scholarly journals Tree Compatibility, Incomplete Directed Perfect Phylogeny, and Dynamic Graph Connectivity: An Experimental Study

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 53
Author(s):  
David Fernández-Baca ◽  
Lei Liu
Keyword(s):  
Data Structure ◽  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Computational Study ◽  
Graph Connectivity ◽  
Data Matrix ◽  
Connected Components ◽  
Perfect Phylogeny ◽  
Dynamic Graph ◽  
Tree Compatibility

We study two problems in computational phylogenetics. The first is tree compatibility. The input is a collection of phylogenetic trees over different partially-overlapping sets of species. The goal is to find a single phylogenetic tree that displays all the evolutionary relationships implied by . The second problem is incomplete directed perfect phylogeny (IDPP). The input is a data matrix describing a collection of species by a set of characters, where some of the information is missing. The question is whether there exists a way to fill in the missing information so that the resulting matrix can be explained by a phylogenetic tree satisfying certain conditions. We explain the connection between tree compatibility and IDPP and show that a recent tree compatibility algorithm is effectively a generalization of an earlier IDPP algorithm. Both algorithms rely heavily on maintaining the connected components of a graph under a sequence of edge and vertex deletions, for which they use the dynamic connectivity data structure of Holm et al., known as HDT. We present a computational study of algorithms for tree compatibility and IDPP. We show experimentally that substituting HDT by a much simpler data structure—essentially, a single-level version of HDT—improves the performance of both of these algorithm in practice. We give partial empirical and theoretical justifications for this observation.

scholarly journals Computing nearest neighbour interchange distances between ranked phylogenetic trees

2021 ◽  
Vol 82 (1-2) ◽  
Author(s):  
Lena Collienne ◽  
Alex Gavryushkin
Keyword(s):  
Cancer Research ◽  
Computational Complexity ◽  
Phylogenetic Tree ◽  
Shortest Path ◽  
Phylogenetic Trees ◽  
Shortest Paths ◽  
Nearest Neighbour ◽  
Tree Inference ◽  
Subtree Prune And Regraft ◽  
Comparison Algorithms

AbstractMany 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 $${\mathbf {N}}{\mathbf {P}}$$ N P -hard in each of these graphs, making tree inference and comparison algorithms challenging to design in practice. Although anked 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 $${\mathbf {N}}{\mathbf {P}}$$ N 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).

scholarly journals Techniques for the verification of minimal phylogenetic trees illustrated with ten mammalian haemoglobin sequences

1980 ◽  
Vol 187 (1) ◽  
pp. 65-74 ◽  
Author(s):  
D Penny ◽  
M D Hendy ◽  
L R Foulds
Keyword(s):  
Amino Acid ◽  
Phylogenetic Tree ◽  
Protein Sequence ◽  
Phylogenetic Trees ◽  
Sequence Data ◽  
Protein Sequences ◽  
Nucleotide Sequences ◽  
Amino Acid Sequences ◽  
Minimal Tree ◽  
Protein Sequence Data

We have recently reported a method to identify the shortest possible phylogenetic tree for a set of protein sequences [Foulds Hendy & Penny (1979) J. Mol. Evol. 13. 127–150; Foulds, Penny & Hendy (1979) J. Mol. Evol. 13, 151–166]. The present paper discusses issues that arise during the construction of minimal phylogenetic trees from protein-sequence data. The conversion of the data from amino acid sequences into nucleotide sequences is shown to be advantageous. A new variation of a method for constructing a minimal tree is presented. Our previous methods have involved first constructing a tree and then either proving that it is minimal or transforming it into a minimal tree. The approach presented in the present paper progressively builds up a tree, taxon by taxon. We illustrate this approach by using it to construct a minimal tree for ten mammalian haemoglobin alpha-chain sequences. Finally we define a measure of the complexity of the data and illustrate a method to derive a directed phylogenetic tree from the minimal tree.

scholarly journals ConnectIt

2020 ◽  
Vol 14 (4) ◽  
pp. 653-667
Author(s):  
Laxman Dhulipala ◽  
Changwan Hong ◽  
Julian Shun
Keyword(s):  
Experimental Evaluation ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Graph Connectivity ◽  
Connected Components ◽  
Sampling Strategies ◽  
Spanning Forest ◽  
Speed Up ◽  
Minimum Spanning Forest ◽  
Edge Sampling

