Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.].

Genome annotations are a common way to represent genomic features such as genes, regulatory elements or epigenetic modifications. The amount of overlap between two annotations is often used to ascertain if there is an underlying biological connection between them. In order to distinguish between true biological association and overlap by pure chance, a robust measure of significance is required. One common way to do this is to determine if the number of intervals in the reference annotation that intersect the query annotation is statistically significant. However, currently employed statistical frameworks are often either inefficient or inaccurate when computing P-values on the scale of the whole human genome.

Nerve cells produce electrical impulses ("spikes") through the coordinated opening and closing of ion channels. Markov processes with voltage-dependent transition rates capture the stochasticity of spike generation at the cost of complex, time-consuming simulations. Schmandt and Galán introduced a novel method, based on the stochastic shielding approximation, as a fast, accurate method for generating approximate sample paths with excellent first and second moment agreement to exact stochastic simulations. We previously analyzed the mathematical basis for the method's remarkable accuracy, and showed that for models with a Gaussian noise approximation, the stationary variance of the occupancy at each vertex in the ion channel state graph could be written as a sum of distinct contributions from each edge in the graph. We extend this analysis to arbitrary discrete population models with first-order kinetics. The resulting decomposition allows us to rank the "importance" of each edge's contribution to the variance of the current under stationary conditions. In most cases, transitions between open (conducting) and closed (non-conducting) states make the greatest contributions to the variance, but there are exceptions. In a 5-state model of the nicotinic acetylcholine receptor, at low agonist concentration, a pair of "hidden" transitions (between two closed states) makes a greater contribution to the variance than any of the open-closed transitions. We exhaustively investigate this "edge importance reversal" phenomenon in simplified 3-state models, and obtain an exact formula for the contribution of each edge to the variance of the open state. Two conditions contribute to reversals: the opening rate should be faster than all other rates in the system, and the closed state leading to the opening rate should be sparsely occupied. When edge importance reversal occurs, current fluctuations are dominated by a slow noise component arising from the hidden transitions.

In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users' activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.

The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microbial communities. However, in FOMC, the number of parameters grows exponentially with the increase of the order of Markov Chain (MC). Under a fixed high order of MC, the parameters might not be accurately estimated owing to the limitation of sequencing depth. In our study, we investigate an alternative to FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data. The VLMC originally designed for long sequences was extended to apply to high-throughput sequencing reads and the strategies to estimate the corresponding parameters were developed. The flexible number of parameters in VLMC avoids estimating the vast number of parameters of high-order MC under limited sequencing depth. Different from the manual selection in FOMC, VLMC determines the MC order adaptively. Several beta diversity measures based on VLMC were applied to compare the bacterial RNA-Seq and metatranscriptomic datasets. Experiments show that VLMC outperforms FOMC to model the background sequences in transcriptomic and metatranscriptomic samples. A software pipeline is available at https://d2vlmc.codeplex.com.

Shortened rapid eye movement (REM) sleep latency and increased REM sleep amount are presumed biological markers of depression. These sleep alterations are also observable in several animal models of depression as well as during the rebound sleep after selective REM sleep deprivation (RD). Furthermore, REM sleep fragmentation is typically associated with stress procedures and anxiety. The selective serotonin reuptake inhibitor (SSRI) antidepressants reduce REM sleep time and increase REM latency after acute dosing in normal condition and even during REM rebound following RD. However, their therapeutic outcome evolves only after weeks of treatment, and the effects of chronic treatment in REM-deprived animals have not been studied yet.

The purpose of the present study was to examine male and female Olympic taekwondo competitors' movement patterns according to their tactical actions by applying a Markov processes analysis. To perform this study, 11,474 actions by male competitors and 12,980 actions by female competitors were compiled and analyzed. The results yielded 32 significant sequences among male competitors and 30 among female competitors. Male competitors demonstrated 11 sequences initiated by an attack, 11 initiated by a counterattack, and 10 initiated by a defensive action. Female competitors demonstrated nine sequences initiated by an attack, 11 initiated by a counterattack, and 10 initiated by a defensive move. The five most popular sequences were the opening and dodge, the direct attack and simultaneous counterattack, the dodge with a direct attack, the indirect attack and simultaneous counterattack, and the simultaneous counterattack with a direct attack. Markov chains help provide coaches and researchers with relevant information about the frequency of actions, both in terms of their frequency of occurrence and the order of their occurrence, during a real competition. It is suggested that coaches and athletes focus on these patterns when training for a real competition.

Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. Although several studies of phylogenetic MCMC convergence exist, these have focused on simulated data sets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives from a relatively small family of analyses under ideal conditions. To address this, we present an overview of commonly applied phylogenetic MCMC diagnostics and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses. Many analyses appeared to perform well and failures in convergence were most likely to be detected using the average standard deviation of split frequencies, a diagnostic that compares topologies among independent chains. Different diagnostics yielded different information about failed convergence, demonstrating that multiple diagnostics must be employed to reliably detect problems. The number of taxa and average branch lengths in analyses have clear impacts on MCMC performance, with more taxa and shorter branches leading to more difficult convergence. We show that the usage of models that include both Γ-distributed among-site rate variation and a proportion of invariable sites is not broadly problematic for MCMC convergence but is also unnecessary. Changes to heating and the usage of model-averaged substitution models can both offer improved convergence in some cases, but neither are a panacea.

Cross-impact balance (CIB) analysis provides a system-theoretical view of scenarios useful for investigating complex socio-economic systems. CIB can synthesize a variety of qualitative or quantitative inputs and return information suggestive of system evolution. Current software tools for CIB are limited to identifying system attractors as well as describing system evolution from only one scenario of initial conditions at a time. Through this study, we enhance CIB by developing and applying a method that considers all possible system evolutions as transitions in a Markov chain. We investigated a simple three-variable system (27 possible scenarios) of the demographic transition and were able to generally replicate the findings of traditional CIB. Through our experiments with four possible approaches to produce CIB Markov chains, we found that information about transition pathways is gained; however, information about system attractors may be lost. Through a comparison of model results to a recent literature review on human demography, we found that low-income countries are more likely to remain stuck in a demographic trap if economic development is not prioritized alongside educational gains. Future work could test our comparative methodological findings for systems comprised of more than three variables.

Recent discoveries suggest that the candidate superphyla Patescibacteria and DPANN constitute a large fraction of the phylogenetic diversity of Bacteria and Archaea. Their small genomes and limited coding potential have been hypothesized to be ancestral adaptations to obligate symbiotic lifestyles. To test this hypothesis, we performed cell-cell association, genomic, and phylogenetic analyses on 4,829 individual cells of Bacteria and Archaea from 46 globally distributed surface and subsurface field samples. This confirmed the ubiquity and abundance of Patescibacteria and DPANN in subsurface environments, the small size of their genomes and cells, and the divergence of their gene content from other Bacteria and Archaea. Our analyses suggest that most Patescibacteria and DPANN in the studied subsurface environments do not form specific physical associations with other microorganisms. These data also suggest that their unusual genomic features and prevalent auxotrophies may be a result of ancestral, minimal cellular energy transduction mechanisms that lack respiration, thus relying solely on fermentation for energy conservation.

We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10 000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments.

Behavioral states alternate between wakefulness (wk), rapid eye movement (rem) and non-rem (nrem) sleep at time scale of hours i.e., light and dark cycle rhythms and from several tens of minutes to seconds (i.e., brief awakenings during sleep). Using statistical analysis of bout duration, Markov chains of sleep-wk dynamics and quantitative EEG analysis, we evaluated the influence of light/dark (ld) changes on brain function along the sleep-wk cycle. Bout duration (bd) histograms and Kaplan-Meier (km) survival curves of wk showed a bimodal statistical distribution, suggesting that two types of wk do exist: brief-wk (wkb) and long-wk (wkl). Light changes modulated specifically wkl bouts, increasing its duration during active/dark period. In contrast, wkb, nrem and rem bd histograms and km curves did not change significantly along ld cycle. Hippocampal eeg of both types of wk were different: in comparison wkb showed a lower spectral power in fast gamma and fast theta bands and less emg tone. After fitting a four-states Markov chain to mice hypnograms, moreover in states transition probabilities matrix was found that: in dark/active period, state-maintenance probability of wkl increased, and probability of wkl to nrem transition decreased; the opposite was found in light period, favoring the hypothesis of the participation of brief wk into nrem-rem intrinsic sleep cycle, and the role of wkl in SWS homeostasis. In conclusion, we propose an extended Markov model of sleep using four stages (wkl, nrem, rem, wkb) as a fully adequate model accounting for both modulation of sleep-wake dynamics based on the differential regulation of long-wk (high gamma/theta) epochs during dark and light phases.

