Title: Relative Efficiencies of Simple and Complex Substitution Models in Estimating Divergence Times in Phylogenomics
Abstract The conventional wisdom in molecular evolution is to apply parameter-rich models of nucleotide and amino acid substitutions for estimating divergence times. However, the actual extent of the difference between time estimates produced by highly complex models compared with those from simple models is yet to be quantified for contemporary data sets that frequently contain sequences from many species and genes. In a reanalysis of many large multispecies alignments from diverse groups of taxa, we found that the use of the simplest models can produce divergence time estimates and credibility intervals similar to those obtained from the complex models applied in the original studies. This result is surprising because the use of simple models underestimates sequence divergence for all the data sets analyzed. We found three fundamental reasons for the observed robustness of time estimates to model complexity in many practical data sets. First, the estimates of branch lengths and node-to-tip distances under the simplest model show an approximately linear relationship with those produced by using the most complex models applied on data sets with many sequences. Second, relaxed clock methods automatically adjust rates on branches that experience considerable underestimation of sequence divergences, resulting in time estimates that are similar to those from complex models. And, third, the inclusion of even a few good calibrations in an analysis can reduce the difference in time estimates from simple and complex models. The robustness of time estimates to model complexity in these empirical data analyses is encouraging, because all phylogenomics studies use statistical models that are oversimplified descriptions of actual evolutionary substitution processes. more »« less
Fields, Peter D.; Waneka, Gus; Naish, Matthew; Schatz, Michael C.; Henderson, Ian R.; Sloan, Daniel B.(
, Genome Biology and Evolution)
Slotte, Tanja
(Ed.)
Abstract Intracellular transfers of mitochondrial DNA continue to shape nuclear genomes. Chromosome 2 of the model plant Arabidopsis thaliana contains one of the largest known nuclear insertions of mitochondrial DNA (numts). Estimated at over 600 kb in size, this numt is larger than the entire Arabidopsis mitochondrial genome. The primary Arabidopsis nuclear reference genome contains less than half of the numt because of its structural complexity and repetitiveness. Recent data sets generated with improved long-read sequencing technologies (PacBio HiFi) provide an opportunity to finally determine the accurate sequence and structure of this numt. We performed a de novo assembly using sequencing data from recent initiatives to span the Arabidopsis centromeres, producing a gap-free sequence of the Chromosome 2 numt, which is 641 kb in length and has 99.933% nucleotide sequence identity with the actual mitochondrial genome. The numt assembly is consistent with the repetitive structure previously predicted from fiber-based fluorescent in situ hybridization. Nanopore sequencing data indicate that the numt has high levels of cytosine methylation, helping to explain its biased spectrum of nucleotide sequence divergence and supporting previous inferences that it is transcriptionally inactive. The original numt insertion appears to have involved multiple mitochondrial DNA copies with alternative structures that subsequently underwent an additional duplication event within the nuclear genome. This work provides insights into numt evolution, addresses one of the last unresolved regions of the Arabidopsis reference genome, and represents a resource for distinguishing between highly similar numt and mitochondrial sequences in studies of transcription, epigenetic modifications, and de novo mutations.
Hochachka, Wesley M.; Ruiz-Gutierrez, Viviana; Johnston, Alison(
, Ornithology)
Abstract
An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order to estimate the actual probability of occupancy (i.e. proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. Because the majority of eBird’s data are not collected in the form of repeated observations at individual locations, we explore 2 ways in which the single-visit records could be used in occupancy models. First, we assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. On average, eBird’s observers did not make their observations at locations that were representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Thus, the use of space-for-time substitution is not always appropriate. Second, we explored the utility of including data from single-visit records to supplement sets of repeated-visit data. In a simulation study we found that inclusion of single-visit records increased the precision of occupancy estimates, but only when detection probabilities are high. When detection probability was low, the addition of single-visit records exacerbated biases in estimates of occupancy probability. We conclude that subsets of data from eBird, and likely from similar projects, can be used for occupancy modeling either using space-for-time substitution or supplementing repeated-visit data with data from single-visit records. The appropriateness of either alternative will depend on the goals of a study and on the probabilities of detection and occupancy of the species of interest.
Beck, James D.; Roberts, Jessica M.; Kitzhaber, Joey M.; Trapp, Ashlyn; Serra, Edoardo; Spezzano, Francesca; Hayden, Eric J.(
, Frontiers in Molecular Biosciences)
Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection ( in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.
