Class of multiple sequence alignment algorithm affects genomic analysis

Evaluating Compression-Based Phylogeny Estimation in the Presence of Incomplete Lineage Sorting

This study assesses characteristics of the normalized compression distance (NCD) technique for building phylogenetic trees from molecular data. We examined results from a mammalian biological data set as well as a collection of simulated data with varying levels of incomplete lineage sorting. The implementation of NCD we analyze is a concatenation-based, distance-based, alignment-free, and model-free phylogeny estimation method, which takes concatenated unaligned sequence data as input and outputs a matrix of distances. We compare the NCD phylogeny estimation method with various other methods, including coalescent- and concatenation-based methods.

UPP2: fast and accurate alignment of datasets with fragmentary sequences

MOTIVATION

Multiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full-length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets.

RESULTS

We present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show that UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity and is among the most accurate otherwise.

AVAILABILITY AND IMPLEMENTATION

https://github.com/gillichu/sepp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intraspecies variation of the mitochondrial genome: An evaluation for phylogenetic approaches based on the conventional choices of genes and segments on mitogenome

Intraspecies nucleotide sequence variation is a key to understanding the evolutionary history of a species, such as the geographic distribution and population structure. To date, numerous phylogenetic and population genetics studies have been conducted based on the sequences of a gene or an intergenic region on the mitochondrial genome (mtDNA), such as cytochrome c oxidase subunits or the D-loop. To evaluate the credibility of the usage of such ‘classic’ markers, we compared the phylogenetic inferences based on the analyses of the partial and entire mtDNA sequences. Importantly, the phylogenetic reconstruction based on the short marker sequences did not necessarily reproduce the tree topologies based on the analyses of the entire mtDNA. In addition, analyses on the datasets of various organisms revealed that the analyses based on the classic markers yielded phylogenetic trees with poor confidence in all tested cases compared to the results based on full-length mtDNA. These results demonstrated that phylogenetic analyses based on complete mtDNA sequences yield more insightful results compared to those based on mitochondrial genes and segments. To ameliorate the shortcomings of the classic markers, we identified a segment of mtDNA that may be used as an ‘approximate marker’ to closely reproduce the phylogenetic inference obtained from the entire mtDNA in the case of mammalian species, which can be utilized to design amplicon-seq-based studies. Our study demonstrates the importance of the choice of mitochondrial markers for phylogenetic analyses and proposes a novel approach to choosing appropriate markers for mammalian mtDNA that reproduces the phylogenetic inferences obtained from full-length mtDNA.

Post-Alignment Adjustment and Its Automation

Multiple sequence alignment (MSA) is the basis for almost all sequence comparison and molecular phylogenetic inferences. Large-scale genomic analyses are typically associated with automated progressive MSA without subsequent manual adjustment, which itself is often error-prone because of the lack of a consistent and explicit criterion. Here, I outlined several commonly encountered alignment errors that cannot be avoided by progressive MSA for nucleotide, amino acid, and codon sequences. Methods that could be automated to fix such alignment errors were then presented. I emphasized the utility of position weight matrix as a new tool for MSA refinement and illustrated its usage by refining the MSA of nucleotide and amino acid sequences. The main advantages of the position weight matrix approach include (1) its use of information from all sequences, in contrast to other commonly used methods based on pairwise alignment scores and inconsistency measures, and (2) its speedy computation, making it suitable for a large number of long viral genomic sequences.

Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants

Background

Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools-these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect.

Methods

Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations.

Results

As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as 'benign'. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as 'benign'. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms.

Conclusion

In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.

Coelacanth SERINC2 Inhibits HIV-1 Infectivity and Is Counteracted by Envelope Glycoprotein from Foamy Virus

SERINC5 restricts nef-defective HIV-1 by affecting early steps of the virus life cycle. Distantly related retroviruses with a wide host range encode virulent factors in response to challenge by SERINC5. However, the evolutionary origins of this antiretroviral activity, its prevalence among the paralogs, and its ability to target retroviruses remain understudied. In agreement with previous studies, we found that four human SERINC paralogs inhibit nef-defective HIV-1, with SERINC2 being an exception. Here, we demonstrate that this lack of activity in human SERINC2 is associated with its post-whole-genome duplication (post-WGD) divergence, as evidenced by the ability of pre-WGD orthologs from Saccharomyces cerevisiae and flies and a post-WGD-proximate SERINC2 from coelacanths to inhibit the virus. Intriguingly, Nef is unable to counter coelacanth SERINC2, indicating that such activity was directed toward other retroviruses found in coelacanths (like foamy viruses). However, foamy virus-derived vectors are intrinsically resistant to the action of SERINC2, and we show that the foamy virus envelope confers this resistance by affecting its steady-state levels. Our study highlights an ancient origin of antiretroviral activity in SERINCs and a hitherto-unknown interaction with a foamy virus.

