BAli-Phy version 3: model-based co-estimation of alignment and phylogeny

Stepwise neofunctionalization of the NF-κB family member Rel during vertebrate evolution

Adaptive immunity and the five vertebrate NF-κB family members first emerged in cartilaginous fish, suggesting that NF-κB family divergence helped to facilitate adaptive immunity. One specialized function of the NF-κB Rel protein in macrophages is activation of Il12b, which encodes a key regulator of T cell development. We found that Il12b exhibits much greater Rel dependence than inducible innate immunity genes in macrophages, with the unique function of Rel dimers depending on a heightened intrinsic DNA-binding affinity. Chromatin immunoprecipitation followed by sequencing experiments defined differential DNA-binding preferences of NF-κB family members genome-wide, and X-ray crystallography revealed a key residue that supports the heightened DNA-binding affinity of Rel dimers. Unexpectedly, this residue, the heightened affinity of Rel dimers, and the portion of the Il12b promoter bound by Rel dimers were largely restricted to mammals. Our findings reveal major structural transitions in an NF-κB family member and one of its key target promoters at a late stage of vertebrate evolution that apparently contributed to immunoregulatory rewiring in mammalian species.

BetaAlign: a deep learning approach for multiple sequence alignment

MOTIVATION

Multiple sequence alignments (MSAs) are extensively used in biology, from phylogenetic reconstruction to structure and function prediction. Here, we suggest an out-of-the-box approach for the inference of MSAs, which relies on algorithms developed for processing natural languages. We show that our artificial intelligence (AI)-based methodology can be trained to align sequences by processing alignments that are generated via simulations, and thus different aligners can be easily generated for datasets with specific evolutionary dynamics attributes. We expect that natural language processing (NLP) solutions will replace or augment classic solutions for computing alignments, and more generally, challenging inference tasks in phylogenomics.

RESULTS

The MSA problem is a fundamental pillar in bioinformatics, comparative genomics, and phylogenetics. Here, we characterize and improve BetaAlign, the first deep learning aligner, which substantially deviates from conventional algorithms of alignment computation. BetaAlign draws on NLP techniques and trains transformers to map a set of unaligned biological sequences to an MSA. We show that our approach is highly accurate, comparable and sometimes better than state-of-the-art alignment tools. We characterize the performance of BetaAlign and the effect of various aspects on accuracy; for example, the size of the training data, the effect of different transformer architectures, and the effect of learning on a subspace of indel-model parameters (subspace learning). We also introduce a new technique that leads to improved performance compared to our previous approach. Our findings further uncover the potential of NLP-based methods for sequence alignment, highlighting that AI-based algorithms can substantially challenge classic approaches in phylogenomics and bioinformatics.

AVAILABILITY AND IMPLEMENTATION

Datasets used in this work are available on HuggingFace (Wolf et al. Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. p.38-45. 2020) at: https://huggingface.co/dotan1111. Source code is available at: https://github.com/idotan286/SimulateAlignments.

The macroevolutionary dynamics of pharyngognathy in fishes fail to support the key innovation hypothesis

Key innovations, traits that provide species access to novel niches, are thought to be a major generator of biodiversity. One commonly cited example of key innovation is pharyngognathy, a set of modifications to the pharyngeal jaws found in some highly species-rich fish clades such as cichlids and wrasses. Here, using comparative phylogenomics and phylogenetic comparative methods, we investigate the genomic basis of pharyngognathy and the impact of this innovation on diversification. Whole genomes resolve the relationships of fish clades with this innovation and their close relatives, but high levels of topological discordance suggest the innovation may have evolved fewer times than previously thought. Closer examination of the topology of noncoding elements accelerated in clades with the pharyngognathy innovation reveals hidden patterns of shared ancestry across putatively independent transitions to pharyngognathy. When our updated phylogenomic relationships are used alongside large-scale phylogenetic and ecological datasets, we find no evidence pharyngognathy consistently modifies the macroevolutionary landscape of trophic ecology nor does it increase diversification. Our results highlight the necessity of incorporating genomic information in studies of key innovation.

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.

How Trustworthy Is Your Tree? Bayesian Phylogenetic Effective Sample Size Through the Lens of Monte Carlo Error

Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) run explores the space of phylogenetic trees. In this paper, we investigate the Monte Carlo error of phylogenies, focusing on high-dimensional summaries of the posterior distribution, including variability in estimated edge/branch (known in phylogenetics as "split") probabilities and tree probabilities, and variability in the estimated summary tree. Specifically, we ask if there is any measure of effective sample size (ESS) applicable to phylogenetic trees which is capable of capturing the Monte Carlo error of these three summary measures. We find that there are some ESS measures capable of capturing the error inherent in using MCMC samples to approximate the posterior distributions on phylogenies. We term these tree ESS measures, and identify a set of three which are useful in practice for assessing the Monte Carlo error. Lastly, we present visualization tools that can improve comparisons between multiple independent MCMC runs by accounting for the Monte Carlo error present in each chain. Our results indicate that common post-MCMC workflows are insufficient to capture the inherent Monte Carlo error of the tree, and highlight the need for both within-chain mixing and between-chain convergence assessments.

