Performance of Akaike Information Criterion and Bayesian Information Criterion in Selecting Partition Models and Mixture Models.

In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests that AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC at comparing amongst mixture models and amongst partition models. We find that under nonstandard conditions (i.e. when some edges have small expected number of changes), AIC underestimates the expected Kullback-Leibler divergence. Under such conditions, AIC preferred the complex mixture models and BIC preferred the simpler mixture models. The mixture models selected by AIC had a better performance in estimating the edge length, while the simpler models selected by BIC performed better in estimating the base frequencies and substitution rate parameters. In contrast, AIC and BIC both prefer simpler partition models over more complex partition models under nonstandard conditions, despite the fact that the more complex partition model was the generating model. We also investigated how mispartitioning (i.e., grouping sites that have not evolved under the same process) affects both the performance of partition models compared with mixture models and the model selection process. We found that as the level of mispartitioning increases, the bias of AIC in estimating the expected Kullback-Leibler divergence remains the same, and the branch lengths and evolutionary parameters estimated by partition models become less accurate. We recommend that researchers are cautious when using AIC and BIC to select among partition and mixture models; other alternatives, such as cross-validation and bootstrapping, should be explored, but may suffer similar limitations [AIC; BIC; mispartitioning; partitioning; partition model; mixture model].

Benchmarking Methods of Protein Structure Alignment.

The function of a protein is primarily determined by its structure and amino acid sequence. Many biological questions of interest rely on being able to accurately determine the group of structures to which domains of a protein belong; this can be done through alignment and comparison of protein structures. Dozens of different methods for Protein Structure Alignment (PSA) have been proposed that use a wide range of techniques. The aim of this study is to determine the ability of PSA methods to identify pairs of protein domains known to share differing levels of structural similarity, and to assess their utility for clustering domains from several different folds into known groups. We present the results of a comprehensive investigation into eighteen PSA methods, to our knowledge the largest piece of independent research on this topic. Overall, SP-AlignNS (non-sequential) was found to be the best method for classification, and among the best performing methods for clustering. Methods (where possible) were split into the algorithm used to find the optimal alignment and the score used to assess similarity. This allowed us to largely separate the algorithm from the score it maximizes and thus, to assess their effectiveness independently of each other. Surprisingly, we found that some hybrids of mismatched scores and algorithms performed better than either of the native methods at classification and, in some cases, clustering as well. It is hoped that this investigation and the accompanying discussion will be useful for researchers selecting or designing methods to align protein structures.

Australian spiny mountain crayfish and their temnocephalan ectosymbionts: an ancient association on the edge of coextinction?

Australian spiny mountain crayfish (Euastacus, Parastacidae) and their ecotosymbiotic temnocephalan flatworms (Temnocephalida, Platyhelminthes) may have co-occurred and interacted through deep time, during a period of major environmental change. Therefore, reconstructing the history of their association is of evolutionary, ecological, and conservation significance. Here, time-calibrated Bayesian phylogenies of Euastacus species and their temnocephalans (Temnohaswellia and Temnosewellia) indicate near-synchronous diversifications from the Cretaceous. Statistically significant cophylogeny correlations between associated clades suggest linked evolutionary histories. However, there is a stronger signal of codivergence and greater host specificity in Temnosewellia, which co-occurs with Euastacus across its range. Phylogeography and analyses of evolutionary distinctiveness (ED) suggest that regional differences in the impact of climate warming and drying had major effects both on crayfish and associated temnocephalans. In particular, Euastacus and Temnosewellia show strong latitudinal gradients in ED and, conversely, in geographical range size, with the most distinctive, northern lineages facing the greatest risk of extinction. Therefore, environmental change has, in some cases, strengthened ecological and evolutionary associations, leaving host-specific temnocephalans vulnerable to coextinction with endangered hosts. Consequently, the extinction of all Euastacus species currently endangered (75%) predicts coextinction of approximately 60% of the studied temnocephalans, with greatest loss of the most evolutionarily distinctive lineages.

Simple, rapid and accurate genotyping-by-sequencing from aligned whole genomes with ArrayMaker.