IMPORTANCESERINC5 constitutes a critical barrier to the propagation of retroviruses, as highlighted by parallel emergence of anti-SERINC5 activities among distant retroviral lineages. Therefore, understanding the origin and evolution of these host factors will provide key information about virus-host relationships that can be exploited for future drug development. Here, we show that SERINC5-mediated nef-defective HIV-1 infection inhibition is evolutionarily conserved. SERINC2 from coelacanth restricts HIV-1, and it was functionally adapted to target foamy viruses. Our findings provide insights into the evolutionary origin of antiretroviral activity in the SERINC gene family and uncover the role of SERINCs in shaping the long-term conflicts between retroviruses and their hosts.

Bioinformatics resources for SARS-CoV-2 discovery and surveillance

In early January 2020, the novel coronavirus (SARS-CoV-2) responsible for a pneumonia outbreak in Wuhan, China, was identified using next-generation sequencing (NGS) and readily available bioinformatics pipelines. In addition to virus discovery, these NGS technologies and bioinformatics resources are currently being employed for ongoing genomic surveillance of SARS-CoV-2 worldwide, tracking its spread, evolution and patterns of variation on a global scale. In this review, we summarize the bioinformatics resources used for the discovery and surveillance of SARS-CoV-2. We also discuss the advantages and disadvantages of these bioinformatics resources and highlight areas where additional technical developments are urgently needed. Solutions to these problems will be beneficial not only to the prevention and control of the current COVID-19 pandemic but also to infectious disease outbreaks of the future.

Loss of inner kinetochore genes is associated with the transition to an unconventional point centromere in budding yeast

Background

The genomic sequences of centromeres, as well as the set of proteins that recognize and interact with centromeres, are known to quickly diverge between lineages potentially contributing to post-zygotic reproductive isolation. However, the actual sequence of events and processes involved in the divergence of the kinetochore machinery is not known. The patterns of gene loss that occur during evolution concomitant with phenotypic changes have been used to understand the timing and order of molecular changes.

Methods

I screened the high-quality genomes of twenty budding yeast species for the presence of well-studied kinetochore genes. Based on the conserved gene order and complete genome assemblies, I identified gene loss events. Subsequently, I searched the intergenic regions to identify any un-annotated genes or gene remnants to obtain additional evidence of gene loss.

Results

My analysis identified the loss of four genes (NKP1, NKP2, CENPL/IML3 and CENPN/CHL4) of the inner kinetochore constitutive centromere-associated network (CCAN/also known as CTF19 complex in yeast) in both the Naumovozyma species for which genome assemblies are available. Surprisingly, this collective loss of four genes of the CCAN/CTF19 complex coincides with the emergence of unconventional centromeres in N. castellii and N. dairenensis. My study suggests a tentative link between the emergence of unconventional point centromeres and the turnover of kinetochore genes in budding yeast.

A new phylogenetic protocol: dealing with model misspecification and confirmation bias in molecular phylogenetics

Molecular phylogenetics plays a key role in comparative genomics and has increasingly significant impacts on science, industry, government, public health and society. In this paper, we posit that the current phylogenetic protocol is missing two critical steps, and that their absence allows model misspecification and confirmation bias to unduly influence phylogenetic estimates. Based on the potential offered by well-established but under-used procedures, such as assessment of phylogenetic assumptions and tests of goodness of fit, we introduce a new phylogenetic protocol that will reduce confirmation bias and increase the accuracy of phylogenetic estimates.

Major Revisions in Arthropod Phylogeny Through Improved Supermatrix, With Support for Two Possible Waves of Land Invasion by Chelicerates

Deep phylogeny involving arthropod lineages is difficult to recover because the erosion of phylogenetic signals over time leads to unreliable multiple sequence alignment (MSA) and subsequent phylogenetic reconstruction. One way to alleviate the problem is to assemble a large number of gene sequences to compensate for the weakness in each individual gene. Such an approach has led to many robustly supported but contradictory phylogenies. A close examination shows that the supermatrix approach often suffers from two shortcomings. The first is that MSA is rarely checked for reliability and, as will be illustrated, can be poor. The second is that, to alleviate the problem of homoplasy at the third codon position of protein-coding genes due to convergent evolution of nucleotide frequencies, phylogeneticists may remove or degenerate the third codon position but may do it improperly and introduce new biases. We performed extensive reanalysis of one of such "big data" sets to highlight these two problems, and demonstrated the power and benefits of correcting or alleviating these problems. Our results support a new group with Xiphosura and Arachnopulmonata (Tetrapulmonata + Scorpiones) as sister taxa. This favors a new hypothesis in which the ancestor of Xiphosura and the extinct Eurypterida (sea scorpions, of which many later forms lived in brackish or freshwater) returned to the sea after the initial chelicerate invasion of land. Our phylogeny is supported even with the original data but processed with a new "principled" codon degeneration. We also show that removing the 1673 codon sites with both AGN and UCN codons (encoding serine) in our alignment can partially reconcile discrepancies between nucleotide-based and AA-based tree, partly because two sequences, one with AGN and the other with UCN, would be identical at the amino acid level but quite different at the nucleotide level.

