Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A LASSO-based approach to sample sites for phylogenetic tree search.

ABSTRACT:

Motivation

In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale alignments are challenging for likelihood-based phylogenetic inference programs and usually require using a powerful computer cluster. Current tools for alignment trimming prior to phylogenetic analysis do not promise a significant reduction in the alignment size and are claimed to have a negative effect on the accuracy of the obtained tree.

Results

Here, we propose an artificial-intelligence-based approach, which provides means to select the optimal subset of sites and a formula by which one can compute the log-likelihood of the entire data based on this subset. Our approach is based on training a regularized Lasso-regression model that optimizes the log-likelihood prediction accuracy while putting a constraint on the number of sites used for the approximation. We show that computing the likelihood based on 5% of the sites already provides accurate approximation of the tree likelihood based on the entire data. Furthermore, we show that using this Lasso-based approximation during a tree search decreased running-time substantially while retaining the same tree-search performance.

Availability and implementation

The code was implemented in Python version 3.8 and is available through GitHub (https://github.com/noaeker/lasso_positions_sampling). The datasets used in this paper were retrieved from Zhou et al. (2018) as described in section 3.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Ecker N

PROVIDER: S-EPMC9236582 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO.

Project description:Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including sampled ancestors in which we sequence a genotype along with its direct descendants, and polytomies in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the tree. However, current maximum-likelihood based approaches are not capable of revealing such zero-length branches. In this paper, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators for the branch lengths of phylogenetic trees, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics.

| S-EPMC8300927 | biostudies-literature

Harnessing machine learning to guide phylogenetic-tree search algorithms.

Project description:Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems involving more than a handful of sequences, inference under the maximum-likelihood paradigm integrates heuristic approaches to evaluate only a subset of all potential trees. Consequently, existing methods suffer from the known tradeoff between accuracy and running time. In this proof-of-concept study, we train a machine-learning algorithm over an extensive cohort of empirical data to predict the neighboring trees that increase the likelihood, without actually computing their likelihood. This provides means to safely discard a large set of the search space, thus potentially accelerating heuristic tree searches without losing accuracy. Our analyses suggest that machine learning can guide tree-search methodologies towards the most promising candidate trees.

| S-EPMC8012635 | biostudies-literature

RNA-Seq Based Reconstruction of the Phylogenetic Tree of the Genus Flaveria (Asteraceae)

Project description:The genus Flaveria has been extensively used as a model to study the evolution of C4 photosynthesis as it contains both C3 and C4 species as well as a number of species that exhibit intermediate types of photosynthesis. The current phylogenetic tree of the Flaveria genus contains 21 of the 23 known Flaveria species and has been constructed using a combination of morphologicial data and three non-coding DNA sequences (nuclear encoded ETS, ITS and chloroplast encoded trnl-F). However, recent studies have suggested that phylogenetic trees inferred using a small number of molecular sequences may often be incorrect. Moreover, studies in other genera have often shown substantial differences between trees inferred using morphological data and those using molecular sequence. To provide new insight into the phylogeny of the genus Flaveria we utilize RNA-Seq data to construct a multi-gene concatenated phylogenetic tree of 17 Flaveria species. Furthermore, we use this new data to identify 14 C4 specific non-synonymous mutation sites, 12 of which (86%) can be independently verified by public sequence data. We propose that the data collection method provided in this study can be used as a generic method for facilitating phylogenetic tree reconstruction in the absence of reference genomes for the target species.

2015-01-08 | GSE54339 | GEO

New approach for phylogenetic tree recovery based on genome-scale metabolic networks.

Project description:A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

| S-EPMC4082356 | biostudies-literature

Very few sites can reshape the inferred phylogenetic tree.

Project description:The history of animal evolution, and the relative placement of extant animal phyla in this history is, in principle, testable from phylogenies derived from molecular sequence data. Though datasets have increased in size and quality in the past years, the contribution of individual genes (and ultimately amino acid sites) to the final phylogeny is unequal across genes. Here we demonstrate that removing a small fraction of sites strongly favoring one topology can produce a highly-supported tree of an alternate topology. We explore this approach using a dataset for animal phylogeny, and create a highly-supported tree with a monophyletic group of sponges and ctenophores, a topology not usually recovered. Because of the high sensitivity of such an analysis to gene selection, and because most gene sets are neither standardized nor representative of the entire genome, researchers should be diligent about making intermediate analyses available with their phylogenetic studies. Effort is needed to ensure these datasets are maximally informative, by ensuring all genes are systematically sampled across relevant species. From there, it could be determined whether any gene or gene sets introduce bias, and then deal with those biases appropriately.

| S-EPMC7353913 | biostudies-literature

RNA-Seq Based Reconstruction of the Phylogenetic Tree of the Genus Flaveria (Asteraceae)

2015-01-08 | E-GEOD-54339 | biostudies-arrayexpress

PhyTB: Phylogenetic tree visualisation and sample positioning for M. tuberculosis.

