We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.

https://www.science-open.com/document_file/00d79e6c-98e4-425c-9fd1-3a85d5fdefbc/PubMedCentral/00d79e6c-98e4-425c-9fd1-3a85d5fdefbc.pdf

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.

/pdf/modelfinder-fast-model-selection-for-accurate-phylogenetic-4hcz0zt4o8.pdf

ModelFinder: fast model selection for accurate phylogenetic estimates

Evolution of Protein Molecules

Motivation: Sequence-based methods to delimit species are central to DNA taxonomy, microbial community surveys and DNA metabarcoding studies. Current approaches either rely on simple sequence similarity thresholds (OTU-picking) or on complex and compute-intensive evolutionary models. The OTU-picking methods scale well on large datasets, but the results are highly sensitive to the similarity threshold. Coalescent-based species delimitation approaches often rely on Bayesian statistics and Markov Chain Monte Carlo sampling, and can therefore only be applied to small datasets. Results: We introduce the Poisson tree processes (PTP) model to infer putative species boundaries on a given phylogenetic input tree. We also integrate PTP with our evolutionary placement algorithm (EPA-PTP) to count the number of species in phylogenetic placements. We compare our approaches with popular OTU-picking methods and the General Mixed Yule Coalescent (GMYC) model. For de novo species delimitation, the stand-alone PTP model generally outperforms GYMC as well as OTU-picking methods when evolutionary distances between species are small. PTP neither requires an ultrametric input tree nor a sequence similarity threshold as input. In the open reference species delimitation approach, EPA-PTP yields more accurate results than de novo species delimitation methods. Finally, EPA-PTP scales on large datasets because it relies on the parallel implementations of the EPA and RAxML, thereby allowing to delimit species in high-throughput sequencing data. Availability and implementation: The code is freely available at www.

/pdf/a-general-species-delimitation-method-with-applications-to-32mb4uk65d.pdf

A general species delimitation method with applications to phylogenetic placements

Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions. We introduce ASTRAL-III, which substantially improves the running time of ASTRAL-II and guarantees polynomial running time as a function of both the number of species (n) and the number of genes (k). ASTRAL-III limits the bipartition constraint set (X) to grow at most linearly with n and k. Moreover, it handles polytomies more efficiently than ASTRAL-II, exploits similarities between gene trees better, and uses several techniques to avoid searching parts of the search space that are mathematically guaranteed not to include the optimal tree. The asymptotic running time of ASTRAL-III in the presence of polytomies is $O\left ((nk)^{1.726} D \right)$
 where D=O(nk) is the sum of degrees of all unique nodes in input trees. The running time improvements enable us to test whether contracting low support branches in gene trees improves the accuracy by reducing noise. In extensive simulations, we show that removing branches with very low support (e.g., below 10%) improves accuracy while overly aggressive filtering is harmful. We observe on a biological avian phylogenomic dataset of 14K genes that contracting low support branches greatly improve results. ASTRAL-III is a faster version of the ASTRAL method for phylogenetic reconstruction and can scale up to 10,000 species. With ASTRAL-III, low support branches can be removed, resulting in improved accuracy.

/pdf/astral-iii-polynomial-time-species-tree-reconstruction-from-166mfu5bjc.pdf

ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees.

Many methods exist for reconstructing phylogenies from molecular sequence data, but few phylogenies are known and can be used to check their efficacy. Simulation remains the most important approach to testing the accuracy and robustness of phylogenetic inference methods. However, current simulation programs are limited, especially concerning realistic models for simulating insertions and deletions. We implement a portable and flexible application, named INDELible, for generating nucleotide, amino acid and codon sequence data by simulating insertions and deletions (indels) as well as substitutions. Indels are simulated under several models of indel-length distribution. The program implements a rich repertoire of substitution models, including the general unrestricted model and nonstationary nonhomogeneous models of nucleotide substitution, mixture, and partition models that account for heterogeneity among sites, and codon models that allow the nonsynonymous/synonymous substitution rate ratio to vary among sites and branches. With its many unique features, INDELible should be useful for evaluating the performance of many inference methods, including those for multiple sequence alignment, phylogenetic tree inference, and ancestral sequence, or genome reconstruction.

/pdf/indelible-a-flexible-simulator-of-biological-sequence-3rjtl1rh0z.pdf

INDELible: a flexible simulator of biological sequence evolution.

The detection of positive Darwinian selection affecting protein-coding genes remains a topic of great interest and importance. The "branch-site" test is designed to detect localized episodic bouts of positive selection that affect only a few amino acid residues on particular lineages and has been shown to have reasonable power and low false-positive rates for a wide range of selection schemes. Previous simulations examining the performance of the test, however, were conducted under idealized conditions without insertions, deletions, or alignment errors. As the test is sometimes used to analyze divergent sequences, the impact of indels and alignment errors is a major concern. Here, we used a recently developed indel-simulation program to examine the false-positive rate and power of the branch-site test. We find that insertions and deletions do not cause excessive false positives if the alignment is correct, but alignment errors can lead to unacceptably high false positives. Of the alignment methods evaluated, PRANK consistently outperformed MUSCLE, MAFFT, and ClustalW, mostly because the latter programs tend to place nonhomologous codons (or amino acids) into the same column, producing shorter and less accurate alignments and giving the false impression that many amino acid substitutions have occurred at those sites. Our examination of two previous studies suggests that alignment errors may impact the analysis of mammalian and vertebrate genes by the branch-site test, and it is important to use reliable alignment methods.

/pdf/the-effect-of-insertions-deletions-and-alignment-errors-on-1dglqq059i.pdf

The Effect of Insertions, Deletions and Alignment Errors on the Branch-Site Test of Positive Selection

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.

/pdf/evolutionary-distances-in-the-twilight-zone-a-rational-4sbe7szkwg.pdf

William Fletcher

Papers

INDELible: a flexible simulator of biological sequence evolution.

The Effect of Insertions, Deletions and Alignment Errors on the Branch-Site Test of Positive Selection

Evolutionary distances in the twilight zone--a rational kernel approach.