Exploring the impact of morphology, multiple sequence alignment and choice of optimality criteria in phylogenetic inference: a case study with the Neotropical orb-weaving spider genus Wagneriana (Araneae: Araneidae)

We present a total evidence phylogenetic analysis of the Neotropical orb-weaving spider genus Wagneriana and discuss the phylogenetic impacts of methodological choices. We analysed 167 phenotypic characters and nine loci scored for 115 Wagneriana and outgroups, including 46 newly sequenced species. We compared total evidence analyses and molecular-only analyses to evaluate the impact of phenotypic evidence, and we performed analyses using the programs POY, TNT, RAxML, GARLI, IQ-TREE and MrBayes to evaluate the effects of multiple sequence alignment and optimality criteria. In all analyses, Wagneriana carimagua and Wagneriana uropygialis were nested in the genera Parawixia and Alpaida, respectively, and the remaining species of Wagneriana fell into three main clades, none of which formed a pair of sister taxa. However, sister-group relationships among the main clades and their internal relationships were strongly influenced by methodological choices. Alignment methods had comparable topological effects to those of optimality criteria in terms of ‘subtree pruning and regrafting’ moves. The inclusion of phenotypic evidence, 2.80–3.05% of the total evidence matrices, increased support irrespective of the optimality criterion used. The monophyly of some groups was recovered only after the addition of morphological characters. A new araneid genus, Popperaneus gen. nov., is erected, and Paraverrucosa is resurrected. Four new synonymies and seven new combinations are proposed.

ASPEN, a methodology for reconstructing protein evolution with improved accuracy using ensemble models

Evolutionary reconstruction algorithms produce models of the evolutionary history of proteins or species. Such algorithms are highly sensitive to their inputs: the sequences used and their alignments. Here, we asked whether the variance introduced by selecting different input sequences could be used to better identify accurate evolutionary models. We subsampled from available ortholog sequences and measured the distribution of observed relationships between paralogs produced across hundreds of models inferred from the subsamples. We observed two important phenomena. First, the reproducibility of an all-sequence, single-alignment reconstruction, measured by comparing topologies inferred from 90% subsamples, directly correlates with the accuracy of that single-alignment reconstruction, producing a measurable value for something that has been traditionally unknowable. Second, topologies that are most consistent with the observations made in the ensemble are more accurate and we present a meta algorithm that exploits this property to improve model accuracy.

Identifying Clusters of High Confidence Homologies in Multiple Sequence Alignments

Multiple sequence alignment (MSA) is ubiquitous in evolution and bioinformatics. MSAs are usually taken to be a known and fixed quantity on which to perform downstream analysis despite extensive evidence that MSA accuracy and uncertainty affect results. These errors are known to cause a wide range of problems for downstream evolutionary inference, ranging from false inference of positive selection to long branch attraction artifacts. The most popular approach to dealing with this problem is to remove (filter) specific columns in the MSA that are thought to be prone to error. Although popular, this approach has had mixed success and several studies have even suggested that filtering might be detrimental to phylogenetic studies. We present a graph-based clustering method to address MSA uncertainty and error in the software Divvier (available at https://github.com/simonwhelan/Divvier), which uses a probabilistic model to identify clusters of characters that have strong statistical evidence of shared homology. These clusters can then be used to either filter characters from the MSA (partial filtering) or represent each of the clusters in a new column (divvying). We validate Divvier through its performance on real and simulated benchmarks, finding Divvier substantially outperforms existing filtering software by retaining more true pairwise homologies calls and removing more false positive pairwise homologies. We also find that Divvier, in contrast to other filtering tools, can alleviate long branch attraction artifacts induced by MSA and reduces the variation in tree estimates caused by MSA uncertainty.

Evaluating Statistical Multiple Sequence Alignment in Comparison to Other Alignment Methods on Protein Data Sets

The estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical coestimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical coestimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy has better precision and recall (with respect to the true alignments) than the other alignment methods on the simulated data sets but has consistently lower recall on the biological benchmarks (with respect to the reference alignments) than many of the other methods. In other words, we find that BAli-Phy systematically underaligns when operating on biological sequence data but shows no sign of this on simulated data. There are several potential causes for this change in performance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments, and future research is needed to determine the most likely explanation. We conclude with a discussion of the potential ramifications for each of these possibilities. [BAli-Phy; homology; multiple sequence alignment; protein sequences; structural alignment.]

Six Impossible Things before Breakfast: Assumptions, Models, and Belief in Molecular Dating

Confidence in molecular dating analyses has grown with the increasing sophistication of the methods. Some problematic cases where molecular dates disagreed with paleontological estimates appear to have been resolved with a growing agreement between molecules and fossils. But we cannot relax just yet. The growing analytical sophistication of many molecular dating methods relies on an increasingly large number of assumptions about evolutionary history and processes. Many of these assumptions are based on statistical tractability rather than being informed by improved understanding of molecular evolution, yet changing the assumptions can influence molecular dates. How can we tell if the answers we get are driven more by the assumptions we make than by the molecular data being analyzed?

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

Motivation

Most evolutionary analyses are based on pre-estimated multiple sequence alignment. Wong et al. established the existence of an uncertainty induced by multiple sequence alignment when reconstructing phylogenies. They were able to show that in many cases different aligners produce different phylogenies, with no simple objective criterion sufficient to distinguish among these alternatives.

