COATi: statistical pairwise alignment of protein-coding sequences.

Sequence alignment is an essential method in bioinformatics and the basis of many analyses, including phylogenetic inference, ancestral sequence reconstruction, and gene annotation. Sequencing artifacts and errors made during genome assembly, such as abiological frameshifts and incorrect early stop codons, can impact downstream analyses leading to erroneous conclusions in comparative and functional genomic studies. More significantly, while indels can occur both within and between codons in natural sequences, most amino-acid- and codon-based aligners assume that indels only occur between codons. This mismatch between biology and alignment algorithms produces suboptimal alignments and errors in downstream analyses. To address these issues, we present COATi, a statistical, codon-aware pairwise aligner that supports complex insertion-deletion models and can handle artifacts present in genomic data. COATi allows users to reduce the amount of discarded data while generating more accurate sequence alignments. COATi can infer indels both within and between codons, leading to improved sequence alignments. We applied COATi to a dataset containing orthologous protein-coding sequences from humans and gorillas and conclude that 41% of indels occurred between codons, agreeing with previous work in other species. We also applied COATi to semi-empirical benchmark alignments and find that it outperforms several popular alignment programs on several measures of alignment quality and accuracy.

A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions.

Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate.

The first steps toward a global pandemic: Reconstructing the demographic history of parasite host switches in its native range.

Host switching allows parasites to expand their niches. However, successful switching may require suites of adaptations and also may decrease performance on the old host. As a result, reductions in gene flow accompany many host switches, driving speciation. Because host switches tend to be rapid, it is difficult to study them in real-time, and their demographic parameters remain poorly understood. As a result, fundamental factors that control subsequent parasite evolution, such as the size of the switching population or the extent of immigration from the original host, remain largely unknown. To shed light on the host switching process, we explored how host switches occur in independent host shifts by two ectoparasitic honey bee mites (Varroa destructor and V. jacobsoni). Both switched to the western honey bee (Apis mellifera) after being brought into contact with their ancestral host (Apis cerana), ~70 and ~12 years ago, respectively. Varroa destructor subsequently caused worldwide collapses of honey bee populations. Using whole-genome sequencing on 63 mites collected in their native ranges from both the ancestral and novel hosts, we were able to reconstruct the known temporal dynamics of the switch. We further found multiple previously undiscovered mitochondrial lineages on the novel host, along with the genetic equivalent of tens of individuals that were involved in the initial host switch. Despite being greatly reduced, some gene flow remains between mites adapted to different hosts. Our findings suggest that while reproductive isolation may facilitate the fixation of traits beneficial for exploiting the new host, ongoing genetic exchange may allow genetic amelioration of inbreeding effects.

Mitogenomic phylogeny of Callithrix with special focus on human transferred taxa.

BACKGROUND: Callithrix marmosets are a relatively young primate radiation, whose phylogeny is not yet fully resolved. These primates are naturally para- and allopatric, but three species with highly invasive potential have been introduced into the southeastern Brazilian Atlantic Forest by the pet trade. There, these species hybridize with each other and endangered, native congeners. We aimed here to reconstruct a robust Callithrix phylogeny and divergence time estimates, and identify the biogeographic origins of autochthonous and allochthonous Callithrix mitogenome lineages. We sequenced 49 mitogenomes from four species (C. aurita, C. geoffroyi, C. jacchus, C. penicillata) and anthropogenic hybrids (C. aurita x Callithrix sp., C. penicillata x C. jacchus, Callithrix sp. x Callithrix sp., C. penicillata x C. geoffroyi) via Sanger and whole genome sequencing. We combined these data with previously published Callithrix mitogenomes to analyze five Callithrix species in total. RESULTS: We report the complete sequence and organization of the C. aurita mitogenome. Phylogenetic analyses showed that C. aurita was the first to diverge within Callithrix 3.54 million years ago (Ma), while C. jacchus and C. penicillata lineages diverged most recently 0.5 Ma as sister clades. MtDNA clades of C. aurita, C. geoffroyi, and C. penicillata show intraspecific geographic structure, but C. penicillata clades appear polyphyletic. Hybrids, which were identified by phenotype, possessed mainly C. penicillata or C. jacchus mtDNA haplotypes. The biogeographic origins of mtDNA haplotypes from hybrid and allochthonous Callithrix were broadly distributed across natural Callithrix ranges. Our phylogenetic results also evidence introgression of C. jacchus mtDNA into C. aurita. CONCLUSION: Our robust Callithrix mitogenome phylogeny shows C. aurita lineages as basal and C. jacchus lineages among the most recent within Callithrix. We provide the first evidence that parental mtDNA lineages of anthropogenic hybrid and allochthonous marmosets are broadly distributed inside and outside of the Atlantic Forest. We also show evidence of cryptic hybridization between allochthonous Callithrix and autochthonous C. aurita. Our results encouragingly show that further development of genomic resources will allow to more clearly elucidate Callithrix evolutionary relationships and understand the dynamics of Callithrix anthropogenic introductions into the Brazilian Atlantic Forest.

