Order-Dependent dissimilarity measures on phylogenetic trees.

Cyclic generators and an improved linear kernel for the rooted subtree prune and regraft distance

Note on the hybridization number and subtree distance in phylogenetics

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Many popular algorithms for searching the space of leaf-labelled (phylogenetic) trees are based on tree rearrangement operations. Under any such operation, the problem is reduced to searching a graph where vertices are trees and (undirected) edges are given by pairs of trees connected by one rearrangement operation (sometimes called a move). Most popular are the classical nearest neighbour interchange, subtree prune and regraft, and tree bisection and reconnection moves. The problem of computing distances, however, is {mathbf {N}}{mathbf {P}}-hard in each of these graphs, making tree inference and comparison algorithms challenging to design in practice. Although ranked phylogenetic trees are one of the central objects of interest in applications such as cancer research, immunology, and epidemiology, the computational complexity of the shortest path problem for these trees remained unsolved for decades. In this paper, we settle this problem for the ranked nearest neighbour interchange operation by establishing that the complexity depends on the weight difference between the two types of tree rearrangements (rank moves and edge moves), and varies from quadratic, which is the lowest possible complexity for this problem, to {mathbf {N}}{mathbf {P}}-hard, which is the highest. In particular, our result provides the first example of a phylogenetic tree rearrangement operation for which shortest paths, and hence the distance, can be computed efficiently. Specifically, our algorithm scales to trees with tens of thousands of leaves (and likely hundreds of thousands if implemented efficiently).

A note on the relaxation time of two Markov chains on rooted phylogenetic tree spaces

Global comparison of multiple-segmented viruses in 12-dimensional genome space

Finding the best phylogenetic tree under the maximum parsimony optimality criterion is computationally difficult. We quantify the occurrence of such optima for well-behaved sets of data. When nearest neighbor interchange operations are used, multiple local optima can occur even for "perfect" sequence data, which results in hill-climbing searches that never reach a global optimum. In contrast, we show that when neighbors are defined via the subtree prune and regraft metric, there is a single local optimum for perfect sequence data, and thus, every such search finds a global optimum quickly. We further characterize conditions for which sequences simulated under the Cavender-Farris-Neyman and Jukes-Cantor models of evolution yield well-behaved search spaces.

Weedy rice (Oryza spp.) is a semi‐domesticated genetic resource. In high‐latitude paddy ecosystems, weedy rice tends to head earlier than accompanied japonica rice. However, the heading allele genes underlying this weedy rice de‐domestication, which is crucial for understanding the genetic basis and repeatability of the heading, remain largely unexplored. Therefore, this study aims to investigate the allelic variation of nine heading genes using 46 weedy rice from three areas in northeast China (weedy rice from northeast China [WRNC]) and 32 of their accompanying japonica rice (Oryza sativa L.) varieties. The heading time of WRNC was 3–20 days earlier than that of the accompanying japonica rice. The heading‐promotion genotype heading date 1 (Hd1) was the main factor aiding WRNC heading earlier than its accompanying japonica rice. Functional haplotypes of heading date 2 (Hd2), grains height date 7 (Ghd7), and days to heading 2 (Dth2) may affect the early heading of weedy rice from Liaoning, Jilin, and Heilongjiang in northeast China. Phylogenetic tree analysis showed that WRNC is closely related to the cultivated rice in Northeast China and has no direct relationship with southern indica weedy rice. The results showed that some heading genes in the study differentiated during the divergence of indica and japonica rice; these genes in WRNC are not only affected by the differentiation of indica and japonica rice but also by the domestication of cultivated rice, forming an independent heading genotype distinct from northern japonica rice. This serves as a basis for research aimed at improving the heading period of cultivated rice by utilizing traits from weedy rice.