Results

We demonstrate that incorporating MSA induced uncertainty into bootstrap sampling can significantly increase correlation between clade correctness and its corresponding bootstrap value. Our procedure involves concatenating several alternative multiple sequence alignments of the same sequences, produced using different commonly used aligners. We then draw bootstrap replicates while favoring columns of the more unique aligner among the concatenated aligners. We named this concatenation and bootstrapping method, Weighted Partial Super Bootstrap (wpSBOOT). We show on three simulated datasets of 16, 32 and 64 tips that our method improves the predictive power of bootstrap values. We also used as a benchmark an empirical collection of 853 1-to-1 orthologous genes from seven yeast species and found wpSBOOT to significantly improve discrimination capacity between topologically correct and incorrect trees. Bootstrap values of wpSBOOT are comparable to similar readouts estimated using a single method. However, for reduced trees by 50% and 95% bootstrap thresholds, wpSBOOT comes out the lowest Type I error (less FP).

Availability

The automated generation of replicates has been implemented in the T-Coffee package, which is available as open source freeware available from www.tcoffee.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Split-inducing indels in phylogenomic analysis

Background

Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.

Results

Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.

Conclusions

Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

Electronic supplementary material

The online version of this article (10.1186/s13015-018-0130-7) contains supplementary material, which is available to authorized users.

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

Phylogenetic tree inference is a critical component of many systematic and evolutionary studies. The majority of these studies are based on the two-step process of multiple sequence alignment followed by tree inference, despite persistent evidence that the alignment step can lead to biased results. Here we present a two-part study that first presents PaHMM-Tree, a novel neighbor joining-based method that estimates pairwise distances without assuming a single alignment. We then use simulations to benchmark its performance against a wide-range of other phylogenetic tree inference methods, including the first comparison of alignment-free distance-based methods against more conventional tree estimation methods. Our new method for calculating pairwise distances based on statistical alignment provides distance estimates that are as accurate as those obtained using standard methods based on the true alignment. Pairwise distance estimates based on the two-step process tend to be substantially less accurate. This improved performance carries through to tree inference, where PaHMM-Tree provides more accurate tree estimates than all of the pairwise distance methods assessed. For close to moderately divergent sequence data we find that the two-step methods using statistical inference, where information from all sequences is included in the estimation procedure, tend to perform better than PaHMM-Tree, particularly full statistical alignment, which simultaneously estimates both the tree and the alignment. For deep divergences we find the alignment step becomes so prone to error that our distance-based PaHMM-Tree outperforms all other methods of tree inference. Finally, we find that the accuracy of alignment-free methods tends to decline faster than standard two-step methods in the presence of alignment uncertainty, and identify no conditions where alignment-free methods are equal to or more accurate than standard phylogenetic methods even in the presence of substantial alignment error. [Alignment-free; distance-based phylogenetics; pair Hidden Markov Models; phylogenetic inference; statistical alignment.].

Inferring Trees

Molecular evolution can reveal the relationship between sets of homologous sequences and the patterns of change that occur during their evolution. An important aspect of these studies is the inference of a phylogenetic tree, which explicitly describes evolutionary relationships between homologous sequences. This chapter provides an introduction to evolutionary trees and how to infer them from sequence data using some commonly used inferential methodology. It focuses on statistical methods for inferring trees and how to assess the confidence one should have in any resulting tree, with a particular emphasis on the underlying assumptions of the methods and how they might affect the tree estimate. There is also some discussion of the underlying algorithms used to perform tree search and recommendations regarding the performance of different algorithms. Finally, there are a few practical guidelines, including how to combine multiple software packages to improve inference, and a comparison between Bayesian and Maximum likelihood phylogenetics.

General continuous-time Markov model of sequence evolution via insertions/deletions: local alignment probability computation

Background

Insertions and deletions (indels) account for more nucleotide differences between two related DNA sequences than substitutions do, and thus it is imperative to develop a method to reliably calculate the occurrence probabilities of sequence alignments via evolutionary processes on an entire sequence. Previously, we presented a perturbative formulation that facilitates the ab initio calculation of alignment probabilities under a continuous-time Markov model, which describes the stochastic evolution of an entire sequence via indels with quite general rate parameters. And we demonstrated that, under some conditions, the ab initio probability of an alignment can be factorized into the product of an overall factor and contributions from regions (or local alignments) delimited by gapless columns.

Results

Here, using our formulation, we attempt to approximately calculate the probabilities of local alignments under space-homogeneous cases. First, for each of all types of local pairwise alignments (PWAs) and some typical types of local multiple sequence alignments (MSAs), we numerically computed the total contribution from all parsimonious indel histories and that from all next-parsimonious histories, and compared them. Second, for some common types of local PWAs, we derived two integral equation systems that can be numerically solved to give practically exact solutions. We compared the total parsimonious contribution with the practically exact solution for each such local PWA. Third, we developed an algorithm that calculates the first-approximate MSA probability by multiplying total parsimonious contributions from all local MSAs. Then we compared the first-approximate probability of each local MSA with its absolute frequency in the MSAs created via a genuine sequence evolution simulator, Dawg. In all these analyses, the total parsimonious contributions approximated the multiplication factors fairly well, as long as gap sizes and branch lengths are at most moderate. Examination of the accuracy of another indel probabilistic model in the light of our formulation indicated some modifications necessary for the model’s accuracy improvement.

