Accurate Phylogenetic Trees Research Articles

OHDLF: A Method for Selecting Orthologous Genes for Phylogenetic Construction and Its Application in the Genus Camellia

Accurate phylogenetic tree construction for species without reference genomes often relies on de novo transcriptome assembly to identify single-copy orthologous genes. However, challenges such as whole-genome duplication (WGD), heterozygosity, gene duplication, and loss can hinder the selection of these genes, leading to limited data for constructing reliable species trees. To address these issues, we developed a new analytical pipeline, OHDLF (Orthologous Haploid Duplication and Loss Filter), which filters orthologous genes from transcript data and adapts parameter settings based on genomic characteristics for further phylogenetic tree construction. In this study, we applied OHDLF to the genus Camellia and evaluated its effectiveness in constructing phylogenetic trees. The results highlighted the pipeline’s ability to handle challenges like high heterozygosity and recent gene duplications by selectively retaining genes with a missing rate and merging duplicates with high similarity. This approach ensured the preservation of informative sites and produced a highly supported consensus tree for Camellia. Additionally, we evaluate the accuracy of the OHDLF phylogenetic trees for different species, demonstrating that the OHDLF pipeline provides a flexible and effective method for selecting orthologous genes and constructing accurate phylogenetic trees, adapting to the genomic characteristics of various plant groups.

Identification of horizontal gene transference by means of anomaly detection and natural language-inspired interpretable embeddings

Horizontal gene transference is a biological process that involves the donation of DNA or RNA from an organism to a second, unrelated organism. This process is different from the more common one, vertical transference, which is present whenever an organism or pair of organisms reproduce and transmit their genetic material to the descendants. The identification of segments of genetic material that are the result of horizontal transference is relevant to construct accurate phylogenetic trees, on one hand, and to detect possible drug-resistance mechanisms, on the other, since this movement of genetic material is the main cause behind antibiotic resistance in bacteria. Here, we describe a novel algorithm able to detect sequences of foreign origin, and thus, possible acquired via horizontal transference. The general idea of our method is that within the genome of an organism, there might be sequences that are different from the vast majority of the remaining sequences from the same organism. The former are candidate anomalies, and thus, their origin may be explained by horizontal transference. This approach is equivalent to a particular instance of the authorship attribution problem, that in which from a set of texts or paragraphs, almost all of them were written by the same author, whereas a minority has a different authorship. The constraint is that the author of each text is not known, so the algorithm has to attribute the authorship of each one of the texts. The texts detected to be written by a different author are the equivalent of the sequences of foreign origin for the case of genetic material. We describe here a novel method to detect anomalous sequences, based on interpretable embeddings derived from a common attention mechanism in humans, that of identifying novel tokens within a given sequence. Our proposal achieves novel and consistent results over the genome of a well known organism.

Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) enables precise and efficient phylogenetic estimation in viruses.

Understanding phylogenetic relationships among species is essential for many biological studies, which call for an accurate phylogenetic tree to understand major evolutionary transitions. The phylogenetic analyses present a major challenge in estimation accuracy and computational efficiency, especially recently facing a wave of severe emerging infectious disease outbreaks. Here, we introduced a novel, efficient framework called Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) for new sample placement for viruses. In this study, a brand-new recoding method called Frequency Vector Recoding was implemented to approximate the phylogenetic distance, and the Phylogenetic Simulated Annealing Search algorithm was developed to match the recoded distance matrix with the phylogenetic tree. Meanwhile, the indel (insertion/deletion) was heuristically introduced to foreign sequence recognition for the first time. Here, we compared the Bd-RPC with the recent placement software (PAGAN2, EPA-ng, TreeBeST) and evaluated it in Alphacoronavirus, Alphaherpesvirinae, and Betacoronavirus by using Split and Robinson-Foulds distances. The comparisons showed that Bd-RPC maintained the highest precision with great efficiency, demonstrating good performance in new sample placement on all three virus genera. Finally, a user-friendly website (http://www.bd-rpc.xyz) is available for users to classify new samples instantly and facilitate exploration of the phylogenetic research in viruses, and the Bd-RPC is available on GitHub (http://github.com/Bin-Ma/bd-rpc).

Paramecium: RNA sequence-structure phylogenetics.