SUMMARY: Whole-genome sequencing has revolutionized the study of genetics. Genotyping-by-sequencing is now a viable method of genotyping, yet the bioinformatics involved can be daunting if not prohibitive for some laboratories. Here we present ArrayMaker, a user-friendly tool that extracts accurate single nucleotide polymorphism genotypes at pre-defined loci from whole-genome alignments and presents them in a standard genotyping format compatible with association analysis software and datasets genotyped on commercial array platforms. Using this tool, geneticists with only basic computing ability can genotype samples at any desired list of markers, facilitating genome-wide association analysis, fine mapping, candidate variant assessment, data sharing and compatibility of data sourced from multiple technologies. AVAILABILITY AND IMPLEMENTATION: ArrayMaker is licensed under The MIT License and can be freely obtained at https://github.com/cw2014/ArrayMaker/. The program is implemented in Perl and runs on Linux operating systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: cali.willet@sydney.edu.au.

Evolving nutritional strategies in the presence of competition: a geometric agent-based model.

Access to nutrients is a key factor governing development, reproduction and ultimately fitness. Within social groups, contest-competition can fundamentally affect nutrient access, potentially leading to reproductive asymmetry among individuals. Previously, agent-based models have been combined with the Geometric Framework of nutrition to provide insight into how nutrition and social interactions affect one another. Here, we expand this modelling approach by incorporating evolutionary algorithms to explore how contest-competition over nutrient acquisition might affect the evolution of animal nutritional strategies. Specifically, we model tolerance of nutrient excesses and deficits when ingesting nutritionally imbalanced foods, which we term 'nutritional latitude'; a higher degree of nutritional latitude constitutes a higher tolerance of nutritional excess and deficit. Our results indicate that a transition between two alternative strategies occurs at moderate to high levels of competition. When competition is low, individuals display a low level of nutritional latitude and regularly switch foods in search of an optimum. When food is scarce and contest-competition is intense, high nutritional latitude appears optimal, and individuals continue to consume an imbalanced food for longer periods before attempting to switch to an alternative. However, the relative balance of nutrients within available foods also strongly influences at what levels of competition, if any, transitions between these two strategies occur. Our models imply that competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth. We discuss how the integration of agent-based, nutritional and evolutionary modelling may be applied in future studies to further understand the evolution of nutritional strategies across social and ecological contexts.

Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions.

Over recent years, modelling approaches from nutritional ecology (known as Nutritional Geometry) have been increasingly used to describe how animals and some other organisms select foods and eat them in appropriate amounts in order to maintain a balanced nutritional state maximising fitness. These nutritional strategies profoundly affect the physiology, behaviour and performance of individuals, which in turn impact their social interactions within groups and societies. Here, we present a conceptual framework to study the role of nutrition as a major ecological factor influencing the development and maintenance of social life. We first illustrate some of the mechanisms by which nutritional differences among individuals mediate social interactions in a broad range of species and ecological contexts. We then explain how studying individual- and collective-level nutrition in a common conceptual framework derived from Nutritional Geometry can bring new fundamental insights into the mechanisms and evolution of social interactions, using a combination of simulation models and manipulative experiments.

Introducing TreeCollapse: a novel greedy algorithm to solve the cophylogeny reconstruction problem.

BACKGROUND: Cophylogeny mapping is used to uncover deep coevolutionary associations between two or more phylogenetic histories at a macro coevolutionary scale. As cophylogeny mapping is NP-Hard, this technique relies heavily on heuristics to solve all but the most trivial cases. One notable approach utilises a metaheuristic to search only a subset of the exponential number of fixed node orderings possible for the phylogenetic histories in question. This is of particular interest as it is the only known heuristic that guarantees biologically feasible solutions. This has enabled research to focus on larger coevolutionary systems, such as coevolutionary associations between figs and their pollinator wasps, including over 200 taxa. Although able to converge on solutions for problem instances of this size, a reduction from the current cubic running time is required to handle larger systems, such as Wolbachia and their insect hosts. RESULTS: Rather than solving this underlying problem optimally this work presents a greedy algorithm called TreeCollapse, which uses common topological patterns to recover an approximation of the coevolutionary history where the internal node ordering is fixed. This approach offers a significant speed-up compared to previous methods, running in linear time. This algorithm has been applied to over 100 well-known coevolutionary systems converging on Pareto optimal solutions in over 68% of test cases, even where in some cases the Pareto optimal solution has not previously been recoverable. Further, while TreeCollapse applies a local search technique, it can guarantee solutions are biologically feasible, making this the fastest method that can provide such a guarantee. CONCLUSION: As a result, we argue that the newly proposed algorithm is a valuable addition to the field of coevolutionary research. Not only does it offer a significantly faster method to estimate the cost of cophylogeny mappings but by using this approach, in conjunction with existing heuristics, it can assist in recovering a larger subset of the Pareto front than has previously been possible.

