bioinformatics

Research Article

Erik S. Wright¹ 

[1] Department of Bacteriology, University of Wisconsin-Madison, 53715, Madison, WI, USA;Wisconsin Institute for Discovery, University of Wisconsin-Madison, 330 N. Orchard St., 53715, Madison, WI, USA;

关键词: Multiple sequence alignment;    Secondary structure predictions;    Large scale alignment;    Benchmark datasets;    Modeling gap penalties;   

DOI  :  10.1186/s12859-015-0749-z

 received in 2015-06-26, accepted in 2015-09-23,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundAlignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.ResultsTwo predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.ConclusionsPredicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

【 授权许可】

CC BY   
© Wright. 2015