Organisms classified as members of the genus Paramecium belong to the best-known group of single-celled eukaryotes. Nevertheless, the phylogeny within the genus Paramecium has been discussed and revisited in recent decades and remains partly unresolved. By applying an RNA sequence-structure approach, we attempt to increase accuracy and robustness of phylogenetic trees. For each individual 18S and internal transcribed spacer 2 (ITS2) sequence, a putative secondary structure was predicted through homology modelling. While searching for a structural template, we found, in contrast to the available literature, that the ITS2 molecule consists of three helices in members of the genus Paramecium and four helices in members of the genus Tetrahymena. Two sequencestructure neighbor-joining overall trees were reconstructed with (1) more than 400 taxa (ITS2) and (2) more than 200 taxa (18S). For smaller subsets, neighbor-joining, maximum-parsimony, and maximum-likelihood analyses were executed using sequence-structure information simultaneously. Based on a combined data set (ITS2+18S rDNA) a well-supported tree was reconstructed with bootstrap values over 50 in at least one of the applied analyses. Our results are in general agreement with those published in the available literature based on multi-gene analyses. Our study supports the simultaneous use of sequence-structure data to reconstruct accurate and robust phylogenetic trees.

A target Capture Probe Set Useful for Deep- and Shallow-Level Phylogenetic Studies in Cactaceae.

The molecular phylogenies of Cactaceae have enabled us to better understand their systematics, biogeography, and diversification ages. However, most of the phylogenetic relationships within Cactaceae major groups remain unclear, largely due to the lack of an appropriate set of molecular markers to resolve its contentious relationships. Here, we explored the genome and transcriptome assemblies available for Cactaceae and identified putative orthologous regions shared among lineages of the subfamily Cactoideae. Then we developed a probe set, named Cactaceae591, targeting both coding and noncoding nuclear regions for representatives from the subfamilies Pereskioideae, Opuntioideae, and Cactoideae. We also sampled inter- and intraspecific variation to evaluate the potential of this panel to be used in phylogeographic studies. We retrieved on average of 547 orthologous regions per sample. Targeting noncoding nuclear regions showed to be crucial to resolving inter- and intraspecific relationships. Cactaceae591 covers 13 orthologous genes shared with the Angiosperms353 kit and two plastid regions largely used in Cactaceae studies, enabling the phylogenies generated by our panel to be integrated with angiosperm and Cactaceae phylogenies, using these sequences. We highlighted the importance of using coalescent-based species tree approaches on the Cactaceae591 dataset to infer accurate phylogenetic trees in the presence of extensive incomplete lineage sorting in this family.

PASTA with many application-aware optimization criteria for alignment based phylogeny inference

Multiple sequence alignment (MSA) is a prerequisite for several analyses in bioinformatics, such as, phylogeny estimation, protein structure prediction, etc. PASTA (Practical Alignments using SATé and TrAnsitivity) is a state-of-the-art method for computing MSAs, well-known for its accuracy and scalability. It iteratively co-estimates both MSA and maximum likelihood (ML) phylogenetic tree. It attempts to exploit the close association between the accuracy of an MSA and the corresponding tree while finding the output through multiple iterations from both directions. Currently, PASTA uses the ML score as its optimization criterion which is a good score in phylogeny estimation but cannot be proven as a necessary and sufficient criterion to produce an accurate phylogenetic tree. Therefore, the integration of multiple application-aware objectives into PASTA, which are carefully chosen considering their better association to the tree accuracy, may potentially have a profound positive impact on its performance. This paper has employed four application-aware objectives alongside ML score to develop a multi-objective (MO) framework, namely, PMAO that leverages PASTA to generate a bunch of high-quality solutions that are considered equivalent in the context of conflicting objectives under consideration. our experimental analysis on a popular biological benchmark reveals that the tree-space generated by PMAO contains significantly better trees than stand-alone PASTA. To help the domain experts further in choosing the most appropriate tree from the PMAO output (containing a relatively large set of high-quality solutions), we have added an additional component within the PMAO framework that is capable of generating a smaller set of high-quality solutions. Finally, we have attempted to obtain a single high-quality solution without using any external evidences and have found that summarizing the few solutions detected through the above component can serve this purpose to some extent.

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology.
 In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

PanACoTA: a modular tool for massive microbial comparative genomics.

