Accurate structural correlations from maximum likelihood superpositions.
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ABSTRACT:
SUBMITTER: Theobald DL
PROVIDER: S-EPMC2242818 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationPhylogenetic networks can represent non-treelike evolutionary scenarios. Current, actively developed approaches for phylogenetic network inference jointly account for non-treelike evolution and incomplete lineage sorting (ILS). Unfortunately, this induces a very high computational complexity and current tools can only analyze small datasets.ResultsWe present NetRAX, a tool for maximum likelihood (ML) inference of phylogenetic networks in the absence of ILS. Our tool leverages state-of-the-art methods for efficiently computing the phylogenetic likelihood function on trees, and extends them to phylogenetic networks via the notion of 'displayed trees'. NetRAX can infer ML phylogenetic networks from partitioned multiple sequence alignments and returns the inferred networks in Extended Newick format. On simulated data, our results show a very low relative difference in Bayesian Information Criterion (BIC) score and a near-zero unrooted softwired cluster distance to the true, simulated networks. With NetRAX, a network inference on a partitioned alignment with 8000 sites, 30 taxa and 3 reticulations completes within a few minutes on a standard laptop.Availability and implementationOur implementation is available under the GNU General Public License v3.0 at https://github.com/lutteropp/NetRAX.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9344847 | biostudies-literature
Project description:DNA methylation is a reaction that results in the formation of 5-methylcytosine when a methyl group is added to the cytosine’s C5 position. As organisms age, DNA methylation patterns change in a reproducible fashion. This phenomenon has established DNA methylation as a valuable biomarker in aging studies. Epigenetic clocks based on weighted combinations of methylation sites have been developed to accurately predict the age of an individual from their methylome. However, many epigenetic clocks, particularly those that utilize penalized regression, model the changes in methylation linearly with age. Moreover, these models, which use methylation levels as features, are not robust to missing data and do not account for the count-based nature of bisulfite sequence data. Additionally, the models are generally not interpretable. To overcome these challenges, we present BayesAge, an extension of the previously developed scAge approach that was developed for the analysis of single cell DNA methylation datasets. BayesAge utilizes maximum likelihood estimation (MLE) to infer ages, models count data using binomial distributions, and uses LOWESS smoothing to capture the non-linear dynamics between methylation and age. Our approach is designed for use with bulk bisulfite sequencing datasets. BayesAge outperforms scAge in several respects. Specifically, BayesAge’s age residuals are not age associated, thus providing a less biased representation of epigenetic age variation across populations. Moreover, BayesAge enables the estimation of error bounds on age inference and, when run on downsampled data, its coefficient of determination between predicted and actual ages surpasses both scAge and penalized regression.
2024-03-20 | GSE261769 | GEO
Project description:Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares or combines distances. This paper introduces a fast estimator for the covariance of two pairwise maximum likelihood distances, estimated under general Markov models. The estimator is based on a conjecture (going back to Nei & Jin, 1989) which links the covariance to path lengths. It is proven here under a simple symmetric substitution model. A simulation shows that the estimator outperforms previously published ones in terms of the mean squared error.
| S-EPMC4179615 | biostudies-literature
Project description:The likelihood-based approach to density modification [Terwilliger (2000), Acta Cryst. D56, 965-972] is extended to include the recognition of patterns of electron density. Once a region of electron density in a map is recognized as corresponding to a known structural element, the likelihood of the map is reformulated to include a term that reflects how closely the map agrees with the expected density for that structural element. This likelihood is combined with other aspects of the likelihood of the map, including the presence of a flat solvent region and the electron-density distribution in the protein region. This likelihood-based pattern-recognition approach was tested using the recognition of helical segments in a largely helical protein. The pattern-recognition method yields a substantial phase improvement over both conventional and likelihood-based solvent-flattening and histogram-matching methods. The method can potentially be used to recognize any common structural motif and incorporate prior knowledge about that motif into density modification.
| S-EPMC2745886 | biostudies-literature
Project description:A likelihood-based approach to density modification is developed that can be applied to a wide variety of cases where some information about the electron density at various points in the unit cell is available. The key to the approach consists of developing likelihood functions that represent the probability that a particular value of electron density is consistent with prior expectations for the electron density at that point in the unit cell. These likelihood functions are then combined with likelihood functions based on experimental observations and with others containing any prior knowledge about structure factors to form a combined likelihood function for each structure factor. A simple and general approach to maximizing the combined likelihood function is developed. It is found that this likelihood-based approach yields greater phase improvement in model and real test cases than either conventional solvent flattening and histogram matching or a recent reciprocal-space solvent-flattening procedure [Terwilliger (1999), Acta Cryst. D55, 1863-1871].
| S-EPMC2792768 | biostudies-literature
Project description:The genetic code is degenerate--most amino acids can be encoded by from two to as many as six different codons. The synonymous codons are not used with equal frequency: not only are some codons favored over others, but also their usage can vary significantly from species to species and between different genes in the same organism. Known causes of codon bias include differences in mutation rates as well as selection pressure related to the expression level of a gene, but the standard analysis methods can account for only a fraction of the observed codon usage variation. We here introduce an explicit model of codon usage bias, inspired by statistical physics. Combining this model with a maximum likelihood approach, we are able to clearly identify different sources of bias in various genomes. We have applied the algorithm to Saccharomyces cerevisiae as well as 325 prokaryote genomes, and in most cases our model explains essentially all observed variance.