Convergent genomic signatures associated with vertebrate viviparity

BACKGROUND

Viviparity-live birth-is a complex and innovative mode of reproduction that has evolved repeatedly across the vertebrate Tree of Life. Viviparous species exhibit remarkable levels of reproductive diversity, both in the amount of care provided by the parent during gestation, and the ways in which that care is delivered. The genetic basis of viviparity has garnered increasing interest over recent years; however, such studies are often undertaken on small evolutionary timelines, and thus are not able to address changes occurring on a broader scale. Using whole genome data, we investigated the molecular basis of this innovation across the diversity of vertebrates to answer a long held question in evolutionary biology: is the evolution of convergent traits driven by convergent genomic changes?

RESULTS

We reveal convergent changes in protein family sizes, protein-coding regions, introns, and untranslated regions (UTRs) in a number of distantly related viviparous lineages. Specifically, we identify 15 protein families showing evidence of contraction or expansion associated with viviparity. We additionally identify elevated substitution rates in both coding and noncoding sequences in several viviparous lineages. However, we did not find any convergent changes-be it at the nucleotide or protein level-common to all viviparous lineages.

CONCLUSIONS

Our results highlight the value of macroevolutionary comparative genomics in determining the genomic basis of complex evolutionary transitions. While we identify a number of convergent genomic changes that may be associated with the evolution of viviparity in vertebrates, there does not appear to be a convergent molecular signature shared by all viviparous vertebrates. Ultimately, our findings indicate that a complex trait such as viviparity likely evolves with changes occurring in multiple different pathways.

AGO, a Framework for the Reconstruction of Ancestral Syntenies and Gene Orders

Reconstructing ancestral gene orders from the genome data of extant species is an important problem in comparative and evolutionary genomics. In a phylogenomics setting that accounts for gene family evolution through gene duplication and gene loss, the reconstruction of ancestral gene orders involves several steps, including multiple sequence alignment, the inference of reconciled gene trees, and the inference of ancestral syntenies and gene adjacencies. For each of the steps of such a process, several methods can be used and implemented using a growing corpus of, often parameterized, tools; in practice, interfacing such tools into an ancestral gene order reconstruction pipeline is far from trivial. This chapter introduces AGO, a Python-based framework aimed at creating ancestral gene order reconstruction pipelines allowing to interface and parameterize different bioinformatics tools. The authors illustrate the features of AGO by reconstructing ancestral gene orders for the X chromosome of three ancestral Anopheles species using three different pipelines. AGO is freely available at https://github.com/cchauve/AGO-pipeline .

Beginner's Guide on the Use of PAML to Detect Positive Selection

The CODEML program in the PAML package has been widely used to analyze protein-coding gene sequences to estimate the synonymous and nonsynonymous rates (dS and dN) and to detect positive Darwinian selection driving protein evolution. For users not familiar with molecular evolutionary analysis, the program is known to have a steep learning curve. Here, we provide a step-by-step protocol to illustrate the commonly used tests available in the program, including the branch models, the site models, and the branch-site models, which can be used to detect positive selection driving adaptive protein evolution affecting particular lineages of the species phylogeny, affecting a subset of amino acid residues in the protein, and affecting a subset of sites along prespecified lineages, respectively. A data set of the myxovirus (Mx) genes from ten mammal and two bird species is used as an example. We discuss a new feature in CODEML that allows users to perform positive selection tests for multiple genes for the same set of taxa, as is common in modern genome-sequencing projects. The PAML package is distributed at https://github.com/abacus-gene/paml under the GNU license, with support provided at its discussion site (https://groups.google.com/g/pamlsoftware). Data files used in this protocol are available at https://github.com/abacus-gene/paml-tutorial.

Roadmap to the study of gene and protein phylogeny and evolution-A practical guide

Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.

Chromosome segregation fidelity requires microtubule polyglutamylation by the cancer downregulated enzyme TTLL11

Regulation of microtubule (MT) dynamics is key for mitotic spindle assembly and faithful chromosome segregation. Here we show that polyglutamylation, a still understudied post-translational modification of spindle MTs, is essential to define their dynamics within the range required for error-free chromosome segregation. We identify TTLL11 as an enzyme driving MT polyglutamylation in mitosis and show that reducing TTLL11 levels in human cells or zebrafish embryos compromises chromosome segregation fidelity and impairs early embryonic development. Our data reveal a mechanism to ensure genome stability in normal cells that is compromised in cancer cells that systematically downregulate TTLL11. Our data suggest a direct link between MT dynamics regulation, MT polyglutamylation and two salient features of tumour cells, aneuploidy and chromosome instability (CIN).

Accurate Large-scale Phylogeny-Aware Alignment using BAli-Phy

MOTIVATION

BAli-Phy, a popular Bayesian method that co-estimates multiple sequence alignments and phylogenetic trees, is a rigorous statistical method, but due to its computational requirements, it has generally been limited to relatively small datasets (at most about 100 sequences). Here we repurpose BAli-Phy as a ``phylogeny-aware" alignment method: we estimate the phylogeny from the input of unaligned sequences, and then use that as a fixed tree within BAli-Phy.

RESULTS

We show that this approach achieves high accuracy, greatly superior to Prank, the current most popular phylogeny-aware alignment method, and is even more accurate than MAFFT, one of the top performing alignment methods in common use. Furthermore, this approach can be used to align very large datasets (up to 1000 sequences in this study).

AVAILABILITY

See https://doi.org/10.13012/B2IDB-7863273_V1 for datasets used in this study.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.