The study of the gene repertoires of microbial species, their pangenomes, has become a key part of microbial evolution and functional genomics. Yet, the increasing number of genomes available complicates the establishment of the basic building blocks of comparative genomics. Here, we present PanACoTA (https://github.com/gem-pasteur/PanACoTA), a tool that allows to download all genomes of a species, build a database with those passing quality and redundancy controls, uniformly annotate and then build their pangenome, several variants of core genomes, their alignments and a rapid but accurate phylogenetic tree. While many programs building pangenomes have become available in the last few years, we have focused on a modular method, that tackles all the key steps of the process, from download to phylogenetic inference. While all steps are integrated, they can also be run separately and multiple times to allow rapid and extensive exploration of the parameters of interest. PanACoTA is built in Python3, includes a singularity container and features to facilitate its future development. We believe PanACoTa is an interesting addition to the current set of comparative genomics tools, since it will accelerate and standardize the more routine parts of the work, allowing microbial genomicists to more quickly tackle their specific questions.

Phylogenetic tree building in the genomic age.

Knowing phylogenetic relationships among species is fundamental for many studies in biology. An accurate phylogenetic tree underpins our understanding of the major transitions in evolution, such as the emergence of new body plans or metabolism, and is key to inferring the origin of new genes, detecting molecular adaptation, understanding morphological character evolution and reconstructing demographic changes in recently diverged species. Although data are ever more plentiful and powerful analysis methods are available, there remain many challenges to reliable tree building. Here, we discuss the major steps of phylogenetic analysis, including identification of orthologous genes or proteins, multiple sequence alignment, and choice of substitution models and inference methodologies. Understanding the different sources of errors and the strategies to mitigate them is essential for assembling an accurate tree of life.

Abstract 2476: RECUR: Algorithm for directional allelic imbalance profiling and visualization from multi-sample data

Abstract Somatic chromosomal alterations play an important role in the development and progression of cancer. Allelic imbalance (AI) resulting from such changes (gain, loss, or copy-neutral loss-of-heterozygosity; cnLOH) is defined as a deviation from the 1:1 ratio of inherited parental haplotypes. Observed “B allele” frequencies (BAFs) at germline heterozygous loci, from SNP array or next-generation sequencing data, are used to detect AI. In addition, log R ratio or read-depth data, can be analyzed alone or jointly with BAFs to detect gains and losses. When AI is detected in multiple intra-individual samples, spanning similar loci, it is natural to assess whether these signals reflect the same underlying mutation. One way to do this would be to note if the observed AI differs in mutation type (gain, loss), an approach that falls short for events of the same type and subtle AI events that cannot be classified as gains, losses, or cnLOH. In such a case, or with two mutations of the same type, samples may differ in their maternal/paternal haplotype balance and consequently their alleles will shift in opposite directions. This phenomenon is indicative of a recurrent or independent mutation, or an error in chromosome segregation that generates two “mirrored” clones, one with a gain and the other with a loss. Recent studies have used such a directional AI analysis to yield important insights into cancer initiation and progression (Jakubek et al. Cancer Research 2016, Jamal-Hanjani et al. New England Journal of Medicine 2017, Turajlic et al. Cell 2018). We developed REpeat Chromosomal changes Uncovered by Reflection (RECUR) to explicitly test directionality of AI from multiple samples with overlapping AI segments through the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and BAFs from at least 2 samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. It can help identify genomic regions under selective pressure for example recurrent deletions/gains in the same genomic loci and/or regions with generalized genomic instability. Integration of directional AI, copy number, and somatic mutation data can help build more accurate phylogenetic trees and further illuminate the timing and distribution of somatic chromosomal aberrations to offer insights into cancer initiation and progression. RECUR is available at scheet.org Citation Format: Yasminka A. Jakubek, F. Anthony San Lucas, Paul Scheet. RECUR: Algorithm for directional allelic imbalance profiling and visualization from multi-sample data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2476.

A fast alignment-free bioinformatics procedure to infer accurate distance-based phylogenetic trees from genome assemblies

This paper describes a novel alignment-free distance-based procedure for inferring phylogenetic trees from genome contig sequences using publicly available bioinformatics tools. For each pair of genomes, a dissimilarity measure is first computed and next transformed to obtain an estimation of the number of substitution events that have occurred during their evolution. These pairwise evolutionary distances are then used to infer a phylogenetic tree and assess a confidence support for each internal branch. Analyses of both simulated and real genome datasets show that this bioinformatics procedure allows accurate phylogenetic trees to be reconstructed with fast running times, especially when launched on multiple threads. Implemented in a publicly available script, named JolyTree, this procedure is a useful approach for quickly inferring species trees without the burden and potential biases of multiple sequence alignments.

