Ari Löytynoja

Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. For more than a few sequences, exact algorithms become computationally impractical, and progressive algorithms iterating pairwise alignments are widely used. These heuristic methods have a serious drawback because pairwise algorithms do not differentiate insertions from deletions and end up penalizing single insertion events multiple times. Such an unrealistically high penalty for insertions typically results in overmatching of sequences and an underestimation of the number of insertion events. We describe a modification of the traditional alignment algorithm that can distinguish insertion from deletion and avoid repeated penalization of insertions and illustrate this method with a pair hidden Markov model that uses an evolutionary scoring function. In comparison with a traditional progressive alignment method, our algorithm infers a greater number of insertion events and creates gaps that are phylogenetically consistent but spatially less concentrated. Our results suggest that some insertion/deletion "hot spots" may actually be artifacts of traditional alignment algorithms.

Evolutionary analyses require sequence alignments that correctly represent evolutionary homology. Evolutionary and structural homology are not the same and sequence alignments generated with methods designed for structural matching can be seriously misleading in comparative and phylogenetic analyses. The phylogeny-aware alignment algorithm implemented in the program PRANK has been shown to produce good alignments for evolutionary inferences. Unlike other alignment programs, PRANK makes use of phylogenetic information to distinguish alignment gaps caused by insertions or deletions and, thereafter, handles the two types of events differently. As a by-product of the correct handling of insertions and deletions, PRANK can provide the inferred ancestral sequences as a part of the output and mark the alignment gaps differently depending on their origin in insertion or deletion events. As the algorithm infers the evolutionary history of the sequences, PRANK can be sensitive to errors in the guide phylogeny and violations on the underlying assumptions about the origin and patterns of gaps. These issues are discussed in detail and practical advice for the use of PRANK in evolutionary analysis is provided. The PRANK software and other methods discussed here can be found from the program home page at http://code.google.com/p/prank-msa/.

Genetic sequence alignment is the basis of many evolutionary and comparative studies, and errors in alignments lead to errors in the interpretation of evolutionary information in genomes. Traditional multiple sequence alignment methods disregard the phylogenetic implications of gap patterns that they create and infer systematically biased alignments with excess deletions and substitutions, too few insertions, and implausible insertion-deletion-event histories. We present a method that prevents these systematic errors by recognizing insertions and deletions as distinct evolutionary events. We show theoretically and practically that this improves the quality of sequence alignments and downstream analyses over a wide range of realistic alignment problems. These results suggest that insertions and sequence turnover are more common than is currently thought and challenge the conventional picture of sequence evolution and mechanisms of functional and structural changes.

BACKGROUND: Phylogeny-aware progressive alignment has been found to perform well in phylogenetic alignment benchmarks and to produce superior alignments for the inference of selection on codon sequences. Its implementation in the PRANK alignment program package also allows modelling of complex evolutionary processes and inference of posterior probabilities for sequence sites evolving under each distinct scenario, either simultaneously with the alignment of sequences or as a post-processing step for an existing alignment. This has led to software with many advanced features, and users may find it difficult to generate optimal alignments, visualise the full information in their alignment results, or post-process these results, e.g. by objectively selecting subsets of alignment sites. RESULTS: We have created a web server called webPRANK that provides an easy-to-use interface to the PRANK phylogeny-aware alignment algorithm. The webPRANK server supports the alignment of DNA, protein and codon sequences as well as protein-translated alignment of cDNAs, and includes built-in structure models for the alignment of genomic sequences. The resulting alignments can be exported in various formats widely used in evolutionary sequence analyses. The webPRANK server also includes a powerful web-based alignment browser for the visualisation and post-processing of the results in the context of a cladogram relating the sequences, allowing (e.g.) removal of alignment columns with low posterior reliability. In addition to de novo alignments, webPRANK can be used for the inference of ancestral sequences with phylogenetically realistic gap patterns, and for the annotation and post-processing of existing alignments. The webPRANK server is freely available on the web at http://tinyurl.com/webprank . CONCLUSIONS: The webPRANK server incorporates phylogeny-aware multiple sequence alignment, visualisation and post-processing in an easy-to-use web interface. It widens the user base of phylogeny-aware multiple sequence alignment and allows the performance of all alignment-related activity for small sequence analysis projects using only a standard web browser.