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At least 19 recordsLinked to original sources
PubMed2024

BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees.

Next-generation sequencing is widely applied to the investigation of pedigree data for gene discovery. However, identifying plausible disease-causing variants within a robust statistical framework is challenging. Here, we introduce BICEP: a Bayesian inference tool for rare variant causality evaluation in pedigree-based cohorts. BICEP calculates the posterior odds that a genomic variant is causal for a phenotype based on the variant cosegregation as well as a priori evidence such as deleteriousness and functional consequence. BICEP can correctly identify causal variants for phenotypes with both Mendelian and complex genetic architectures, outperforming existing methodologies. Additionally, BICEP can correctly down-weight common variants that are unlikely to be involved in phenotypic liability in the context of a pedigree, even if they have reasonable cosegregation patterns. The output metrics from BICEP allow for the quantitative comparison of variant causality within and across pedigrees, which is not possible with existing approaches.

Pedigree↗
PubMed1991

A computational model of approximate Bayesian inference for associating clinical algorithms with decision analyses.

The lack of rationale or explanation is a major deficiency of clinical algorithms. To address this issue, the authors present a computational model for associating decision analyses with clinical algorithms. Automata theory is used to model categorical reasoning with approximate Bayesian inference based on probability intervals. This approximation reduces the number of computations to linear-order instead of the exponential-order combinations of clinical findings in exact Bayes. The linkage of decision analyses and clinical algorithms by means of this model exploits a new concept of "regular" clinical algorithms and their equivalency in theory and provides valuable perspectives in practice for developers of clinical algorithms.

Algorithms↗
PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗
PubMed2025

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Bayes Theorem↗
PubMed2025

ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.

Phylodynamic models capture joint epidemiological-evolutionary dynamics during an outbreak, providing a powerful tool to enhance understanding and management of disease transmission. Existing phylodynamic approaches, however, mostly rely on various non-mechanistic or semi-mechanistic approximations of the underlying epidemiological-evolutionary process. Previous work by Lau and colleagues has shown that full Bayesian mechanistic models, without relying on these approximations, can enable highly accurate joint inference of the epidemiological-evolutionary dynamics including the unobserved transmission tree. However, the Lau method faces major computational bottlenecks. As the volume of genomic data collected during outbreaks continues to grow, it is crucial to develop scalable yet accurate phylodynamic methods. Here we propose a new Bayesian phylodynamic model, overcoming the major scalability issue in the previous method and enabling a readily deployable, yet accurate, phylodynamic modeling framework. Specifically, we develop a scalable spatio-temporal phylodynamic framework for inferring the transmission tree (ScITree) and other key epidemiological parameters considering the infinite sites assumption in modeling mutation on the sequence level, in contrast to the Lau method in which mutation was modeled explicitly on the nucleotide level. Our approach features full Bayesian implementation utilizing an exact likelihood to mechanistically integrate epidemiological and evolutionary processes. We develop a computationally-efficient data-augmentation Markov Chain Monte Carlo algorithm, inferring key model parameters and unobserved dynamics including the transmission tree. We assess performance of our method using multiple simulated outbreak datasets. Our results indicate that our method can achieve high inference accuracy, comparable to the performance of the Lau method. Additionally, our method scales significantly more efficiently for large outbreaks, with computing time increasing linearly with outbreak size, compared to the exponential scaling of the Lau method. We also demonstrate our method's utility by applying our validated modeling framework to a dataset describing a foot-and-mouth disease outbreak in the UK. Our results show that our method is able to generate estimates of the transmission dynamics consistent with those from the prior method, further demonstrating the robustness of our new approach. In summary, our method provides a computationally-efficient, highly scalable, accurate modeling framework for inferring the joint spatio-temporal dynamics of epidemiological and evolutionary processes, facilitating timely and effective outbreak responses in space and time. Our method is implemented in our R package ScITree.

Bayes Theorem↗
PubMed1991

A Monte Carlo method for Bayesian inference in frailty models.

Many analyses in epidemiological and prognostic studies and in studies of event history data require methods that allow for unobserved covariates or "frailties." Clayton and Cuzick (1985, Journal of the Royal Statistical Society, Series A 148, 82-117) proposed a generalization of the proportional hazards model that implemented such random effects, but the proof of the asymptotic properties of the method remains elusive, and practical experience suggests that the likelihoods may be markedly nonquadratic. This paper sets out a Bayesian representation of the model in the spirit of Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221) and discusses inference using Monte Carlo methods.

Algorithms↗
PubMed2026

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

Journal Article↗
PubMed2025

Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.

The Ancestral Recombination Graph (ARG), which describes the genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have substantially increased ARG reconstruction scalability, but they rely on approximations that can reduce accuracy, especially under model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. Here, to address these challenges, we introduce SINGER (sampling and inferring of genealogies with recombination), a method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for hundreds of whole-genome sequences. Through extensive simulations, we demonstrate SINGER's enhanced accuracy and robustness to model misspecification compared to existing methods. We demonstrate the utility of SINGER by applying it to individuals of British and African descent within the 1000 Genomes Project, identifying signals of population differentiation, archaic introgression and strong support for ancient polymorphism in the human leukocyte antigen region shared across primates.

Humans↗
PubMed2026

Plastome evolution and phylogenomic relationships in Ajuga (Lamiaceae, Ajugoideae).