Hoyt, Savannah J.; Storer, Jessica M.; Hartley, Gabrielle A.; Grady, Patrick G.; Gershman, Ariel; de Lima, Leonardo G.; Limouse, Charles; Halabian, Reza; Wojenski, Luke; Rodriguez, Matias; et al(
, Science)
INTRODUCTION Transposable elements (TEs), repeat expansions, and repeat-mediated structural rearrangements play key roles in chromosome structure and species evolution, contribute to human genetic variation, and substantially influence human health through copy number variants, structural variants, insertions, deletions, and alterations to gene transcription and splicing. Despite their formative role in genome stability, repetitive regions have been relegated to gaps and collapsed regions in human genome reference GRCh38 owing to the technological limitations during its development. The lack of linear sequence in these regions, particularly in centromeres, resulted in the inability to fully explore the repeat content of the human genome in the context of both local and regional chromosomal environments. RATIONALE Long-read sequencing supported the complete, telomere-to-telomere (T2T) assembly of the pseudo-haploid human cell line CHM13. This resource affords a genome-scale assessment of all human repetitive sequences, including TEs and previously unknown repeats and satellites, both within and outside of gaps and collapsed regions. Additionally, a complete genome enables the opportunity to explore the epigenetic and transcriptional profiles of these elements that are fundamental to our understanding of chromosome structure, function, and evolution. Comparative analyses reveal modes of repeat divergence, evolution, and expansion or contraction with locus-level resolution. RESULTS We implemented a comprehensive repeat annotation workflow using previously known human repeats and de novo repeat modeling followed by manual curation, including assessing overlaps with gene annotations, segmental duplications, tandem repeats, and annotated repeats. Using this method, we developed an updated catalog of human repetitive sequences and refined previous repeat annotations. We discovered 43 previously unknown repeats and repeat variants and characterized 19 complex, composite repetitive structures, which often carry genes, across T2T-CHM13. Using precision nuclear run-on sequencing (PRO-seq) and CpG methylated sites generated from Oxford Nanopore Technologies long-read sequencing data, we assessed RNA polymerase engagement across retroelements genome-wide, revealing correlations between nascent transcription, sequence divergence, CpG density, and methylation. These analyses were extended to evaluate RNA polymerase occupancy for all repeats, including high-density satellite repeats that reside in previously inaccessible centromeric regions of all human chromosomes. Moreover, using both mapping-dependent and mapping-independent approaches across early developmental stages and a complete cell cycle time series, we found that engaged RNA polymerase across satellites is low; in contrast, TE transcription is abundant and serves as a boundary for changes in CpG methylation and centromere substructure. Together, these data reveal the dynamic relationship between transcriptionally active retroelement subclasses and DNA methylation, as well as potential mechanisms for the derivation and evolution of new repeat families and composite elements. Focusing on the emerging T2T-level assembly of the HG002 X chromosome, we reveal that a high level of repeat variation likely exists across the human population, including composite element copy numbers that affect gene copy number. Additionally, we highlight the impact of repeats on the structural diversity of the genome, revealing repeat expansions with extreme copy number differences between humans and primates while also providing high-confidence annotations of retroelement transduction events. CONCLUSION The comprehensive repeat annotations and updated repeat models described herein serve as a resource for expanding the compendium of human genome sequences and reveal the impact of specific repeats on the human genome. In developing this resource, we provide a methodological framework for assessing repeat variation within and between human genomes. The exhaustive assessment of the transcriptional landscape of repeats, at both the genome scale and locally, such as within centromeres, sets the stage for functional studies to disentangle the role transcription plays in the mechanisms essential for genome stability and chromosome segregation. Finally, our work demonstrates the need to increase efforts toward achieving T2T-level assemblies for nonhuman primates and other species to fully understand the complexity and impact of repeat-derived genomic innovations that define primate lineages, including humans. Telomere-to-telomere assembly of CHM13 supports repeat annotations and discoveries. The human reference T2T-CHM13 filled gaps and corrected collapsed regions (triangles) in GRCh38. Combining long read–based methylation calls, PRO-seq, and multilevel computational methods, we provide a compendium of human repeats, define retroelement expression and methylation profiles, and delineate locus-specific sites of nascent transcription genome-wide, including previously inaccessible centromeres. SINE, short interspersed element; SVA, SINE–variable number tandem repeat– Alu ; LINE, long interspersed element; LTR, long terminal repeat; TSS, transcription start site; pA, xxxxxxxxxxxxxxxx.
