Incorporating alignment uncertainty into Felsenstein's phylogenetic bootstrap to improve its reliability

A machine-learning-based alternative to phylogenetic bootstrap

MOTIVATION

Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic analyses, having accurate, fast, and interpretable scores is of high importance.

RESULTS

Here, we employed a data-driven approach to estimate branch support values with a probabilistic interpretation. To this end, we simulated thousands of realistic phylogenetic trees and the corresponding multiple sequence alignments. Each of the obtained alignments was used to infer the phylogeny using state-of-the-art phylogenetic inference software, which was then compared to the true tree. Using these extensive data, we trained machine-learning algorithms to estimate branch support values for each bipartition within the maximum-likelihood trees obtained by each software. Our results demonstrate that our model provides fast and more accurate probability-based branch support values than commonly used procedures. We demonstrate the applicability of our approach on empirical datasets.

AVAILABILITY AND IMPLEMENTATION

The data supporting this work are available in the Figshare repository at https://doi.org/10.6084/m9.figshare.25050554.v1, and the underlying code is accessible via GitHub at https://github.com/noaeker/bootstrap_repo.

Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny

Multiple sequence alignments are widely used to infer evolutionary relationships, enabling inferences of structure, function, and phylogeny. Standard practice is to construct one alignment by some preferred method and use it in further analysis; however, undetected alignment bias can be problematic. I describe Muscle5, a novel algorithm which constructs an ensemble of high-accuracy alignment with diverse biases by perturbing a hidden Markov model and permuting its guide tree. Confidence in an inference is assessed as the fraction of the ensemble which supports it. Applied to phylogenetic tree estimation, I show that ensembles can confidently resolve topologies with low bootstrap according to standard methods, and conversely that some topologies with high bootstraps are incorrect. Applied to the phylogeny of RNA viruses, ensemble analysis shows that recently adopted taxonomic phyla are probably polyphyletic. Ensemble analysis can improve confidence assessment in any inference from an alignment.

A survey on the algorithm and development of multiple sequence alignment

Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy. Therefore, developing an efficient and accurate strategy for MSA has become one of the research hotspots in bioinformatics. In this work, we mainly summarize the algorithms for MSA and its applications in bioinformatics. To provide a structured and clear perspective, we systematically introduce MSA's knowledge, including background, database, metric and benchmark. Besides, we list the most common applications of MSA in the field of bioinformatics, including database searching, phylogenetic analysis, genomic analysis, metagenomic analysis and protein analysis. Furthermore, we categorize and analyze classical and state-of-the-art algorithms, divided into progressive alignment, iterative algorithm, heuristics, machine learning and divide-and-conquer. Moreover, we also discuss the challenges and opportunities of MSA in bioinformatics. Our work provides a comprehensive survey of MSA applications and their relevant algorithms. It could bring valuable insights for researchers to contribute their knowledge to MSA and relevant studies.

An empirical test of the relationship between the bootstrap and likelihood ratio support in maximum likelihood phylogenetic analysis

In maximum likelihood (ML), the support for a clade can be calculated directly as the likelihood ratio (LR) or log-likelihood difference (S, LLD) of the best trees with and without the clade of interest. However, bootstrap (BS) clade frequencies are more pervasive in ML phylogenetics and are almost universally interpreted as measuring support. In addition to theoretical arguments against that interpretation, BS has several undesirable attributes for a support measure. For example, it does not vary in proportion to optimality or identify clades that are rejected by the evidence and can be overestimated due to missing data. Nevertheless, if BS is a reliable predictor of S, then it might be an efficient indirect method of measuring support-an attractive possibility, given the speed of many BS implementations. To assess the relationship between S and BS, we analyzed 106 empirical datasets retrieved from TreeBASE. Also, to evaluate the degree to which S and BS are affected by the number of replicates during suboptimal tree searches for S and pseudoreplicates during BS estimation, we randomly selected 5 of the 106 datasets and analyzed them using variable numbers of replicates and pseudoreplicates, respectively. The correlation between S and BS was extremely weak in the datasets we analyzed. Increasing the number of replicates during tree search decreased the estimated values of S for most clades, but the magnitude of change was small. In contrast, although increasing pseudoreplicates affected BS values for only approximately 40% of clades, values both increased and decreased, and they did so at much greater magnitudes. Increasing replicates/pseudoreplicates affected the rank order of clades in each tree for both S and BS. Our findings show decisively that BS is not an efficient indirect method of measuring support and suggest that even quite superficial searches to calculate S provide better estimates of support.

Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited

Motivation

The standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate ‘phylogenetic support’). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted.

Results

In this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR (‘RAndom Walk Resampling’). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the ‘mirrored inputs’ idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR’s performance is compared to the state-of-the-art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin’s finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution.

Availability and implementation

Data and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.