SAliBASE: A Database of Simulated Protein Alignments.

Simulated alignments are alternatives to manually constructed multiple sequence alignments for evaluating performance of multiple sequence alignment tools. The importance of simulated sequences is recognized because their true evolutionary history is known, which is very helpful for reconstructing accurate phylogenetic trees and alignments. However, generating simulated alignments require expertise to use bioinformatics tools and consume several hours for reconstructing even a few hundreds of simulated sequences. It becomes a tedious job for an end user who needs a few datasets of variety of simulated sequences. Currently, there is no databank available which may help researchers to download simulated sequences/alignments for their study. Major focus of our study was to develop a database of simulated protein sequences (SAliBASE) based on different varying parameters such as insertion rate, deletion rate, sequence length, number of sequences, and indel size. Each dataset has corresponding alignment as well. This repository is very useful for evaluating multiple alignment methods.

The Site-Specific Amino Acid Preferences of Homologous Proteins Depend on Sequence Divergence.

The propensity of protein sites to be occupied by any of the 20 amino acids is known as site-specific amino acid preferences (SSAP). Under the assumption that SSAP are conserved among homologs, they can be used to parameterize evolutionary models for the reconstruction of accurate phylogenetic trees. However, simulations and experimental studies have not been able to fully assess the relative conservation of SSAP as a function of sequence divergence between protein homologs. Here, we implement a computational procedure to predict the SSAP of proteins based on the effect of changes in thermodynamic stability upon mutation. An advantage of this computational approach is that it allows us to interrogate a large and unbiased sample of homologous proteins, over the entire spectrum of sequence divergence, and under selection for the same molecular trait. We show that computational predictions have reproducibilities that resemble those obtained in experimental replicates, and can largely recapitulate the SSAP observed in a large-scale mutagenesis experiment. Our results support recent experimental reports on the conservation of SSAP of related homologs, with a slowly increasing fraction of up to 15% of different sites at sequence distances lower than 40%. However, even under the sole contribution of thermodynamic stability, our conservative approach identifies up to 30% of significant different sites between divergent homologs. We show that this relation holds for homologs of diverse sizes and structural classes. Analyses of residue contact networks suggest that an important determinant of these differences is the increasing accumulation of structural deviations that results from sequence divergence.

Split-inducing indels in phylogenomic analysis

BackgroundMost phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.ResultsHere we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.ConclusionsSuitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

Parsimony, not Bayesian analysis, recovers more stratigraphically congruent phylogenetic trees.

Reconstructing evolutionary histories requires accurate phylogenetic trees. Recent simulation studies suggest that probabilistic phylogenetic analyses of morphological data are more accurate than traditional parsimony techniques. Here, we use empirical data to compare Bayesian and parsimony phylogenies in terms of their congruence with the distribution of age ranges of the component taxa. Analysis of 167 independent morphological data matrices of fossil tetrapods finds that Bayesian trees exhibit significantly lower stratigraphic congruence than the equivalent parsimony trees. As such, taking stratigraphic data as an independent benchmark indicates that parsimony analyses are more accurate for phylogenetic reconstruction of morphological data. The discrepancy between simulated and empirical studies may result from historic data peaking practices or some complexities of empirical data as yet unaccounted for.

A novel approach based on multiobjective variable mesh optimization to Phylogenetics

One of the most relevant problems in Bioinformaticsand Computational Biology is the search and reconstruction ofthe most accurate phylogenetic tree that explains, as exactly aspossible, the evolutionary relationships among species from agiven dataset. Different criteria have been employed to evaluatethe accuracy of evolutionary hypothesis in order to guide a searchalgorithm towards the best tree. However, these criteria may leadto distinct phylogenies, which are often conflicting among them.Therefore, a multi-objective approach can be useful. In this work,we present a phylogenetic adaptation of a multiobjective variablemesh optimization algorithm for inferring phylogenies, to tacklethe phylogenetic inference problem according to two optimalitycriteria: maximum parsimony and maximum likelihood. Theaim of this approach is to propose a complementary view ofphylogenetics in order to generate a set of trade-off phylogenetictopologies that represent a consensus between both criteria.Experiments on four real nucleotide datasets show that ourproposal can achieve promising results, under both multiobjectiveand biological approaches, with regard to other classical andrecent multiobjective metaheuristics from the state-of-the-art.