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

scholarly journals UCEasy: A software package for automating and simplifying the analysis of ultraconserved elements (UCEs)

2021 ◽  
Vol 9 ◽  
Author(s):  
Caio Ribeiro ◽  
Lucas Oliveira ◽  
Romina Batista ◽  
Marcos De Sousa
Keyword(s):  
Best Practices ◽  
Software Package ◽  
Phylogenetic Trees ◽  
Computational Analysis ◽  
Data Matrix ◽  
Command Line ◽  
Command Line Interface ◽  
Ultraconserved Elements ◽  
Research Software ◽  
Different Levels

The use of Ultraconserved Elements (UCEs) as genetic markers in phylogenomics has become popular and has provided promising results. Although UCE data can be easily obtained from targeted enriched sequencing, the protocol for in silico analysis of UCEs consist of the execution of heterogeneous and complex tools, a challenge for scientists without training in bioinformatics. Developing tools with the adoption of best practices in research software can lessen this problem by improving the execution of computational experiments, thus promoting better reproducibility. We present UCEasy, an easy-to-install and easy-to-use software package with a simple command line interface that facilitates the computational analysis of UCEs from sequencing samples, following the best practices of research software. UCEasy is a wrapper that standardises, automates and simplifies the quality control of raw reads, assembly and extraction and alignment of UCEs, generating at the end a data matrix with different levels of completeness that can be used to infer phylogenetic trees. We demonstrate the functionalities of UCEasy by reproducing the published results of phylogenomic studies of the bird genus Turdus (Aves) and of Adephaga families (Coleoptera) containing genomic datasets to efficiently extract UCEs.

scholarly journals Analysis of SARS-CoV-2 nucleocapsid protein sequence variations in ASEAN countries

2021 ◽  
Author(s):  
Mochammad Rajasa Mukti Negara ◽  
Ita Krissanti ◽  
Gita Widya Pradini
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Protein Sequences ◽  
Reference Sequence ◽  
N Protein ◽  
Asean Country ◽  
Sequence Variations ◽  
Complete Sequences ◽  
Asean Countries ◽  
Global Initiative

BACKGROUND Nucleocapsid (N) protein is one of four structural proteins of SARS-CoV-2  which is known to be more conserved than spike protein and is highly immunogenic. This study aimed to analyze the variation of the SARS-CoV-2 N protein sequences in ASEAN countries, including Indonesia. METHODS Complete sequences of SARS-CoV-2 N protein from each ASEAN country were obtained from Global Initiative on Sharing All Influenza Data (GISAID), while the reference sequence was obtained from GenBank. All sequences collected from December 2019 to March 2021 were grouped to the clade according to GISAID, and two representative isolates were chosen from each clade for the analysis. The sequences were aligned by MUSCLE, and phylogenetic trees were built using MEGA-X software based on the nucleotide and translated AA sequences. RESULTS 98 isolates of complete N protein genes from ASEAN countries were analyzed. The nucleotides of all isolates were 97.5% conserved. Of 31 nucleotide changes, 22 led to amino acid (AA) substitutions; thus, the AA sequences were 94.5% conserved. The phylogenetic tree of nucleotide and AA sequences shows similar branches. Nucleotide variations in clade O (C28311T); clade GR (28881–28883 GGG>AAC); and clade GRY (28881–28883 GGG>AAC and C28977T) lead to specific branches corresponding to the clade within both trees. CONCLUSIONS The N protein sequences of SARS-CoV-2 across ASEAN countries are highly conserved. Most isolates were closely related to the reference sequence originating from China, except the isolates representing clade O, GR, and GRY which formed specific branches in the phylogenetic tree.

INFERRING PHYLOGENETIC RELATIONSHIPS AVOIDING FORBIDDEN ROOTED TRIPLETS

2006 ◽  
Vol 04 (01) ◽  
pp. 59-74 ◽  
Author(s):  
YING-JUN HE ◽  
TRINH N. D. HUYNH ◽  
JESPER JANSSON ◽  
WING-KIN SUNG
Keyword(s):  
Approximation Algorithms ◽  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Phylogenetic Network ◽  
Evolutionary Relationships ◽  
Large Set ◽  
Tree Network ◽  
History Of ◽  
Overlapping Sets

To construct a phylogenetic tree or phylogenetic network for describing the evolutionary history of a set of species is a well-studied problem in computational biology. One previously proposed method to infer a phylogenetic tree/network for a large set of species is by merging a collection of known smaller phylogenetic trees on overlapping sets of species so that no (or as little as possible) branching information is lost. However, little work has been done so far on inferring a phylogenetic tree/network from a specified set of trees when in addition, certain evolutionary relationships among the species are known to be highly unlikely. In this paper, we consider the problem of constructing a phylogenetic tree/network which is consistent with all of the rooted triplets in a given set [Formula: see text] and none of the rooted triplets in another given set [Formula: see text]. Although NP-hard in the general case, we provide some efficient exact and approximation algorithms for a number of biologically meaningful variants of the problem.