Conclusions

At least under moderate conditions, the approximate methods can quite accurately calculate ab initio alignment probabilities under biologically more realistic models than before. Thus, our formulation will provide other indel probabilistic models with a sound reference point.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1167-6) contains supplementary material, which is available to authorized users.

A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference

Molecular phylogenetic inference is inherently dependent on choices in both methodology and data. Many insightful studies have shown how choices in methodology, such as the model of sequence evolution or optimality criterion used, can strongly influence inference. In contrast, much less is known about the impact of choices in the properties of the data, typically genes, on phylogenetic inference. We investigated the relationships between 52 gene properties (24 sequence-based, 19 function-based, and 9 tree-based) with each other and with three measures of phylogenetic signal in two assembled data sets of 2,832 yeast and 2,002 mammalian genes. We found that most gene properties, such as evolutionary rate (measured through the percent average of pairwise identity across taxa) and total tree length, were highly correlated with each other. Similarly, several gene properties, such as gene alignment length, Guanine-Cytosine content, and the proportion of tree distance on internal branches divided by relative composition variability (treeness/RCV), were strongly correlated with phylogenetic signal. Analysis of partial correlations between gene properties and phylogenetic signal in which gene evolutionary rate and alignment length were simultaneously controlled, showed similar patterns of correlations, albeit weaker in strength. Examination of the relative importance of each gene property on phylogenetic signal identified gene alignment length, alongside with number of parsimony-informative sites and variable sites, as the most important predictors. Interestingly, the subsets of gene properties that optimally predicted phylogenetic signal differed considerably across our three phylogenetic measures and two data sets; however, gene alignment length and RCV were consistently included as predictors of all three phylogenetic measures in both yeasts and mammals. These results suggest that a handful of sequence-based gene properties are reliable predictors of phylogenetic signal and could be useful in guiding the choice of phylogenetic markers.

Two Simple and Efficient Algorithms to Compute the SP-Score Objective Function of a Multiple Sequence Alignment

BACKGROUND

Multiple sequence alignment (MSA) is a crucial step in many molecular analyses and many MSA tools have been developed. Most of them use a greedy approach to construct a first alignment that is then refined by optimizing the sum of pair score (SP-score). The SP-score estimation is thus a bottleneck for most MSA tools since it is repeatedly required and is time consuming.

RESULTS

Given an alignment of n sequences and L sites, I introduce here optimized solutions reaching O(nL) time complexity for affine gap cost, instead of O(n2L), which are easy to implement.

PhyPA: Phylogenetic method with pairwise sequence alignment outperforms likelihood methods in phylogenetics involving highly diverged sequences

While pairwise sequence alignment (PSA) by dynamic programming is guaranteed to generate one of the optimal alignments, multiple sequence alignment (MSA) of highly divergent sequences often results in poorly aligned sequences, plaguing all subsequent phylogenetic analysis. One way to avoid this problem is to use only PSA to reconstruct phylogenetic trees, which can only be done with distance-based methods. I compared the accuracy of this new computational approach (named PhyPA for phylogenetics by pairwise alignment) against the maximum likelihood method using MSA (the ML+MSA approach), based on nucleotide, amino acid and codon sequences simulated with different topologies and tree lengths. I present a surprising discovery that the fast PhyPA method consistently outperforms the slow ML+MSA approach for highly diverged sequences even when all optimization options were turned on for the ML+MSA approach. Only when sequences are not highly diverged (i.e., when a reliable MSA can be obtained) does the ML+MSA approach outperforms PhyPA. The true topologies are always recovered by ML with the true alignment from the simulation. However, with MSA derived from alignment programs such as MAFFT or MUSCLE, the recovered topology consistently has higher likelihood than that for the true topology. Thus, the failure to recover the true topology by the ML+MSA is not because of insufficient search of tree space, but by the distortion of phylogenetic signal by MSA methods. I have implemented in DAMBE PhyPA and two approaches making use of multi-gene data sets to derive phylogenetic support for subtrees equivalent to resampling techniques such as bootstrapping and jackknifing.

Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map

BACKGROUND

Reconstruction of multiple sequence alignments (MSAs) is a crucial step in most homology-based sequence analyses, which constitute an integral part of computational biology. To improve the accuracy of this crucial step, it is essential to better characterize errors that state-of-the-art aligners typically make. For this purpose, we here introduce two tools: the complete-likelihood score and the position-shift map.