Improvement of phylogenetic method to analyze compositional heterogeneity

BackgroundPhylogenetic analysis is a key way to understand current research in the biological processes and detect theory in evolution of natural selection. The evolutionary relationship between species is generally reflected in the form of phylogenetic trees. Many methods for constructing phylogenetic trees, are based on the optimization criteria. We extract the biological data via modeling features, and then compare these characteristics to study the biological evolution between species.ResultsHere, we use maximum likelihood and Bayesian inference method to establish phylogenetic trees; multi-chain Markov chain Monte Carlo sampling method can be used to select optimal phylogenetic tree, resolving local optimum problem. The correlation model of phylogenetic analysis assumes that phylogenetic trees are built on homogeneous data, however there exists a large deviation in the presence of heterogeneous data. We use conscious detection to solve compositional heterogeneity. Our method is evaluated on two sets of experimental data, a group of bacterial 16S ribosomal RNA gene data, and a group of genetic data with five homologous species.ConclusionsOur method can obtain accurate phylogenetic trees on the homologous data, and also detect the compositional heterogeneity of experimental data. We provide an efficient method to enhance the accuracy of generated phylogenetic tree.

An improved and general streamlined phylogenetic protocol applied to the fatty acid desaturase family

Numerous tools to generate phylogenetic estimates are available, but there is no single protocol that will produce an accurate phylogenetic tree for any dataset. Here, we investigated some of those tools, paying particular attention to different alignment algorithms, in order to produce a phylogeny for the integral membrane fatty acid desaturase (FAD) family. Herein, we report a novel streamlined protocol which utilises peptide pattern recognition (PPR). This protocol can theoretically be applied universally to generate accurate multiple sequence alignments and improve downstream phylogenetic analyses. Applied to the desaturases, the protocol generated the first detailed phylogenetic estimates for the family since 2003, which suggested they may have evolved from three functionally distinct desaturases and further, that desaturases evolved first in cyanobacteria. In addition to the phylogenetic outputs, we mapped PPR sequence motifs onto an X-ray protein structure to provide insights into biochemical function and demonstrate the complementarity of PPR and phylogenetics.

Scaling statistical multiple sequence alignment to large datasets

BackgroundMultiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. While many alignment methods exist, the most accurate alignments are likely to be based on stochastic models where sequences evolve down a tree with substitutions, insertions, and deletions. While some methods have been developed to estimate alignments under these stochastic models, only the Bayesian method BAli-Phy has been able to run on even moderately large datasets, containing 100 or so sequences. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today.ResultsWe use simulated data with up to 10,000 sequences representing a variety of model conditions, including some that are significantly divergent from the statistical models used in BAli-Phy and elsewhere. We give a method for incorporating BAli-Phy into PASTA and UPP, two strategies for enabling alignment methods to scale to large datasets, and give alignment and tree accuracy results measured against the ground truth from simulations. Comparable results are also given for other methods capable of aligning this many sequences.ConclusionsExtensions of BAli-Phy using PASTA and UPP produce significantly more accurate alignments and phylogenetic trees than the current leading methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3101-8) contains supplementary material, which is available to authorized users.

Accelerating the Smith-Waterman algorithm with interpair pruning and band optimization for the all-pairs comparison of base sequences.

BackgroundThe Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm (by realizing interpair block pruning and band optimization) compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).ResultsWe present an interpair optimization method for the Smith-Waterman algorithm with the aim of accelerating the all-pairs comparison of base sequences. Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed. This lower bound is further used for band optimization. We integrated our interpair optimization method into SW#, a previous GPU-based implementation that employs variants of a banded Smith-Waterman algorithm and a banded Myers-Miller algorithm. Evaluation using the six genomes of Bacillus anthracis shows that our method pruned 88 % of the matrix cells on a single GPU and 73 % of the matrix cells on two GPUs. For the genomes of the human chromosome 21, the alignment performance reached 202 giga-cell updates per second (GCUPS) on two Tesla K40 GPUs.ConclusionsEfficient interpair pruning and band optimization makes it possible to complete the all-pairs comparisons of the sequences of the same species 1.2 times faster than the intrapair pruning method. This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound. However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla. Consequently, our method is useful in accelerating the applications that require optimal local alignments scores for the same species. The source code is available for download from http://www-hagi.ist.osaka-u.ac.jp/research/code/.