Visualizing Speciation in Artificial Cichlid Fish

2006 ◽  
Vol 12 (2) ◽  
pp. 243-257 ◽  
Author(s):  
Ross Clement
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Cichlid Fish ◽  
Natural Consequence ◽  
Open Problems ◽  
Visualization System ◽  
Low Level ◽  
Wide Range ◽  
Level Information ◽  
History Of

The Cichlid Speciation Project (CSP) is an ALife simulation system for investigating open problems in the speciation of African cichlid fish. The CSP can be used to perform a wide range of experiments that show that speciation is a natural consequence of certain biological systems. A visualization system capable of extracting the history of speciation from low-level trace data and creating a phylogenetic tree has been implemented. Unlike previous approaches, this visualization system presents a concrete trace of speciation, rather than a summary of low-level information from which the viewer can make subjective decisions on how speciation progressed. The phylogenetic trees are a more objective visualization of speciation, and enable automated collection and summarization of the results of experiments. The visualization system is used to create a phylogenetic tree from an experiment that models sympatric speciation.

scholarly journals Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data

2019 ◽  
Vol 37 (2) ◽  
pp. 599-603 ◽  
Author(s):  
Li-Gen Wang ◽  
Tommy Tsan-Yuk Lam ◽  
Shuangbin Xu ◽  
Zehan Dai ◽  
Lang Zhou ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
R Package ◽  
External Data ◽  
Input And Output ◽  
Evolutionary Context ◽  
Tree Data ◽  
Downstream Analysis ◽  
Different Sources ◽  
Associated Data

Abstract Phylogenetic trees and data are often stored in incompatible and inconsistent formats. The outputs of software tools that contain trees with analysis findings are often not compatible with each other, making it hard to integrate the results of different analyses in a comparative study. The treeio package is designed to connect phylogenetic tree input and output. It supports extracting phylogenetic trees as well as the outputs of commonly used analytical software. It can link external data to phylogenies and merge tree data obtained from different sources, enabling analyses of phylogeny-associated data from different disciplines in an evolutionary context. Treeio also supports export of a phylogenetic tree with heterogeneous-associated data to a single tree file, including BEAST compatible NEXUS and jtree formats; these facilitate data sharing as well as file format conversion for downstream analysis. The treeio package is designed to work with the tidytree and ggtree packages. Tree data can be processed using the tidy interface with tidytree and visualized by ggtree. The treeio package is released within the Bioconductor and rOpenSci projects. It is available at https://www.bioconductor.org/packages/treeio/.

scholarly journals Model checking software for phylogenetic trees using distribution and database methods

2013 ◽  
Vol 10 (3) ◽  
pp. 16-30 ◽  
Author(s):  
José Ignacio Requeno ◽  
José Manuel Colom
Keyword(s):  
Model Checking ◽  
Phylogenetic Tree ◽  
Dna Sequence ◽  
Phylogenetic Trees ◽  
Computation Time ◽  
Biological Properties ◽  
Traditional Model ◽  
Temporal Logics ◽  
Memory Consumption ◽  
Speed Up

Summary Model checking, a generic and formal paradigm stemming from computer science based on temporal logics, has been proposed for the study of biological properties that emerge from the labeling of the states defined over the phylogenetic tree. This strategy allows us to use generic software tools already present in the industry. However, the performance of traditional model checking is penalized when scaling the system for large phylogenies. To this end, two strategies are presented here. The first one consists of partitioning the phylogenetic tree into a set of subgraphs each one representing a subproblem to be verified so as to speed up the computation time and distribute the memory consumption. The second strategy is based on uncoupling the information associated to each state of the phylogenetic tree (mainly, the DNA sequence) and exporting it to an external tool for the management of large information systems. The integration of all these approaches outperforms the results of monolithic model checking and helps us to execute the verification of properties in a real phylogenetic tree.

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