Ribosomal surveillance pathways scan for ribosomes that are transiently paused or terminally stalled owing to structural elements in mRNAs or nascent chain sequences1, 2. Some stalls in budding yeast are sensed by the GTPase Hbs1, which loads Dom34, a catalytically inactive member of the archaeo-eukaryotic release factor 1 superfamily. Hbs1-Dom34 and the ATPase Rli1 dissociate stalled ribosomes into 40S and 60S subunits. However, the 60S subunits retain the peptidyl-tRNA nascent chains, which recruit the ribosome quality control complex that consists of Rqc1-Rqc2-Ltn1-Cdc48-Ufd1-Npl4. Nascent chains ubiquitylated by the E3 ubiquitin ligase Ltn1 are extracted from the 60S subunit by the ATPase Cdc48-Ufd1-Npl4 and presented to the 26S proteasome for degradation3-9. Failure to degrade the nascent chains leads to protein aggregation and proteotoxic stress in yeast and neurodegeneration in mice10-14. Despite intensive investigations on the ribosome quality control pathway, it is not known how the tRNA is hydrolysed from the ubiquitylated nascent chain before its degradation. Here we show that the Cdc48 adaptor Vms1 is a peptidyl-tRNA hydrolase. Similar to classical eukaryotic release factor 1, Vms1 activity is dependent on a conserved catalytic glutamine. Evolutionary analysis indicates that yeast Vms1 is the founding member of a clade of eukaryotic release factor 1 homologues that we designate the Vms1-like release factor 1 clade.

To reveal transition dynamics of global neuronal networks of math-gifted adolescents in handling long-chain reasoning, this study explores momentary phase-synchronized patterns, that is, electroencephalogram (EEG) synchrostates, of intracerebral sources sustained in successive 50 ms time windows during a reasoning task and non-task idle process. Through agglomerative hierarchical clustering for functional connectivity graphs and nested iterative cosine similarity tests, this study identifies seven general and one reasoning-specific prototypical functional connectivity patterns from all synchrostates. Markov modeling is performed for the time-sequential synchrostates of each trial to characterize the interstate transitions. The analysis reveals that default mode network, central executive network (CEN), dorsal attention network, cingulo-opercular network, left/right ventral frontoparietal network, and ventral visual network aperiodically recur over non-task or reasoning process, exhibiting high predictability in interactively reachable transitions. Compared to non-gifted subjects, math-gifted adolescents show higher fractional occupancy and mean duration in CEN and reasoning-triggered transient right frontotemporal network (rFTN) in the time course of the reasoning process. Statistical modeling of Markov chains reveals that there are more self-loops in CEN and rFTN of the math-gifted brain, suggesting robust state durability in temporally maintaining the topological structures. Besides, math-gifted subjects show higher probabilities in switching from the other types of synchrostates to CEN and rFTN, which represents more adaptive reconfiguration of connectivity pattern in the large-scale cortical network for focused task-related information processing, which underlies superior executive functions in controlling goal-directed persistence and high predictability of implementing imagination and creative thinking during long-chain reasoning.