Ancient and Modern Genomes Reveal Microsatellites Maintain a Dynamic Equilibrium Through Deep Time.

Microsatellites are widely used in population genetics, but their evolutionary dynamics remain poorly understood. It is unclear whether microsatellite loci drift in length over time. This is important because the mutation processes that underlie these important genetic markers are central to the evolutionary models that employ microsatellites. We identify more than 27 million microsatellites using a novel and unique dataset of modern and ancient Adélie penguin genomes along with data from 63 published chordate genomes. We investigate microsatellite evolutionary dynamics over 2 timescales: one based on Adélie penguin samples dating to ∼46.5 ka and the other dating to the diversification of chordates aged more than 500 Ma. We show that the process of microsatellite allele length evolution is at dynamic equilibrium; while there is length polymorphism among individuals, the length distribution for a given locus remains stable. Many microsatellites persist over very long timescales, particularly in exons and regulatory sequences. These often retain length variability, suggesting that they may play a role in maintaining phenotypic variation within populations.

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies.

BACKGROUND: The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the "correlation" of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. RESULTS: Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method's performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. CONCLUSIONS: The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network.

A review of visualisations of protein fold networks and their relationship with sequence and function.

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Methods of analyzing patterns of multimorbidity using network analysis: a scoping review protocol.

OBJECTIVE: The purpose of this review is to summarize the techniques used for network analysis of multimorbidity to inform development of a standard methodology. INTRODUCTION: There is a growing trend of using network analysis to investigate relationships between chronic illnesses in people with multimorbidities. However, there is currently no recommended approach to calculating and displaying networks of chronic health conditions. This review intends to summarize the current literature to further the development of a standard methodology. INCLUSION CRITERIA: Studies will be included if they investigated the relationships between multiple chronic health conditions without referring to an index condition, using network analysis techniques. Studies using both survey and administrative data will be included. Studies including biological or genomic data sets will not be included as they are out of scope. METHODS: Databases searched will include MEDLINE, ScienceDirect, Scopus, and PsycINFO. All relevant publications will be included provided they were published before October 2020. Publications from all languages will be included where an appropriate translation in English can be obtained. Data extracted will include country of origin, type of data used, measure of association, software used, and notes on any specific points of methodological interest relevant to the review question.

Hantavirus evolution in relation to its rodent and insectivore hosts: no evidence for codivergence.

Hantaviruses are considered one of the best examples of a long-term association between RNA viruses and their hosts. Based on the appearance of strong host specificity, it has been suggested that hantaviruses cospeciated with the rodents and insectivores they infect since these mammals last shared a common ancestor, approximately 100 million years ago. We tested this hypothesis of host-virus codivergence in two ways: 1) we used cophylogenetic reconciliation analysis to assess the fit of the virus tree onto that of the host and 2) we estimated the evolutionary rates and divergence times for the Hantavirus genus using a Bayesian Markov Chain Monte Carlo method and similarly compared these with those of their hosts. Our reconciliation analysis provided no evidence for a history of codivergence between hantaviruses and their hosts. Further, the divergence times for the Hantavirus genus were many orders of magnitude too recent to correspond with the timescale of their hosts' speciation. We therefore propose that apparent similarities between the phylogenies of hantaviruses and their mammalian hosts are the result of a more recent history of preferential host switching and local adaptation. Based on the presence of clade-defining amino acids in all genomic segments, we propose that the patterns of amino acid replacement in these viruses are also compatible with a history of host-specific adaptation.

Insights into the evolutionary history of an emerging livestock pathogen: porcine circovirus 2.

Porcine circovirus 2 (PCV2) is the primary etiological agent of postweaning multisystemic wasting syndrome (PMWS), one of the most economically important emerging swine diseases worldwide. Virulent PCV2 was first identified following nearly simultaneous outbreaks of PMWS in North America and Europe in the 1990s and has since achieved global distribution. However, the processes responsible for the emergence and spread of PCV2 remain poorly understood. Here, phylogenetic and cophylogenetic inferences were utilized to address key questions on the time scale, processes, and geographic diffusion of emerging PCV2. The results of these analyses suggest that the two genotypes of PCV2 (PCV2a and PCV2b) are likely to have emerged from a common ancestor approximately 100 years ago and have been on independent evolutionary trajectories since that time, despite cocirculating in the same host species and geographic regions. The patterns of geographic movement of PCV2 that we recovered appear to mimic those of the global pig trade and suggest that the movement of asymptomatic animals is likely to have facilitated the rapid spread of virulent PCV2 around the globe. We further estimated the rate of nucleotide substitution for PCV2 to be on the order of 1.2 x 10(-3) substitutions/site/year, the highest yet recorded for a single-stranded DNA virus. This high rate of evolution may allow PCV2 to maintain evolutionary dynamics closer to those of single-stranded RNA viruses than to those of double-stranded DNA viruses, further facilitating the rapid emergence of PCV2 worldwide.

