As more and more genomes are sequenced, evolutionary biologists are becoming increasingly interested in evolution at the level of whole genomes, in scenarios in which the genome evolves through insertions, deletions, and movements of genes along its chromosomes. In the mathematical model pioneered by Sankoff and others, a unichromosomal genome is represented by a signed permutation of a multi-set of genes; Hannenhalli and Pevzner showed that the edit distance between two signed permutations of the same set can be computed in polynomial time when all operations are inversions. El-Mabrouk extended that result to allow deletions and a limited form of insertions (which forbids duplications). In this paper we extend El-Mabrouk's work to handle duplications as well as insertions and present an alternate framework for computing (near) minimal edit sequences involving insertions, deletions, and inversions. We derive an error bound for our polynomial-time distance computation under various assumptions and present preliminary experimental results that suggest that performance in practice may be excellent, within a few percent of the actual distance.

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Abstract. We present a comparative genomics approach for inferring ancestral genome organization and evolutionary scenarios, based on a model accounting for content-modifying operations. More precisely, we focus on comparing two ordered gene sequences with duplicated genes that have evolved from a common ancestor through duplications and losses; our model can be grouped in the class of "Block Edit" models. From a combinatorial point of view, the main consequence is the possibility of formulating the problem as an alignment problem. On the other hand, in contrast to symmetrical metrics such as the inversion distance, duplications and losses are asymmetrical operations that are applicable to one of the two aligned sequences. Consequently, an ancestral genome can directly be inferred from a duplication-loss scenario attached to a given alignment. Although alignments are a priori simpler to handle than rearrangements, we show that a direct approach based on dynamic programming leads, at best, to an efficient heuristic. We present an exact pseudo-boolean linear programming algorithm to search for the optimal alignment along with an optimal scenario of duplications and losses. Although exponential in the worst case, we show low running times on real datasets as well as synthetic data. We apply our algorithm in a phylogenetic context to the evolution of stable RNA (tRNA and rRNA) gene content and organization in Bacillus genomes. Our results lead to various biological insights, such as rates of ribosomal RNA proliferation among lineages, their role in altering tRNA gene content, and evidence of tRNA class conversion.

As more and more genomes are sequenced, evolutionary biologists are becoming increasingly interested in evolution at the level of whole genomes, in scenarios in which the genome evolves through insertions, duplications, deletions, and movements of genes along its chromosomes. In the mathematical model pioneered by Sankoff and others, a unichromosomal genome is represented by a signed permutation of a multiset of genes; Hannenhalli and Pevzner showed that the edit distance between two signed permutations of the same set can be computed in polynomial time when all operations are inversions. El-Mabrouk extended that result to allow deletions and a limited form of insertions (which forbids duplications); in turn we extended it to compute a nearly optimal edit sequence between an arbitrary genome and the identity permutation. In this paper we generalize our approach to compute distances between two arbitrary genomes, but focus on approximating the true evolutionary distance rather than the edit distance. We present experimental results showing that our algorithm produces excellent estimates of the true evolutionary distance up to a (high) threshold of saturation; indeed, the distances thus produced are good enough to enable the simple neighbor-joining procedure to reconstruct our test trees with high accuracy. ACM Reference Format:Swenson, K. M., Marron, M., Earnest-Deyoung, J. V., and Moret, B. M. E. 2008. Approximating the true evolutionary distance between two genomes. ACM J.

Motivation Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing. Results We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics. Availability and implementation Program and sources freely available for download at https://github.com/blinard-BIOINFO/RAPPAS. Supplementary information Supplementary data are available at Bioinformatics online.

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BackgroundReconciled gene trees yield orthology and paralogy relationships between genes. This information may however contradict other information on orthology and paralogy provided by other footprints of evolution, such as conserved synteny.ResultsWe explore a way to include external information on orthology in the process of gene tree construction. Given an initial gene tree and a set of orthology constraints on pairs of genes or on clades, we give polynomial-time algorithms for producing a modified gene tree satisfying the set of constraints, that is as close as possible to the original one according to the Robinson-Foulds distance. We assess the validity of the modifications we propose by computing the likelihood ratio between initial and modified trees according to sequence alignments on Ensembl trees, showing that often the two trees are statistically equivalent.AvailabilitySoftware and data available upon request to the corresponding author.

Abstract. Many of the steps in phylogenetic reconstruction can be confounded by "rogue" taxa, taxa that cannot be placed with assurance anywhere within the tree-whose location within the tree, in fact, varies with almost any choice of algorithm or parameters. Phylogenetic consensus methods, in particular, are known to suffer from this problem. In this paper we provide a novel framework in which to define and identify rogue taxa. In this framework, we formulate a bicriterion optimization problem that models the net increase in useful information present in the consensus tree when certain taxa are removed from the input data. We also provide an effective greedy heuristic to identify a subset of rogue taxa and use it in a series of experiments, using both pathological examples described in the literature and a collection of large biological datasets. As the presence of rogue taxa in a set of bootstrap replicates can lead to deceivingly poor support values, we propose a procedure to recompute support values in light of the rogue taxa identified by our algorithm; applying this procedure to our biological datasets caused a large number of edges to change from "unsupported" to "supported" status, indicating that many existing phylogenies should be recomputed and reevaluated to reduce any inaccuracies introduced by rogue taxa.