| S-EPMC2441815 | biostudies-literature
Project description:Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer such epidemiological parameters as the average number of secondary infections Re and the infectious time from a phylogenetic tree (a genealogy of pathogen sequences). The representatives of this model family focus on various aspects of pathogen epidemics. For instance, the birth-death exposed-infectious (BDEI) model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters. With constantly growing sequencing data, MTBD models should be extremely useful for unravelling information on pathogen epidemics. However, existing implementations of these models in a phylodynamic framework have not yet caught up with the sequencing speed. Computing time and numerical instability issues limit their applicability to medium data sets (≤ 500 samples), while the accuracy of estimations should increase with more data. We propose a new highly parallelizable formulation of ordinary differential equations for MTBD models. We also extend them to forests to represent situations when a (sub-)epidemic started from several cases (e.g., multiple introductions to a country). We implemented it for the BDEI model in a maximum likelihood framework using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in two minutes on a phylogenetic tree of 10,000 samples. Comparison to the existing implementations on simulated data shows that it is not only much faster but also more accurate. An application of our tool to the 2014 Ebola epidemic in Sierra-Leone is also convincing, with very fast calculation and precise estimates. As MTBD models are closely related to Cladogenetic State Speciation and Extinction (ClaSSE)-like models, our findings could also be easily transferred to the macroevolution domain.
| S-EPMC10924745 | biostudies-literature
Project description:BACKGROUND:Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at clustering sub-conditions of cancers and other diseases. Hierarchical clustering methods, which can be categorized into agglomerative and divisive, have been widely used in such situations. However, unlike agglomerative methods divisive clustering approaches have consistently proved to be computationally expensive. RESULTS:The proposed clustering algorithm (DRAGON) was verified on mutation and microarray data, and was gauged against standard clustering methods in the literature. Its validation included synthetic and significant biological data. When validated on mixed-lineage leukemia data, DRAGON achieved the highest clustering accuracy with data of four different dimensions. Consequently, DRAGON outperformed previous methods with 3-,4- and 5-dimensional acute leukemia data. When tested on mutation data, DRAGON achieved the best performance with 2-dimensional information. CONCLUSIONS:This work proposes a computationally efficient divisive hierarchical clustering method, which can compete equally with agglomerative approaches. The proposed method turned out to correctly cluster data with distinct topologies. A MATLAB implementation can be extraced from http://www.riken.jp/en/research/labs/ims/med_sci_math/ or http://www.alok-ai-lab.com.
| S-EPMC5751574 | biostudies-literature
Project description:Phylogenetics plays a crucial role in the interpretation of genomic data1. Phylogenetic analyses of SARS-CoV-2 genomes have allowed the detailed study of the virus's origins2, of its international3,4 and local4-9 spread, and of the emergence10 and reproductive success11 of new variants, among many applications. These analyses have been enabled by the unparalleled volumes of genome sequence data generated and employed to study and help contain the pandemic12. However, preferred model-based phylogenetic approaches including maximum likelihood and Bayesian methods, mostly based on Felsenstein's 'pruning' algorithm13,14, cannot scale to the size of the datasets from the current pandemic4,15, hampering our understanding of the virus's evolution and transmission16. We present new approaches, based on reworking Felsenstein's algorithm, for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. We exploit near-certainty regarding ancestral genomes, and the similarities between closely related and densely sampled genomes, to greatly reduce computational demands for memory and time. Combined with new methods for searching amongst candidate evolutionary trees, this results in our MAPLE ('MAximum Parsimonious Likelihood Estimation') software giving better results than popular approaches such as FastTree 217, IQ-TREE 218, RAxML-NG19 and UShER15. Our approach therefore allows complex and accurate probabilistic phylogenetic analyses of millions of microbial genomes, extending the reach of genomic epidemiology. Future epidemiological datasets are likely to be even larger than those currently associated with COVID-19, and other disciplines such as metagenomics and biodiversity science are also generating huge numbers of genome sequences20-22. Our methods will permit continued use of preferred likelihood-based phylogenetic analyses.
| S-EPMC8963701 | biostudies-literature
Project description:BackgroundPhylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process.ResultsVestige was applied to a reference dataset of the SCL locus from four species and provided clear identification of the known conserved regions in this dataset. To demonstrate the flexibility to use diverse models of molecular evolution and dissect the nature of the evolutionary process Vestige was used to footprint the Ka/Ks ratio in primate BRCA1 with a codon model of evolution. Two regions of putative adaptive evolution were identified illustrating the ability of Vestige to represent the spatial distribution of distinct molecular evolutionary processes.ConclusionVestige provides a flexible, open platform for phylogenetic footprinting. Underpinned by the PyEvolve toolkit, Vestige provides a framework for visualising the signatures of evolutionary processes across the genome of numerous organisms simultaneously. By exploiting the maximum-likelihood statistical framework, the complex interplay between mutational processes, DNA repair and selection can be evaluated both spatially (along a sequence alignment) and temporally (for each branch of the tree) providing visual indicators to the attributes and functions of DNA sequences.
| S-EPMC1156870 | biostudies-literature