Generating consensus sequences from partial order multiple sequence alignment graphs.
BACKGROUND
Consensus sequence generation is important in many kinds of sequence analysis ranging from sequence assembly to profile-based iterative search methods. However, how can a consensus be constructed when its inherent assumption-that the aligned sequences form a single linear consensus-is not true?


RESULTS
Partial Order Alignment (POA) enables construction and analysis of multiple sequence alignments as directed acyclic graphs containing complex branching structure. Here we present a dynamic programming algorithm (heaviest_bundle) for generating multiple consensus sequences from such complex alignments. The number and relationships of these consensus sequences reveals the degree of structural complexity of the source alignment. This is a powerful and general approach for analyzing and visualizing complex alignment structures, and can be applied to any alignment. We illustrate its value for analyzing expressed sequence alignments to detect alternative splicing, reconstruct full length mRNA isoform sequences from EST fragments, and separate paralog mixtures that can cause incorrect SNP predictions.


BACKGROUND
The heaviest_bundle source code is available at http://www.bioinformatics.ucla.edu/poa
Lee
Christopher
C
UCLA-DOE Center for Genomics and Proteomics, Molecular Biology Institute Department of Chemistry, University of California, Los Angeles, Los Angeles, CA 90095-1570, USA. leec@mbi.ucla.edu
D000465:Algorithms; D016384:Consensus Sequence / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software