A phylogenomic approach reveals a low somatic mutation rate in a long-lived plant.

Somatic mutations can have important effects on the life history, ecology, and evolution of plants, but the rate at which they accumulate is poorly understood and difficult to measure directly. Here, we develop a method to measure somatic mutations in individual plants and use it to estimate the somatic mutation rate in a large, long-lived, phenotypically mosaic Eucalyptus melliodora tree. Despite being 100 times larger than Arabidopsis, this tree has a per-generation mutation rate only ten times greater, which suggests that this species may have evolved mechanisms to reduce the mutation rate per unit of growth. This adds to a growing body of evidence that illuminates the correlated evolutionary shifts in mutation rate and life history in plants.

Experimental evolution reveals an effective avenue to release catabolite repression via mutations in XylR.

Microbial production of fuels and chemicals from lignocellulosic biomass provides promising biorenewable alternatives to the conventional petroleum-based products. However, heterogeneous sugar composition of lignocellulosic biomass hinders efficient microbial conversion due to carbon catabolite repression. The most abundant sugar monomers in lignocellulosic biomass materials are glucose and xylose. Although industrial Escherichia coli strains efficiently use glucose, their ability to use xylose is often repressed in the presence of glucose. Here we independently evolved three E. coli strains from the same ancestor to achieve high efficiency for xylose fermentation. Each evolved strain has a point mutation in a transcriptional activator for xylose catabolic operons, either CRP or XylR, and these mutations are demonstrated to enhance xylose fermentation by allelic replacements. Identified XylR variants (R121C and P363S) have a higher affinity to their DNA binding sites, leading to a xylose catabolic activation independent of catabolite repression control. Upon introducing these amino acid substitutions into the E. coli D-lactate producer TG114, 94% of a glucose-xylose mixture (50 g⋅L-1 each) was used in mineral salt media that led to a 50% increase in product titer after 96 h of fermentation. The two amino acid substitutions in XylR enhance xylose utilization and release glucose-induced repression in different E. coli hosts, including wild type, suggesting its potential wide application in industrial E. coli biocatalysts.

accuMUlate: a mutation caller designed for mutation accumulation experiments.

Summary: Mutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Here we describe accuMUlate, a probabilistic mutation caller that reflects the design of a typical MA experiment while being flexible enough to accommodate properties unique to any particular experiment. Availability and implementation accuMUlate is available from https://github.com/dwinter/accuMUlate. Supplementary information: Supplementary data are available at Bioinformatics online.

SpartaABC: a web server to simulate sequences with indel parameters inferred using an approximate Bayesian computation algorithm.

Many analyses for the detection of biological phenomena rely on a multiple sequence alignment as input. The results of such analyses are often further studied through parametric bootstrap procedures, using sequence simulators. One of the problems with conducting such simulation studies is that users currently have no means to decide which insertion and deletion (indel) parameters to choose, so that the resulting sequences mimic biological data. Here, we present SpartaABC, a web server that aims to solve this issue. SpartaABC implements an approximate-Bayesian-computation rejection algorithm to infer indel parameters from sequence data. It does so by extracting summary statistics from the input. It then performs numerous sequence simulations under randomly sampled indel parameters. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC retains only parameters behind simulations close to the real data. As output, SpartaABC provides point estimates and approximate posterior distributions of the indel parameters. In addition, SpartaABC allows simulating sequences with the inferred indel parameters. To this end, the sequence simulators, Dawg 2.0 and INDELible were integrated. Using SpartaABC we demonstrate the differences in indel dynamics among three protein-coding genes across mammalian orthologs. SpartaABC is freely available for use at http://spartaabc.tau.ac.il/webserver.

Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions.

MOTIVATION: Accurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others. RESULTS: We modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. AVAILABILITY AND IMPLEMENTATION: Methods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). CONTACT: cartwright@asu.edu. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

The role of gene flow in rapid and repeated evolution of cave-related traits in Mexican tetra, Astyanax mexicanus.

