SEPP: SATe-enabled phylogenetic placement
ABSTRACT www.cs.utexas.edu.edu We address the problem of Phylogenetic Placement, in which the objective is to insert short molecular sequences (called query sequences) into an existing phylogenetic tree and alignment on full-length sequences for the same gene. Phylogenetic placement has the potential to provide information beyond pure “species identification ” (i.e., the association of metagenomic reads to existing species), because it can also give information about the evolutionary relationships between these query sequences and to known species. Approaches for phylogenetic placement have been developed that operate in two steps: first, an alignment is estimated for each query sequence to the alignment of the full-length sequences, and then that alignment is used to find the optimal location in the phylogenetic tree for the query sequence. Recent methods of this type include HMMALIGN+EPA, HMMALIGN+pplacer, and PaPaRa+EPA. We report on a study evaluating phylogenetic placement methods on biological and simulated data. This study shows that these methods have extremely good accuracy and computational tractability under conditions where the input contains a highly accurate alignment and tree for the full-length sequences, and the set of full-length sequences is sufficiently small and not too evolutionarily diverse; however, we also show that under other conditions accuracy declines and the computational requirements for memory and time exceed acceptable limits. We present SEPP, a general
- SourceAvailable from: ncbi.nlm.nih.gov
[Show abstract] [Hide abstract]
- "To test their performance, we used the same datasets as those used in the PAGAN article (Lo¨ytynoja et al., 2012). These are two independently simulated datasets, prepared by Lo¨ytynoja et al. (2012) and Mirarab et al. (2012). We included two representative methods, PaPaRa version 2.0 and PAGAN version 0.38, in the comparison because they can be used in situations similar to ours. "
ABSTRACT: Two methods to add unaligned sequences into an existing multiple sequence alignment have been implemented as the ‘–add’ and ‘–addfragments’ options in the MAFFT package. The former option is a basic one and applicable only to full-length sequences, whereas the latter option is applicable even when the unaligned sequences are short and fragmentary. These methods internally infer the phylogenetic relationship among the sequences in the existing alignment and the phylogenetic positions of unaligned sequences. Benchmarks based on two independent simulations consistently suggest that the “–addfragments” option outperforms recent methods, PaPaRa and PAGAN, in accuracy for difficult problems and that these three methods appropriately handle easy problems.Availability: http://mafft.cbrc.jp/alignment/software/Contact: firstname.lastname@example.orgSupplementary information: Supplementary data are available at Bioinformatics onlineBioinformatics 09/2012; 28(23). DOI:10.1093/bioinformatics/bts578 · 4.62 Impact Factor
[Show abstract] [Hide abstract]
- "We downloaded the simulated test data of Mirarab et al. (2012) and analyzed the first ten replicates of the three different evolutionary scenarios. We used true simulated reference alignments and reference trees with RAxMLestimated branch lengths. "
ABSTRACT: Accurate alignment of large numbers of sequences is demanding and the computational burden is further increased by downstream analyses depending on these alignments. With the abundance of sequence data, an integrative approach of adding new sequences to existing alignments without their full re-computation and maintaining the relative matching of existing sequences is an attractive option. Another current challenge is the extension of reference alignments with fragmented sequences, as those coming from next-generation metagenomics, that contain relatively little information. Widely used methods for alignment extension are based on profile representation of reference sequences. These do not incorporate and use phylogenetic information and are affected by the composition of the reference alignment and the phylogenetic positions of query sequences. We have developed a method for phylogeny-aware alignment of partial-order sequence graphs and apply it here to the extension of alignments with new data. Our new method, called PAGAN, infers ancestral sequences for the reference alignment and adds new sequences in their phylogenetic context, either to predefined positions or by finding the best placement for sequences of unknown origin. Unlike profile-based alternatives, PAGAN considers the phylogenetic relatedness of the sequences and is not affected by inclusion of more diverged sequences in the reference set. Our analyses show that PAGAN outperforms alternative methods for alignment extension and provides superior accuracy for both DNA and protein data, the improvement being especially large for fragmented sequences. Moreover, PAGAN-generated alignments of noisy next-generation sequencing (NGS) sequences are accurate enough for the use of RNA-seq data in evolutionary analyses. PAGAN is written in C++, licensed under the GPL and its source code is available at http://code.google.com/p/pagan-msa.Bioinformatics 04/2012; 28(13):1684-91. DOI:10.1093/bioinformatics/bts198 · 4.62 Impact Factor
Article: A Format for Phylogenetic Placements[Show abstract] [Hide abstract]
ABSTRACT: We have developed a unified format for phylogenetic placements, that is, mappings of environmental sequence data (e.g., short reads) into a phylogenetic tree. We are motivated to do so by the growing number of tools for computing and post-processing phylogenetic placements, and the lack of an established standard for storing them. The format is lightweight, versatile, extensible, and is based on the JSON format, which can be parsed by most modern programming languages. Our format is already implemented in several tools for computing and post-processing parsimony- and likelihood-based phylogenetic placements and has worked well in practice. We believe that establishing a standard format for analyzing read placements at this early stage will lead to a more efficient development of powerful and portable post-analysis tools for the growing applications of phylogenetic placement.PLoS ONE 02/2012; 7(2):e31009. DOI:10.1371/journal.pone.0031009 · 3.23 Impact Factor