Project description:BACKGROUND:Phylogenetic-based classification of M. tuberculosis and other bacterial genomes is a core analysis for studying evolutionary hypotheses, disease outbreaks and transmission events. Whole genome sequencing is providing new insights into the genomic variation underlying intra- and inter-strain diversity, thereby assisting with the classification and molecular barcoding of the bacteria. One roadblock to strain investigation is the lack of user-interactive solutions to interrogate and visualise variation within a phylogenetic tree setting. RESULTS:We have developed a web-based tool called PhyTB ( http://pathogenseq.lshtm.ac.uk/phytblive/index.php ) to assist phylogenetic tree visualisation and identification of M. tuberculosis clade-informative polymorphism. Variant Call Format files can be uploaded to determine a sample position within the tree. A map view summarises the geographical distribution of alleles and strain-types. The utility of the PhyTB is demonstrated on sequence data from 1,601 M. tuberculosis isolates. CONCLUSION:PhyTB contextualises M. tuberculosis genomic variation within epidemiological, geographical and phylogenic settings. Further tool utility is possible by incorporating large variants and phenotypic data (e.g. drug-resistance profiles), and an assessment of genotype-phenotype associations. Source code is available to develop similar websites for other organisms ( http://sourceforge.net/projects/phylotrack ).

| S-EPMC4429496 | biostudies-literature

Search for a 'Tree of Life' in the thicket of the phylogenetic forest.

Project description:BackgroundComparative genomics has revealed extensive horizontal gene transfer among prokaryotes, a development that is often considered to undermine the 'tree of life' concept. However, the possibility remains that a statistical central trend still exists in the phylogenetic 'forest of life'.ResultsA comprehensive comparative analysis of a 'forest' of 6,901 phylogenetic trees for prokaryotic genes revealed a consistent phylogenetic signal, particularly among 102 nearly universal trees, despite high levels of topological inconsistency, probably due to horizontal gene transfer. Horizontal transfers seemed to be distributed randomly and did not obscure the central trend. The nearly universal trees were topologically similar to numerous other trees. Thus, the nearly universal trees might reflect a significant central tendency, although they cannot represent the forest completely. However, topological consistency was seen mostly at shallow tree depths and abruptly dropped at the level of the radiation of archaeal and bacterial phyla, suggesting that early phases of evolution could be non-tree-like (Biological Big Bang). Simulations of evolution under compressed cladogenesis or Biological Big Bang yielded a better fit to the observed dependence between tree inconsistency and phylogenetic depth for the compressed cladogenesis model.ConclusionsHorizontal gene transfer is pervasive among prokaryotes: very few gene trees are fully consistent, making the original tree of life concept obsolete. A central trend that most probably represents vertical inheritance is discernible throughout the evolution of archaea and bacteria, although compressed cladogenesis complicates unambiguous resolution of the relationships between the major archaeal and bacterial clades.

| S-EPMC2737373 | biostudies-literature

Accurate phylogenetic tree reconstruction from quartets: a heuristic approach.

Project description:Supertree methods construct trees on a set of taxa (species) combining many smaller trees on the overlapping subsets of the entire set of taxa. A 'quartet' is an unrooted tree over 4 taxa, hence the quartet-based supertree methods combine many 4-taxon unrooted trees into a single and coherent tree over the complete set of taxa. Quartet-based phylogeny reconstruction methods have been receiving considerable attentions in the recent years. An accurate and efficient quartet-based method might be competitive with the current best phylogenetic tree reconstruction methods (such as maximum likelihood or Bayesian MCMC analyses), without being as computationally intensive. In this paper, we present a novel and highly accurate quartet-based phylogenetic tree reconstruction method. We performed an extensive experimental study to evaluate the accuracy and scalability of our approach on both simulated and biological datasets.

| S-EPMC4130513 | biostudies-literature

Estimating the Effective Sample Size of Tree Topologies from Bayesian Phylogenetic Analyses.

Project description:Bayesian phylogenetic analyses estimate posterior distributions of phylogenetic tree topologies and other parameters using Markov chain Monte Carlo (MCMC) methods. Before making inferences from these distributions, it is important to assess their adequacy. To this end, the effective sample size (ESS) estimates how many truly independent samples of a given parameter the output of the MCMC represents. The ESS of a parameter is frequently much lower than the number of samples taken from the MCMC because sequential samples from the chain can be non-independent due to autocorrelation. Typically, phylogeneticists use a rule of thumb that the ESS of all parameters should be greater than 200. However, we have no method to calculate an ESS of tree topology samples, despite the fact that the tree topology is often the parameter of primary interest and is almost always central to the estimation of other parameters. That is, we lack a method to determine whether we have adequately sampled one of the most important parameters in our analyses. In this study, we address this problem by developing methods to estimate the ESS for tree topologies. We combine these methods with two new diagnostic plots for assessing posterior samples of tree topologies, and compare their performance on simulated and empirical data sets. Combined, the methods we present provide new ways to assess the mixing and convergence of phylogenetic tree topologies in Bayesian MCMC analyses.

| S-EPMC5010905 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data