Axillary nerve block for a wrist fracture in a patient with facioscapulohumeral (Landouzy-Dejerine) disease

Overlaid species forests

A central theme in phylogenetics is the reconstruction and analysis of evolutionary trees from a given set of data. To determine the optimal search methods for reconstructing trees, it is crucial to understand the size and structure of the neighborhoods of trees under tree rearrangement operations. The diameter and size of the immediate neighborhood of a tree have been well-studied; however, little is known about the number of trees at distance two, three, or (more generally) $k$ from a given tree. In this paper we provide a number of exact and asymptotic results concerning these quantities and identify some key aspects of tree shape that play a role in determining these quantities. We obtain several new results for two of the main tree rearrangement operations---nearest neighbor interchange and subtree prune and regraft---as well as for the Robinson--Foulds metric on trees.

Word-based or ‘alignment-free’ sequence comparison has become an active research area in bioinformatics. While previous word-frequency approaches calculated rough measures of sequence similarity or dissimilarity, some new alignment-free methods are able to accurately estimate phylogenetic distances between genomic sequences. One of these approaches is Filtered Spaced Word Matches. Here, we extend this approach to estimate evolutionary distances between complete or incomplete proteomes; our implementation of this approach is called Prot-SpaM. We compare the performance of Prot-SpaM to other alignment-free methods on simulated sequences and on various groups of eukaryotic and prokaryotic taxa. Prot-SpaM can be used to calculate high-quality phylogenetic trees for dozens of whole-proteome sequences in a matter of seconds or minutes and often outperforms other alignment-free approaches. The source code of our software is available through Github: https://github.com/jschellh/ProtSpaM.

The Maximum Parsimony (MP) problem aims at reconstructing a phylogenetic tree from DNA sequences while minimizing the number of genetic transformations. To solve this NP-complete problem, heuristic methods have been developed, often based on local search. In this article, we focus on the influence of the neighborhood relations. After analyzing the advantages and drawbacks of the well-known Nearest Neighbor Interchange (NNI), Subtree Pruning Regrafting (SPR) and Tree-Bisection-Reconnection (TBR) neighborhoods, we introduce the concept of Progressive Neighborhood (PN) which consists in constraining progressively the size of the neighborhood as the search advances. We empirically show that applied to the Maximum Parsimony problem, this progressive neighborhood turns out to be more efficient and robust than the classic neighborhoods using a descent algorithm. Indeed, it allows to find better solutions with a smaller number of iterations or trees evaluated.

With the rapid development of genomic sequencing technologies, there is an increasing demand for efficient and accurate sequence analysis methods. However, existing methods face challenges in handling long, variable-length sequences and large-scale datasets. To address these issues, we propose a novel encoding method—Energy Entropy Vector (EEV). This method encodes gene sequences of arbitrary length into fixed-dimensional vector representations by modeling nucleotide energy characteristics based on information entropy. Experiments conducted on five microbial datasets demonstrate that, compared to traditional alignment-free methods, EEV achieves higher accuracy in convex hull classification and species classification tasks, with improvements of 15% to 30% in family-level classification. In phylogenetic tree construction, EEV significantly accelerates the process relative to multiple sequence alignment methods while maintaining high tree quality, enabling rapid and accurate phylogenetic reconstruction. Moreover, EEV supports flexible dimensional expansion by superimposing nucleotide energies, enhancing its ability to represent complex genomic sequences while effectively alleviating sparsity issues in high-dimensional representations. This study provides an efficient gene encoding strategy for large-scale genomic analysis and evolutionary research.

Similarity/dissimilarity analysis is a key way of understanding the biology of an organism by knowing the origin of the new genes/sequences. Sequence data are grouped in terms of biological relationships. The number of sequences related to any group is susceptible to be increased every day. All the present alignment-free methods approve the utility of their approaches by producing a similarity/dissimilarity matrix. Although this matrix is clear, it measures the degree of similarity among sequences individually. In our work, a representative of each of three groups of protein sequences is introduced. A similarity/dissimilarity vector is evaluated instead of the ordinary similarity/dissimilarity matrix based on the group representative. The approach is applied on three selected groups of protein sequences: beta globin, NADH dehydrogenase subunit 5 (ND5), and spike protein sequences. A cross-grouping comparison is produced to ensure the singularity of each group. A qualitative comparison between our approach, previous articles, and the phylogenetic tree of these protein sequences proved the utility of our approach.