Generating consensus sequences from partial order multiple sequence alignment graphs. Export

@article{citeulike:172075, abstract = {MOTIVATION: 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. AVAILABILITY: The heaviest\_bundle source code is available at http://www.bioinformatics.ucla.edu/poa}, address = {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}, author = {Lee, C.}, citeulike-article-id = {172075}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btg109}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/19/8/999}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/12761063}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=12761063}, day = {22}, doi = {10.1093/bioinformatics/btg109}, issn = {1367-4803}, journal = {Bioinformatics}, month = {May}, number = {8}, pages = {999--1008}, posted-at = {2005-04-27 11:26:55}, priority = {5}, title = {Generating consensus sequences from partial order multiple sequence alignment graphs.}, url = {http://dx.doi.org/10.1093/bioinformatics/btg109}, volume = {19}, year = {2003} }

TY - JOUR ID - citeulike:172075 L3 - citeulike-article-id:172075 N2 - MOTIVATION: 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. AVAILABILITY: The heaviest_bundle source code is available at http://www.bioinformatics.ucla.edu/poa IS - 8 JF - Bioinformatics SN - 1367-4803 AD - 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 EP - 1008 TI - Generating consensus sequences from partial order multiple sequence alignment graphs. VL - 19 SP - 999 AU - Lee, C PY - 2003/05/22/ UR - http://dx.doi.org/10.1093/bioinformatics/btg109 ER -