Generalized Bootstrap Supports for Phylogenetic Analyses of Protein Sequences Incorporating Alignment Uncertainty

Chatzou, Maria; Floden, Evan; Gascuel, Olivier; Notredame, Cédric

doi:10.1093/sysbio/syx096

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…The amino acid and nucleotide data were aligned using Fast Statistical Alignment (FSA v1.15.9; Bradley et al, 2009) with the default settings for peptide and the setting “–noanchored” for nucleotide. FSA has been shown to be one of the top-performing alignment programs (Bradley et al, 2009; Redelings, 2014), and does not rely upon a guide tree for sequence alignment, alleviating downstream bias (Bradley et al, 2009; Boyce, Sievers & Higgins, 2015; Chatzou et al, 2018). A maximum likelihood (ML) tree was then inferred for each gene using RAxMLv.8.2.4 (Stamatakis, 2014), with the PROTGAMMAAUTO and GTR+G models of evolution used for the amino acid and nucleotide data, respectively.…”

Section: Methodsmentioning

confidence: 99%

Characterizing gene tree conflict in plastome-inferred phylogenies

Walker

Walker-Hale

Vargas

et al. 2019

PeerJ

101

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Evolutionary relationships among plants have been inferred primarily using chloroplast data. To date, no study has comprehensively examined the plastome for gene tree conflict. Using a broad sampling of angiosperm plastomes, we characterize gene tree conflict among plastid genes at various time scales and explore correlates to conflict (e.g., evolutionary rate, gene length, molecule type). We uncover notable gene tree conflict against a backdrop of largely uninformative genes. We find alignment length and tree length are strong predictors of concordance, and that nucleotides outperform amino acids. Of the most commonly used markers, matK, greatly outperforms rbcL; however, the rarely used gene rpoC2 is the top-performing gene in every analysis. We find that rpoC2 reconstructs angiosperm phylogeny as well as the entire concatenated set of protein-coding chloroplast genes. Our results suggest that longer genes are superior for phylogeny reconstruction. The alleviation of some conflict through the use of nucleotides suggests that stochastic and systematic error is likely the root of most of the observed conflict, but further research on biological conflict within plastome is warranted given documented cases of heteroplasmic recombination. We suggest that researchers should filter genes for topological concordance when performing downstream comparative analyses on phylogenetic data, even when using chloroplast genomes.

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Section: Methodsmentioning

confidence: 99%

Characterizing gene tree conflict in plastome-inferred phylogenies

Walker

Walker-Hale

Vargas

et al. 2019

PeerJ

101

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“…Alternatives include other non-parametric resampling methods such as the jackknife (Tukey, 1958) and parametric resampling (Warnow, 2017;Felsenstein, 2004). Examples of the latter include MSA-specific confidence measures such as GUIDANCE1 (Landan and Graur, 2008;Penn et al, 2010), GUIDANCE2 (Sela et al, 2015), PSAR (Kim and Ma, 2011), T-COFFEE (Notredame et al, 2000), wpSBOOT (Chang et al, 2019), and Divvier (Ali et al, 2019) and parametric and/or specialpurpose MSA resampling or filtering techniques applied to the problem of phylogenetic support estimation, including TCS (Chang et al, 2014), the unistrap (Chatzou et al, 2018), Gblocks (Talavera and Castresana, 2007), Trimal (Capella-Gutiérrez et al, 2009), and the method of Rajan (2013). Both classes of alternative methods are less popular than the bootstrap.…”

Section: Introductionmentioning

confidence: 99%

Build a Better Bootstrap and the RAWR Shall Beat a Random Path to Your Door: Phylogenetic Support Estimation Revisited

Wang

Liu

2020

Preprint

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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. Special-purpose fully parametric or semi-parametric methods for phylogenetic support estimation have since been introduced, some of which are intended to address this concern. 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 as well as GUIDANCE2, a state-of-the-art purpose-built fully parametric method. Additional simulation study experiments help to clarify practical considerations regarding RAWR support estimation. We conclude with thoughts on future research directions and the untapped potential for sequence-aware non-parametric resampling and re-estimation. Availability: Data and software are publicly available under open-source software and open

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“…The impact of these sequence errors in downstream analyses is not yet fully understood, but it is reasonable to assume that they will adversely affect phylogenetic analyses. Persistence of non-homologous sequence stretches will negatively affect the quality of MSA, which is known to impact phylogenetic accuracy [1]. The non-homologous sequence itself might also cause problems.…”

Section: Introductionmentioning

confidence: 99%

Automated Removal of Non-homologous Sequence Stretches with PREQUAL

Irisarri

Burki

Whelan

2020

Methods in Molecular Biology

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Large-scale multigene datasets used in phylogenomics and comparative genomics often contain sequence errors inherited from source genomes and transcriptomes. These errors typically manifest as stretches of non-homologous characters and derive from sequencing, assembly, and/or annotation errors. The lack of automatic tools to detect and remove sequence errors leads to the propagation of these errors in large-scale datasets. PREQUAL is a command line tool that identifies and masks regions with non-homologous adjacent characters in sets of unaligned homologous sequences. PREQUAL uses a full probabilistic approach based on pair hidden Markov models. On the front end, PREQUAL is user-friendly and simple to use while also allowing full customization to adjust filtering sensitivity. It is primarily aimed at amino acid sequences but can handle protein-coding nucleotide sequences. PREQUAL is computationally efficient and shows high sensitivity and accuracy. In this chapter, we briefly introduce the motivation for PREQUAL and its underlying methodology, followed by a description of basic and advanced usage, and conclude with some notes and recommendations. PREQUAL fills an important gap in the current bioinformatics tool kit for phylogenomics, contributing toward increased accuracy and reproducibility in future studies.

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Generalized Bootstrap Supports for Phylogenetic Analyses of Protein Sequences Incorporating Alignment Uncertainty

Cited by 15 publications

References 33 publications

Characterizing gene tree conflict in plastome-inferred phylogenies

Characterizing gene tree conflict in plastome-inferred phylogenies

Build a Better Bootstrap and the RAWR Shall Beat a Random Path to Your Door: Phylogenetic Support Estimation Revisited

Automated Removal of Non-homologous Sequence Stretches with PREQUAL

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