Understanding the molecular basis of repeatedly evolved phenotypes can yield key insights into the evolutionary process. Quantifying gene flow between populations is especially important in interpreting mechanisms of repeated phenotypic evolution, and genomic analyses have revealed that admixture occurs more frequently between diverging lineages than previously thought. In this study, we resequenced 47 whole genomes of the Mexican tetra from three cave populations, two surface populations and outgroup samples. We confirmed that cave populations are polyphyletic and two Astyanax mexicanus lineages are present in our data set. The two lineages likely diverged much more recently than previous mitochondrial estimates of 5-7 mya. Divergence of cave populations from their phylogenetically closest surface population likely occurred between ~161 and 191 k generations ago. The favoured demographic model for most population pairs accounts for divergence with secondary contact and heterogeneous gene flow across the genome, and we rigorously identified gene flow among all lineages sampled. Therefore, the evolution of cave-related traits occurred more rapidly than previously thought, and trogolomorphic traits are maintained despite gene flow with surface populations. The recency of these estimated divergence events suggests that selection may drive the evolution of cave-derived traits, as opposed to disuse and drift. Finally, we show that a key trogolomorphic phenotype QTL is enriched for genomic regions with low divergence between caves, suggesting that regions important for cave phenotypes may be transferred between caves via gene flow. Our study shows that gene flow must be considered in studies of independent, repeated trait evolution.

The importance of selection in the evolution of blindness in cavefish.

BACKGROUND: Blindness has evolved repeatedly in cave-dwelling organisms, and many hypotheses have been proposed to explain this observation, including both accumulation of neutral loss-of-function mutations and adaptation to darkness. Investigating the loss of sight in cave dwellers presents an opportunity to understand the operation of fundamental evolutionary processes, including drift, selection, mutation, and migration. RESULTS: Here we model the evolution of blindness in caves. This model captures the interaction of three forces: (1) selection favoring alleles causing blindness, (2) immigration of sightedness alleles from a surface population, and (3) mutations creating blindness alleles. We investigated the dynamics of this model and determined selection-strength thresholds that result in blindness evolving in caves despite immigration of sightedness alleles from the surface. We estimate that the selection coefficient for blindness would need to be at least 0.005 (and maybe as high as 0.5) for blindness to evolve in the model cave-organism, Astyanax mexicanus. CONCLUSIONS: Our results indicate that strong selection is required for the evolution of blindness in cave-dwelling organisms, which is consistent with recent work suggesting a high metabolic cost of eye development.

DeNovoGear: de novo indel and point mutation discovery and phasing.

We present DeNovoGear software for analyzing de novo mutations from familial and somatic tissue sequencing data. DeNovoGear uses likelihood-based error modeling to reduce the false positive rate of mutation discovery in exome analysis and fragment information to identify the parental origin of germ-line mutations. We used DeNovoGear on human whole-genome sequencing data to produce a set of predicted de novo insertion and/or deletion (indel) mutations with a 95% validation rate.

A composite genome approach to identify phylogenetically informative data from next-generation sequencing.

BACKGROUND: Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation. RESULTS: For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets. CONCLUSIONS: SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases.

A family-based probabilistic method for capturing de novo mutations from high-throughput short-read sequencing data.

Recent advances in high-throughput DNA sequencing technologies and associated statistical analyses have enabled in-depth analysis of whole-genome sequences. As this technology is applied to a growing number of individual human genomes, entire families are now being sequenced. Information contained within the pedigree of a sequenced family can be leveraged when inferring the donors' genotypes. The presence of a de novo mutation within the pedigree is indicated by a violation of Mendelian inheritance laws. Here, we present a method for probabilistically inferring genotypes across a pedigree using high-throughput sequencing data and producing the posterior probability of de novo mutation at each genomic site examined. This framework can be used to disentangle the effects of germline and somatic mutational processes and to simultaneously estimate the effect of sequencing error and the initial genetic variation in the population from which the founders of the pedigree arise. This approach is examined in detail through simulations and areas for method improvement are noted. By applying this method to data from members of a well-defined nuclear family with accurate pedigree information, the stage is set to make the most direct estimates of the human mutation rate to date.

Bifidobacteria define gut microbiome profiles of golden lion tamarin (Leontopithecus rosalia) and marmoset (Callithrix sp.) metagenomic shotgun pools.

Gut microbiome disruptions may lead to adverse effects on wildlife fitness and viability, thus maintaining host microbiota biodiversity needs to become an integral part of wildlife conservation. The highly-endangered callitrichid golden lion tamarin (GLT-Leontopithecus rosalia) is a rare conservation success, but allochthonous callitrichid marmosets (Callithrix) serve as principle ecological GLT threats. However, incorporation of microbiome approaches to GLT conservation is impeded by limited gut microbiome studies of Brazilian primates. Here, we carried out analysis of gut metagenomic pools from 114 individuals of wild and captive GLTs and marmosets. More specifically, we analyzed the bacterial component of ultra filtered samples originally collected as part of a virome profiling study. The major findings of this study are consistent with previous studies in showing that Bifidobacterium, a bacterial species important for the metabolism of tree gums consumed by callitrichids, is an important component of the callitrichid gut microbiome - although GTLs and marmosets were enriched for different species of Bifidobacterium. Additionally, the composition of GLT and marmoset gut microbiota is sensitive to host environmental factors. Overall, our data expand baseline gut microbiome data for callitrichids to allow for the development of new tools to improve their management and conservation.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Neutral evolution of robustness in Drosophila microRNA precursors.