Dairy cattle breeds have been exposed to intense artificial selection for milk production traits over the last fifty years. In Italy, where over 80% of milk is processed into cheese, selection has also focused on cheese-making traits. Due to a deep-rooted tradition in cheese-making, currently fifty Italian cheeses are marked with the Protected Designation of Origin (PDO) label as they proved traditional land of origin and procedures for milk transformation. This study aimed to explore from a genetic point of view if the presence of such diverse productive contexts in Italy have shaped in a different manner the genome of animals originally belonging to a same breed. We analyzed high density genotype data from 1000 Italian Holstein cows born between 2014 and 2018. Those animals were either farmed in one of four Italian PDO consortia or used for drinkable milk production only. Runs of Homozygosity, Bayesian Information Criterion and Discriminant Analysis of Principal Components were used to evaluate potential signs of genetic divergence within the breed. We showed that the analyzed Italian Holstein cows have genomic inbreeding level above 5% in all subgroups, reflecting the presence of ongoing artificial selection in the breed. Our study provided a comprehensive representation of the genetic structure of the Italian Holstein breed, highlighting the presence of potential genetic subgroups due to divergent dairy farming systems. This study can be used to further investigate genetic variants underlying adaptation traits in these subgroups, which in turn might be used to design more specialized breeding programs.

We revived and implemented a method developed by Kuhn in 1934, originally only published in German, that is, the so-called "freely jointed chain" model. This approach turned out to be surprisingly useful for analyzing state-of-the-art computer simulations of the thermosensitive coil-globule transition of N-Isopropylacrylamide 20-mer. Our atomistic computer simulations are orders of magnitude longer than those of previous studies and lead to a reliable description of thermodynamics and kinetics at many different temperatures. The freely jointed chain model provides a coordinate system, which allows us to construct a Markov state model of the conformational transitions. Furthermore, this guarantees a reliable reconstruction of the kinetics in back-and-forth directions. In addition, we obtain a description of the high diversity and variability of both conformational states. Thus, we gain a detailed understanding of the coil-globule transition. Surprisingly, conformational entropy turns out to play only a minor role in the thermodynamic balance of the process. Moreover, we show that the radius of gyration is an unexpectedly unsuitable coordinate to comprehend the transition kinetics because it does not capture the high conformational diversity within the different states. Consequently, the approach presented here allows for an exhaustive description and resolution of the conformational ensembles of arbitrary linear polymer chains.

To end the largest known outbreak of Ebola virus disease (EVD) in West Africa and to prevent new transmissions, rapid epidemiological tracing of cases and contacts was required. The ability to quickly identify unknown sources and chains of transmission is key to ending the EVD epidemic and of even greater importance in the context of recent reports of Ebola virus (EBOV) persistence in survivors. Phylogenetic analysis of complete EBOV genomes can provide important information on the source of any new infection. A local deep sequencing facility was established at the Mateneh Ebola Treatment Centre in central Sierra Leone. The facility included all wetlab and computational resources to rapidly process EBOV diagnostic samples into full genome sequences. We produced 554 EBOV genomes from EVD cases across Sierra Leone. These genomes provided a detailed description of EBOV evolution and facilitated phylogenetic tracking of new EVD cases. Importantly, we show that linked genomic and epidemiological data can not only support contact tracing but also identify unconventional transmission chains involving body fluids, including semen. Rapid EBOV genome sequencing, when linked to epidemiological information and a comprehensive database of virus sequences across the outbreak, provided a powerful tool for public health epidemic control efforts.