RESULTS

The logarithm of the total probability of a MSA under a stochastic model of sequence evolution along a time axis via substitutions, insertions and deletions (called the "complete-likelihood score" here) can serve as an ideal score of the MSA. A position-shift map, which maps the difference in each residue's position between two MSAs onto one of them, can clearly visualize where and how MSA errors occurred and help disentangle composite errors. To characterize MSA errors using these tools, we constructed three sets of simulated MSAs of selectively neutral mammalian DNA sequences, with small, moderate and large divergences, under a stochastic evolutionary model with an empirically common power-law insertion/deletion length distribution. Then, we reconstructed MSAs using MAFFT and Prank as representative state-of-the-art single-optimum-search aligners. About 40-99% of the hundreds of thousands of gapped segments were involved in alignment errors. In a substantial fraction, from about 1/4 to over 3/4, of erroneously reconstructed segments, reconstructed MSAs by each aligner showed complete-likelihood scores not lower than those of the true MSAs. Out of the remaining errors, a majority by an iterative option of MAFFT showed discrepancies between the aligner-specific score and the complete-likelihood score, and a majority by Prank seemed due to inadequate exploration of the MSA space. Analyses by position-shift maps indicated that true MSAs are in considerable neighborhoods of reconstructed MSAs in about 80-99% of the erroneous segments for small and moderate divergences, but in only a minority for large divergences.

CONCLUSIONS

The results of this study suggest that measures to further improve the accuracy of reconstructed MSAs would substantially differ depending on the types of aligners. They also re-emphasize the importance of obtaining a probability distribution of fairly likely MSAs, instead of just searching for a single optimum MSA.

A simple method to control over-alignment in the MAFFT multiple sequence alignment program

Motivation: We present a new feature of the MAFFT multiple alignment program for suppressing over-alignment (aligning unrelated segments). Conventional MAFFT is highly sensitive in aligning conserved regions in remote homologs, but the risk of over-alignment is recently becoming greater, as low-quality or noisy sequences are increasing in protein sequence databases, due, for example, to sequencing errors and difficulty in gene prediction.

Results: The proposed method utilizes a variable scoring matrix for different pairs of sequences (or groups) in a single multiple sequence alignment, based on the global similarity of each pair. This method significantly increases the correctly gapped sites in real examples and in simulations under various conditions. Regarding sensitivity, the effect of the proposed method is slightly negative in real protein-based benchmarks, and mostly neutral in simulation-based benchmarks. This approach is based on natural biological reasoning and should be compatible with many methods based on dynamic programming for multiple sequence alignment.

Availability and implementation: The new feature is available in MAFFT versions 7.263 and higher. http://mafft.cbrc.jp/alignment/software/

Contact:katoh@ifrec.osaka-u.ac.jp

Supplementary information:Supplementary data are available at Bioinformatics online.

Gene Tree Affects Inference of Sites Under Selection by the Branch-Site Test of Positive Selection

The branch-site test of positive selection is a standard approach to detect past episodic positive selection in a priori-specified branches of a gene phylogeny. Here, we ask if differences in the topology of the gene tree have any influence on the ability to infer positively selected sites. Using simulated sequences, we compare the results obtained for true and rearranged topologies. We find a strong relationship between “conflicting branch length,” which occurs when the set of sequences that experiences selection for a given topology and foreground is changed, and the ability to predict positively selected sites. Moreover, by reanalyzing a previously published data set, we show that the choice of a gene tree also affects the results obtained for real-world sequences. This is the first study to demonstrate that tree topology has a clear effect on the inference of positive selection. We conclude that the choice of a gene tree is an important factor for the branch-site analysis of positive selection.

Multiple sequence alignment modeling: methods and applications

This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.

Inferring Indel Parameters using a Simulation-based Approach

In this study, we present a novel methodology to infer indel parameters from multiple sequence alignments (MSAs) based on simulations. Our algorithm searches for the set of evolutionary parameters describing indel dynamics which best fits a given input MSA. In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data. Using simulations, we demonstrate that our methodology can accurately infer the indel parameters for a large variety of plausible settings. Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses. Finally, we demonstrate how our methodology can be used to simulate MSAs based on indel parameters inferred from real data sets.

Evolutionary insights into host-pathogen interactions from mammalian sequence data

Infections are one of the major selective pressures acting on humans, and host-pathogen interactions contribute to shaping the genetic diversity of both organisms. Evolutionary genomic studies take advantage of experiments that natural selection has been performing over millennia. In particular, inter-species comparative genomic analyses can highlight the genetic determinants of infection susceptibility or severity. Recent examples show how evolution-guided approaches can provide new insights into host-pathogen interactions, ultimately clarifying the basis of host range and explaining the emergence of different diseases. We describe the latest developments in comparative immunology and evolutionary genetics, showing their relevance for understanding the molecular determinants of infection susceptibility in mammals.

Kr/Kc but not dN/dS correlates positively with body mass in birds, raising implications for inferring lineage-specific selection

Background

The ratio of the rates of non-synonymous and synonymous substitution (d_N/d_S) is commonly used to estimate selection in coding sequences. It is often suggested that, all else being equal, d_N/d_S should be lower in populations with large effective size (N_e) due to increased efficacy of purifying selection. As N_e is difficult to measure directly, life history traits such as body mass, which is typically negatively associated with population size, have commonly been used as proxies in empirical tests of this hypothesis. However, evidence of whether the expected positive correlation between body mass and d_N/d_S is consistently observed is conflicting.