Mutational robustness describes the extent to which a phenotype remains unchanged in the face of mutations. Theory predicts that the strength of direct selection for mutational robustness is at most the magnitude of the rate of deleterious mutation. As far as nucleic acid sequences are concerned, only long sequences in organisms with high deleterious mutation rates and large population sizes are expected to evolve mutational robustness. Surprisingly, recent studies have concluded that molecules that meet none of these conditions--the microRNA precursors (pre-miRNAs) of multicellular eukaryotes--show signs of selection for mutational and/or environmental robustness. To resolve the apparent disagreement between theory and these studies, we have reconstructed the evolutionary history of Drosophila pre-miRNAs and compared the robustness of each sequence to that of its reconstructed ancestor. In addition, we "replayed the tape" of pre-miRNA evolution via simulation under different evolutionary assumptions and compared these alternative histories with the actual one. We found that Drosophila pre-miRNAs have evolved under strong purifying selection against changes in secondary structure. Contrary to earlier claims, there is no evidence that these RNAs have been shaped by either direct or congruent selection for any kind of robustness. Instead, the high robustness of Drosophila pre-miRNAs appears to be mostly intrinsic and likely a consequence of selection for functional structures.

History can matter: non-Markovian behavior of ancestral lineages.

Although most of the important evolutionary events in the history of biology can only be studied via interspecific comparisons, it is challenging to apply the rich body of population genetic theory to the study of interspecific genetic variation. Probabilistic modeling of the substitution process would ideally be derived from first principles of population genetics, allowing a quantitative connection to be made between the parameters describing mutation, selection, drift, and the patterns of interspecific variation. There has been progress in reconciling population genetics and interspecific evolution for the case where mutation rates are sufficiently low, but when mutation rates are higher, reconciliation has been hampered due to complications from how the loss or fixation of new mutations can be influenced by linked nonneutral polymorphisms (i.e., the Hill-Robertson effect). To investigate the generation of interspecific genetic variation when concurrent fitness-affecting polymorphisms are common and the Hill-Robertson effect is thereby potentially strong, we used the Wright-Fisher model of population genetics to simulate very many generations of mutation, natural selection, and genetic drift. This was done so that the chronological history of advantageous, deleterious, and neutral substitutions could be traced over time along the ancestral lineage. Our simulations show that the process by which a nonrecombining sequence changes over time can markedly deviate from the Markov assumption that is ubiquitous in molecular phylogenetics. In particular, we find tendencies for advantageous substitutions to be followed by deleterious ones and for deleterious substitutions to be followed by advantageous ones. Such non-Markovian patterns reflect the fact that the fate of the ancestral lineage depends not only on its current allelic state but also on gene copies not belonging to the ancestral lineage. Although our simulations describe nonrecombining sequences, we conclude by discussing how non-Markovian behavior of the ancestral lineage is plausible even when recombination rates are not low. As a result, we believe that increased attention needs to be devoted to the robustness of evolutionary inference procedures that rely upon the Markov assumption.

Federated learning and Indigenous genomic data sovereignty.

Indigenous peoples are under-represented in genomic datasets, which can lead to limited accuracy and utility of machine learning models in precision health. While open data sharing undermines rights of Indigenous communities to govern data decisions, federated learning may facilitate secure and community-consented data sharing.

Genie: an interactive real-time simulation for teaching genetic drift.

Neutral evolution is a fundamental concept in evolutionary biology but teaching this and other non-adaptive concepts is especially challenging. Here we present Genie, a browser-based educational tool that demonstrates population-genetic concepts such as genetic drift, population isolation, gene flow, and genetic mutation. Because it does not need to be downloaded and installed, Genie can scale to large groups of students and is useful for both in-person and online instruction. Genie was used to teach genetic drift to Evolution students at Arizona State University during Spring 2016 and Spring 2017. The effectiveness of Genie to teach key genetic drift concepts and misconceptions was assessed with the Genetic Drift Inventory developed by Price et al. (CBE Life Sci Educ 13(1):65-75, 2014). Overall, Genie performed comparably to that of traditional static methods across all evaluated classes. We have empirically demonstrated that Genie can be successfully integrated with traditional instruction to reduce misconceptions about genetic drift.