Simultaneous molecular dating of population and species divergences is essential in many biological investigations, including phylogeography, phylodynamics and species delimitation studies. In these investigations, multiple sequence alignments consist of both intra‐ and interspecies samples (mixed samples). As a result, the phylogenetic trees contain interspecies, interpopulation and within‐population divergences. Bayesian relaxed clock methods are often employed in these analyses, but they assume the same tree prior for both inter‐ and intraspecies branching processes and require specification of a clock model for branch rates (independent vs. autocorrelated rates models). We evaluated the impact of a single tree prior on Bayesian divergence time estimates by analysing computer‐simulated data sets. We also examined the effect of the assumption of independence of evolutionary rate variation among branches when the branch rates are autocorrelated. Bayesian approach with coalescent tree priors generally produced excellent molecular dates and highest posterior densities with high coverage probabilities. We also evaluated the performance of a non‐Bayesian method, RelTime, which does not require the specification of a tree prior or a clock model. RelTime's performance was similar to that of the Bayesian approach, suggesting that it is also suitable to analyse data sets containing both populations and species variation when its computational efficiency is needed.
Tao, Qiqing, Barba-Montoya, Jose, Huuki, Louise A, Durnan, Mary Kathleen, Kumar, Sudhir, and Thorne, Jeffrey. Relative Efficiencies of Simple and Complex Substitution Models in Estimating Divergence Times in Phylogenomics. Retrieved from https://par.nsf.gov/biblio/10174934. Molecular Biology and Evolution 37.6 Web. doi:10.1093/molbev/msaa049.
Tao, Qiqing, Barba-Montoya, Jose, Huuki, Louise A, Durnan, Mary Kathleen, Kumar, Sudhir, & Thorne, Jeffrey. Relative Efficiencies of Simple and Complex Substitution Models in Estimating Divergence Times in Phylogenomics. Molecular Biology and Evolution, 37 (6). Retrieved from https://par.nsf.gov/biblio/10174934. https://doi.org/10.1093/molbev/msaa049
Tao, Qiqing, Barba-Montoya, Jose, Huuki, Louise A, Durnan, Mary Kathleen, Kumar, Sudhir, and Thorne, Jeffrey.
"Relative Efficiencies of Simple and Complex Substitution Models in Estimating Divergence Times in Phylogenomics". Molecular Biology and Evolution 37 (6). Country unknown/Code not available. https://doi.org/10.1093/molbev/msaa049.https://par.nsf.gov/biblio/10174934.
@article{osti_10174934,
place = {Country unknown/Code not available},
title = {Relative Efficiencies of Simple and Complex Substitution Models in Estimating Divergence Times in Phylogenomics},
url = {https://par.nsf.gov/biblio/10174934},
DOI = {10.1093/molbev/msaa049},
abstractNote = {Abstract The conventional wisdom in molecular evolution is to apply parameter-rich models of nucleotide and amino acid substitutions for estimating divergence times. However, the actual extent of the difference between time estimates produced by highly complex models compared with those from simple models is yet to be quantified for contemporary data sets that frequently contain sequences from many species and genes. In a reanalysis of many large multispecies alignments from diverse groups of taxa, we found that the use of the simplest models can produce divergence time estimates and credibility intervals similar to those obtained from the complex models applied in the original studies. This result is surprising because the use of simple models underestimates sequence divergence for all the data sets analyzed. We found three fundamental reasons for the observed robustness of time estimates to model complexity in many practical data sets. First, the estimates of branch lengths and node-to-tip distances under the simplest model show an approximately linear relationship with those produced by using the most complex models applied on data sets with many sequences. Second, relaxed clock methods automatically adjust rates on branches that experience considerable underestimation of sequence divergences, resulting in time estimates that are similar to those from complex models. And, third, the inclusion of even a few good calibrations in an analysis can reduce the difference in time estimates from simple and complex models. The robustness of time estimates to model complexity in these empirical data analyses is encouraging, because all phylogenomics studies use statistical models that are oversimplified descriptions of actual evolutionary substitution processes.},
journal = {Molecular Biology and Evolution},
volume = {37},
number = {6},
author = {Tao, Qiqing and Barba-Montoya, Jose and Huuki, Louise A and Durnan, Mary Kathleen and Kumar, Sudhir and Thorne, Jeffrey},
}
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