Results

Employing whole genome sequence data from 48 avian species, we assess the relationship between rates of molecular evolution and life history in birds. We find a negative correlation between d_N/d_S and body mass, contrary to nearly neutral expectation. This raises the question whether the correlation might be a method artefact. We therefore in turn consider non-stationary base composition, divergence time and saturation as possible explanations, but find no clear patterns. However, in striking contrast to d_N/d_S, the ratio of radical to conservative amino acid substitutions (K_r/K_c) correlates positively with body mass.

Conclusions

Our results in principle accord with the notion that non-synonymous substitutions causing radical amino acid changes are more efficiently removed by selection in large populations, consistent with nearly neutral theory. These findings have implications for the use of d_N/d_S and suggest that caution is warranted when drawing conclusions about lineage-specific modes of protein evolution using this metric.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0542-8) contains supplementary material, which is available to authorized users.

Evidence of Statistical Inconsistency of Phylogenetic Methods in the Presence of Multiple Sequence Alignment Uncertainty

Evolutionary studies usually use a two-step process to investigate sequence data. Step one estimates a multiple sequence alignment (MSA) and step two applies phylogenetic methods to ask evolutionary questions of that MSA. Modern phylogenetic methods infer evolutionary parameters using maximum likelihood or Bayesian inference, mediated by a probabilistic substitution model that describes sequence change over a tree. The statistical properties of these methods mean that more data directly translates to an increased confidence in downstream results, providing the substitution model is adequate and the MSA is correct. Many studies have investigated the robustness of phylogenetic methods in the presence of substitution model misspecification, but few have examined the statistical properties of those methods when the MSA is unknown. This simulation study examines the statistical properties of the complete two-step process when inferring sequence divergence and the phylogenetic tree topology. Both nucleotide and amino acid analyses are negatively affected by the alignment step, both through inaccurate guide tree estimates and through overfitting to that guide tree. For many alignment tools these effects become more pronounced when additional sequences are added to the analysis. Nucleotide sequences are particularly susceptible, with MSA errors leading to statistical support for long-branch attraction artifacts, which are usually associated with gross substitution model misspecification. Amino acid MSAs are more robust, but do tend to arbitrarily resolve multifurcations in favor of the guide tree. No inference strategies produce consistently accurate estimates of divergence between sequences, although amino acid MSAs are again more accurate than their nucleotide counterparts. We conclude with some practical suggestions about how to limit the effect of MSA uncertainty on evolutionary inference.

ITS2 Secondary Structure Improves Discrimination between Medicinal "Mu Tong" Species when Using DNA Barcoding

DNA barcoding is a promising species identification method, but it has proved difficult to find a standardized DNA marker in plant. Although the ITS/ITS2 RNA transcript has been proposed as the core barcode for seed plants, it has been criticized for being too conserved in some species to provide enough information or too variable in some species to align it within the different taxa ranks. We selected 30 individuals, representing 16 species and four families, to explore whether ITS2 can successfully resolve species in terms of secondary structure. Secondary structure was predicted using Mfold software and sequence-structure was aligned by MARNA. RNAstat software transformed the secondary structures into 28 symbol code data for maximum parsimony (MP) analysis. The results showed that the ITS2 structures in our samples had a common four-helix folding type with some shared motifs. This conserved structure facilitated the alignment of ambiguous sequences from divergent families. The structure alignment yielded a MP tree, in which most topological relationships were congruent with the tree constructed using nucleotide sequence data. When the data was combined, we obtained a well-resolved and highly supported phylogeny, in which individuals of a same species were clustered together into a monophyletic group. As a result, the different species that are often referred to as the herb “Mu tong” were successfully identified using short fragments of 250 bp ITS2 sequences, together with their secondary structure. Thus our analysis strengthens the potential of ITS2 as a promising DNA barcode because it incorporates valuable secondary structure information that will help improve discrimination between species.

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

BACKGROUND

A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment.

RESULTS

In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased.

CONCLUSIONS

The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign .

Indel reliability in indel-based phylogenetic inference

It is often assumed that it is unlikely that the same insertion or deletion (indel) event occurred at the same position in two independent evolutionary lineages, and thus, indel-based inference of phylogeny should be less subject to homoplasy compared with standard inference which is based on substitution events. Indeed, indels were successfully used to solve debated evolutionary relationships among various taxonomical groups. However, indels are never directly observed but rather inferred from the alignment and thus indel-based inference may be sensitive to alignment errors. It is hypothesized that phylogenetic reconstruction would be more accurate if it relied only on a subset of reliable indels instead of the entire indel data. Here, we developed a method to quantify the reliability of indel characters by measuring how often they appear in a set of alternative multiple sequence alignments. Our approach is based on the assumption that indels that are consistently present in most alternative alignments are more reliable compared with indels that appear only in a small subset of these alignments. Using simulated and empirical data, we studied the impact of filtering and weighting indels by their reliability scores on the accuracy of indel-based phylogenetic reconstruction. The new method is available as a web-server at http://guidance.tau.ac.il/RELINDEL/.