A model selection approach to discover age-dependent gene expression patterns using quantile regression models.

BACKGROUND: It has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns. RESULTS: Here we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models. CONCLUSION: In this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.

Differential variability analysis of gene expression and its application to human diseases.

MOTIVATION: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significant increase or decrease in expression variability (variance). As these observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting. RESULTS: Here we propose a novel analysis for changes in gene expression variability between groups of samples, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal. AVAILABILITY: The R source code for differential variability analysis is available from the contact authors upon request.

Macronutrient intakes and the lifespan-fecundity trade-off: a geometric framework agent-based model.

Lifespan and fecundity, the main components in evolutionary fitness, are both strongly affected by nutritional state. Geometric framework of nutrition (GFN) experiments has shown that lifespan and fecundity are separated in nutrient space leading to a functional trade-off between the two traits. Here we develop a spatially explicit agent-based model (ABM) using the GFN to explore how ecological factors may cause selection on macronutrient appetites to optimally balance these life-history traits. We show that increasing the risk of extrinsic mortality favours intake of a mixture of nutrients that is associated with maximal fecundity at the expense of reduced longevity and that this result is robust across spatial and nutritional environments. These model behaviours are consistent with what has been observed in studies that quantify changes in life history in response to environmental manipulations. Previous GFN-derived ABMs have treated fitness as a single value. This is the first such model to instead decompose fitness into its primary component traits, longevity and fecundity, allowing evolutionary fitness to be an emergent property of the two. Our model demonstrates that selection on macronutrient appetites may affect life-history trade-offs and makes predictions that can be directly tested in artificial selection experiments.

Inferring differential leukocyte activity from antibody microarrays using a latent variable model.

Recent development of cluster of differentiation (CD) antibody arrays has enabled expression levels of many leukocyte surface CD antigens to be monitored simultaneously. Such membrane-proteome surveys have provided a powerful means to detect changes in leukocyte activity in various human diseases, such as cancer and cardiovascular diseases. The challenge is to devise a computational method to infer differential leukocyte activity among multiple biological states based on antigen expression profiles. Standard DNA microarray analysis methods cannot accurately infer differential leukocyte activity because they often fail to take the cell-to-antigen relationships into account. Here we present a novel latent variable model (LVM) approach to tackle this problem. The idea is to model each cell type as a latent variable, and represent the class-to-cell and cell-to-antigen relationships as a LVM. Once the parameters of the LVM are learned from the data, differentially active leukocytes can be easily identified from the model. We describe the model formulation and assumptions which lead to an efficient expectation-maximization algorithm. Our LVM method was applied to re-analyze two cardiovascular disease datasets. We show that our results match existing biological knowledge better than other methods such as gene set enrichment analysis. Furthermore, we discuss how our approach can be extended to become a general framework for gene set analysis for DNA microarrays.

Draft Genome Sequence of Subantarctic Rhodococcus sp. Strain 1139.

The draft genome sequence of subantarctic Rhodococcus sp. strain 1139 is reported here. The genome size is 7.04 Mb with high G+C content (62.3%) and it contains a large number of genes involved in lipid synthesis. This lipid synthesis system is characteristic of oleaginous Actinobacteria, which are of interest for biofuel production.

Draft Genome Sequences of Two Novel Sub-Antarctic Williamsia Species.

Illumina MiSeq shotgun sequencing technology was used to sequence the genomes of two novel sub-Antarctic Williamsia species, designated strains 1135 and 1138. The estimated genome sizes for strains 1135 and 1138 are 5.99 Mb and 6.08 Mb, respectively. This genome sequence information will aid in understanding the lipid metabolic pathways of cold-tolerant Williamsia species.

Draft Genome Sequences of Three Sub-Antarctic Rhodococcus spp., Including Two Novel Psychrophilic Genomospecies.

The draft genome sequences of three sub-Antarctic Rhodococcus sp. strains-1159, 1163, and 1168-are reported here. The estimated genome sizes were 7.09 Mb with a 62.3% GC content for strain 1159, 4.45 Mb with a 62.3% GC content for strain 1163, and 5.06 Mb with a 62.10% GC content for strain 1168.