Fibrinogen is a highly pleiotropic protein that is involved in the final step of the coagulation cascade, wound healing, inflammation, and angiogenesis. Heterozygous mutations in Aα, Bβ, or γ fibrinogen-chain genes (FGA, FGB, FGG) have been described as being responsible for fibrinogen deficiencies (hypofibrinogenemia, hypo-dysfibrinogenemia, dysfibrinogenemia) and for more rare conditions, such as fibrinogen storage disease and hereditary renal amyloidosis. Instead, biallelic mutations have been associated with afibrinogenemia/severe hypofibrinogenemia, i.e., the severest forms of fibrinogen deficiency, affecting approximately 1-2 cases per million people. However, the "true" prevalence for these conditions on a global scale is currently not available. Here, we defined the mutational burden of the FGA, FGB, and FGG genes, and estimated the prevalence of inherited fibrinogen disorders through a systematic analysis of exome/genome data from ~140,000 individuals belonging to the genome Aggregation Database. Our analysis showed that the world-wide prevalence for recessively-inherited fibrinogen deficiencies could be 10-fold higher than that reported so far (prevalence rates vary from 1 in 10⁶ in East Asians to 24.5 in 10⁶ in non-Finnish Europeans). The global prevalence for autosomal-dominant fibrinogen disorders was estimated to be ~11 in 1000 individuals, with heterozygous carriers present at a frequency varying from 3 every 1000 individuals in Finns, to 1-2 every 100 individuals among non-Finnish Europeans and Africans/African Americans. Our analysis also allowed for the identification of recurrent (i.e., FGG-p.Ala108Gly, FGG-Thr47Ile) or ethnic-specific mutations (e.g., FGB-p.Gly103Arg in Admixed Americans, FGG-p.Ser245Phe in Africans/African Americans).

SUMOylation is a post-translational modification conserved in eukaryotic organisms that involves the covalent attachment of the small ubiquitin-like protein SUMO to internal lysine residues in target proteins. This tag usually alters the interaction surface of the modified protein and can be translated into changes in its biological activity, stability or subcellular localization, among other possible outputs. SUMO can be attached as a single moiety or as SUMO polymers in case there are internal acceptor sites in SUMO itself. These chains have been shown to be important for proteasomal degradation as well as for the formation of subnuclear structures such as the synaptonemal complex in Saccharomyces cerevisiae or promyelocytic leukemia nuclear bodies in mammals. In this work, we have examined SUMO chain formation in the protozoan parasite Trypanosoma brucei. Using a recently developed bacterial strain engineered to produce SUMOylated proteins we confirmed the ability of TbSUMO to form polymers and determined the type of linkage using site-directed mutational analysis. By generating transgenic procyclic parasites unable to form chains we demonstrated that although not essential for normal growth, SUMO polymerization determines the localization of the modified proteins in the nucleus. In addition, FISH analysis of telomeres showed a differential positioning depending on the polySUMOylation abilities of the cells. Thus, our observations suggest that TbSUMO chains might play a role in establishing interaction platforms contributing to chromatin organization.

Really interesting new gene (RING)-finger protein 52 (RNF52), an E3 ubiquitin ligase, is found in eukaryotes from yeast to humans. Human RNF52 is known as breast cancer type 1 susceptibility protein (BRCA1)-associated protein 2 (BRAP or BRAP2). The central catalytic domain of BRAP comprises four subdomains: nucleotide-binding α/β plait (NBP), really interesting new gene (RING) zinc finger, ubiquitin-specific protease (UBP)-like zinc finger (ZfUBP), and coiled-coil (CC). This domain architecture is conserved in RNF52 orthologs; however, the domain's function in the ubiquitin system has not been delineated. In the present study, we discovered that the RNF52 domain, comprising NBP-RING-ZfUBP-CC, binds to ubiquitin chains (oligo-ubiquitin) but not to the ubiquitin monomers, and can utilize various ubiquitin chains for ubiquitylation and auto-ubiquitylation. The RNF52 domain preferentially bound to M1- and K63-linked di-ubiquitin chains, weakly to K27-linked chains, but not to K6-, K11-, or K48-linked chains. The binding preferences of the RNF52 domain for ubiquitin-linkage types corresponded to ubiquitin usage in the ubiquitylation reaction, except for K11-, K29-, and K33-linked chains. Additionally, the RNF52 domain directly ligated the intact M1-linked, tri-, and tetra-ubiquitin chains and recognized the structural alterations caused by the phosphomimetic mutation of these ubiquitin chains. Full-length BRAP had nearly the same specificity for the ubiquitin-chain types as the RNF52 domain alone. Mass spectrometry analysis of oligomeric ubiquitylation products, mediated by the RNF52 domain, revealed that the ubiquitin-linkage types and auto-ubiquitylation sites depend on the length of ubiquitin chains. Here, we propose a model for the oligomeric ubiquitylation process, controlled by the RNF52 domain, which is not a sequential assembly process involving monomers.