The dawn of open access to phylogenetic data

The scientific enterprise depends critically on the preservation of and open access to published data. This basic tenet applies acutely to phylogenies (estimates of evolutionary relationships among species). Increasingly, phylogenies are estimated from increasingly large, genome-scale datasets using increasingly complex statistical methods that require increasing levels of expertise and computational investment. Moreover, the resulting phylogenetic data provide an explicit historical perspective that critically informs research in a vast and growing number of scientific disciplines. One such use is the study of changes in rates of lineage diversification (speciation--extinction) through time. As part of a meta-analysis in this area, we sought to collect phylogenetic data (comprising nucleotide sequence alignment and tree files) from 217 studies published in 46 journals over a 13-year period. We document our attempts to procure those data (from online archives and by direct request to corresponding authors), and report results of analyses (using Bayesian logistic regression) to assess the impact of various factors on the success of our efforts. Overall, complete phylogenetic data for [Formula: see text] of these studies are effectively lost to science. Our study indicates that phylogenetic data are more likely to be deposited in online archives and/or shared upon request when: (1) the publishing journal has a strong data-sharing policy; (2) the publishing journal has a higher impact factor, and; (3) the data are requested from faculty rather than students. Importantly, our survey spans recent policy initiatives and infrastructural changes; our analyses indicate that the positive impact of these community initiatives has been both dramatic and immediate. Although the results of our study indicate that the situation is dire, our findings also reveal tremendous recent progress in the sharing and preservation of phylogenetic data.

Simultaneous Bayesian estimation of alignment and phylogeny under a joint model of protein sequence and structure

For sequences that are highly divergent, there is often insufficient information to infer accurate alignments, and phylogenetic uncertainty may be high. One way to address this issue is to make use of protein structural information, since structures generally diverge more slowly than sequences. In this work, we extend a recently developed stochastic model of pairwise structural evolution to multiple structures on a tree, analytically integrating over ancestral structures to permit efficient likelihood computations under the resulting joint sequence-structure model. We observe that the inclusion of structural information significantly reduces alignment and topology uncertainty, and reduces the number of topology and alignment errors in cases where the true trees and alignments are known. In some cases, the inclusion of structure results in changes to the consensus topology, indicating that structure may contain additional information beyond that which can be obtained from sequences. We use the model to investigate the order of divergence of cytoglobins, myoglobins, and hemoglobins and observe a stabilization of phylogenetic inference: although a sequence-based inference assigns significant posterior probability to several different topologies, the structural model strongly favors one of these over the others and is more robust to the choice of data set.

Erasing errors due to alignment ambiguity when estimating positive selection

Current estimates of diversifying positive selection rely on first having an accurate multiple sequence alignment. Simulation studies have shown that under biologically plausible conditions, relying on a single estimate of the alignment from commonly used alignment software can lead to unacceptably high false-positive rates in detecting diversifying positive selection. We present a novel statistical method that eliminates excess false positives resulting from alignment error by jointly estimating the degree of positive selection and the alignment under an evolutionary model. Our model treats both substitutions and insertions/deletions as sequence changes on a tree and allows site heterogeneity in the substitution process. We conduct inference starting from unaligned sequence data by integrating over all alignments. This approach naturally accounts for ambiguous alignments without requiring ambiguously aligned sites to be identified and removed prior to analysis. We take a Bayesian approach and conduct inference using Markov chain Monte Carlo to integrate over all alignments on a fixed evolutionary tree topology. We introduce a Bayesian version of the branch-site test and assess the evidence for positive selection using Bayes factors. We compare two models of differing dimensionality using a simple alternative to reversible-jump methods. We also describe a more accurate method of estimating the Bayes factor using Rao-Blackwellization. We then show using simulated data that jointly estimating the alignment and the presence of positive selection solves the problem with excessive false positives from erroneous alignments and has nearly the same power to detect positive selection as when the true alignment is known. We also show that samples taken from the posterior alignment distribution using the software BAli-Phy have substantially lower alignment error compared with MUSCLE, MAFFT, PRANK, and FSA alignments.

Genome alignment with graph data structures: a comparison

Background

Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference.

Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment.

Results

We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures.

Conclusion

We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools.

TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction

Multiple sequence alignment (MSA) is a key modeling procedure when analyzing biological sequences. Homology and evolutionary modeling are the most common applications of MSAs. Both are known to be sensitive to the underlying MSA accuracy. In this work, we show how this problem can be partly overcome using the transitive consistency score (TCS), an extended version of the T-Coffee scoring scheme. Using this local evaluation function, we show that one can identify the most reliable portions of an MSA, as judged from BAliBASE and PREFAB structure-based reference alignments. We also show how this measure can be used to improve phylogenetic tree reconstruction using both an established simulated data set and a novel empirical yeast data set. For this purpose, we describe a novel lossless alternative to site filtering that involves overweighting the trustworthy columns. Our approach relies on the T-Coffee framework; it uses libraries of pairwise alignments to evaluate any third party MSA. Pairwise projections can be produced using fast or slow methods, thus allowing a trade-off between speed and accuracy. We compared TCS with Heads-or-Tails, GUIDANCE, Gblocks, and trimAl and found it to lead to significantly better estimates of structural accuracy and more accurate phylogenetic trees. The software is available from www.tcoffee.org/Projects/tcs.

MAFFT multiple sequence alignment software version 7: improvements in performance and usability

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.