BACKGROUND: Ajuga is currently known to include approximately 69 species, with a combined distribution extending throughout Eurasia, Africa, and Australia. Its popularity and significance are largely based on an extensive history of medicinal and horticultural use. It is divided into two sections based on morphological characters, and this sectional classification is also reflected in pronounced geographic patterns. Although previous studies have largely focused on Ajuga sect. Ajuga in East Asia, A. sect. Chamaepithys, which ranges from the Mediterranean to Central Asia, remains insufficiently sampled, thereby limiting a comprehensive understanding of infrageneric sectional relationships within the genus. Here, we generated complete plastid genomes for 12 species representing both sections of the genus and used these data to characterize plastome structure and infer evolutionary relationships. RESULTS: In this study, 21 Ajuga plastomes were analyzed, including 12 newly sequenced plastomes and 9 previously published plastomes representing 19 species. Comparative analyses showed that all plastomes exhibited a highly conserved quadripartite structure, with genome sizes ranging from 149,963 to 150,740 bp and GC contents varying from 38.2% to 38.3%. Each plastome contained 133 genes, including 88 protein-coding genes, 37 transfer RNA genes, and 8 ribosomal RNA genes. The boundaries between the inverted repeat (IR) and single-copy (SC) regions were also highly conserved across species. In addition, 796 simple sequence repeats (SSRs), 874 long repeat sequences (LRSs), and 12 highly variable regions (ccsA-ndhD, ndhF-rpl32, petA-psbJ, rpl32-trnL-UAG, rps2-rpoC2, trnH-GUG-psbA, trnK-UUU-rps16, trnP-UGG-psaJ, trnT-UGU-trnL-UAA, ycf15-trnL-CAA, ndhF, and ycf1) were identified among the 21 plastomes. Phylogenetic analyses based on four datasets and conducted using Maximum Likelihood and Bayesian Inference recovered two major clades corresponding to the traditionally recognized sectional classification, with one distributed from the Mediterranean to Central Asia and the other in East Asia. CONCLUSION: This study represents the most comprehensive plastome-based sampling of Ajuga to date, including representative species from the Mediterranean, Central Asia, and East Asia. Our results have significantly enhanced our understanding of its infrageneric relationships. The plastome resources generated in this study provide a valuable foundation for future research on species delimitation, phylogeny, and the evolutionary history of Ajuga.

Phylogeny↗
PubMed2026

Insights Into the Structural Features, Codon Usage Patterns, and Phylogenetic Analysis in Neoniphon argenteus (Teleostei: Holocentriformes) Based on Complete Mitochondrial Genome.

Neoniphon argenteus, a widely distributed nocturnal coral reef fish in the family Holocentridae, plays an important role in maintaining coral reef ecosystem health, yet its phylogenetic position remains poorly resolved. To bridge this gap, we sequenced and analyzed the complete mitochondrial genome of a specimen from the South China Sea to characterize its structural features, codon usage patterns, and phylogenetic relationships. The 16,569 bp mitogenome (GenBank: PP190474.1) encodes 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and two non-coding regions, exhibiting a distinct A + T bias. All tRNAs fold into typical cloverleaf secondary structures except tRNA-Ser (AGN), which lacks the dihydrouridine (DHU) arm. The control region contains palindromic motifs (TACAT/ATGTA) capable of forming hairpin structures and five conserved sequence blocks, whereas the OL region harbors a conserved 5'-GCCGG-3' motif. RSCU analysis revealed 31 frequently used codons (RSCU > 1) with a pronounced preference for A/C-ending codons. The ΔRSCU method identified 10 candidate optimal codons (GCA, CAA, GAA, GGA, AUU, CUA, CCA, CGA, ACA, and GUC). Selection pressure analysis using EasyCodeML and site-specific models indicated that all PCGs are predominantly under purifying selection, with no significant evidence of pervasive positive selection. ND6 exhibited elevated pairwise Ka/Ks ratios (mean = 1.209 ± 0.047), consistent with reduced selective constraint rather than adaptive evolution. Phylogenetic analysis of 19 Holocentriformes species using maximum likelihood and Bayesian inference with partitioned models based on 13 PCGs and two rRNA genes (12S and 16S) assigned all taxa to two well-supported subfamilies (Holocentrinae and Myripristinae). Within Holocentrinae, Neoniphon species form a monophyletic clade nested within a paraphyletic Sargocentron, suggesting that the genus Sargocentron as currently defined is not monophyletic. This study provides useful baseline molecular data for further exploration of the evolutionary history of N. argenteus and other members of Holocentriformes.

Holocentridae↗
PubMedDate not supplied

Medical expert systems based on causal probabilistic networks.

Causal probabilistic networks (CPNs) offer new methods by which you can build medical expert systems that can handle all types of medical reasoning within a uniform conceptual framework. Based on the experience from a commercially available system and a couple of large prototype systems, it appears that CPNs are now an attractive alternative to other methods. A CPN is an intensional model of a domain, and it is therefore conceptually much closer to qualitative reasoning systems and to simulation systems than to rule-based or logic-based systems. Recent progress in Bayesian inference in networks has yielded computationally efficient methods. The inference method used follows the fundamental axioms of probability theory, and gives a sound framework for causal and diagnostic (deductive and abductive) reasoning under uncertainty. Experience with the prototypes indicates that it may be possible to use decision theory as a rational approach to test planning and therapy planning. The way in which knowledge is acquired and represented in CPNs makes it easy to express 'deep knowledge' for example in the form of physiological models, and the facilities for learning make it possible to make a smooth transition from expert opinion to statistics based on empirical data.

Artificial Intelligence↗
PubMed2026

Mitochondrial genome characteristics and phylogenetic analysis of Ramaria longispora.

This study, for the first time, assembled and annotated the complete mitochondrial genome of R. longispora using high-throughput sequencing technology. The genome is a circular molecule with a total length of 157,712 bp and a GC content of 31.55%. It encodes 71 genes, including 15 core protein-coding genes (PCGs), 25 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes, 5 free-stranding open reading frames (ORFs), and 24 intronic ORFs. Among these, most free-stranding ORFs have unknown functions but include a DNA polymerase gene, while the intronic ORFs primarily encode LAGLIDADG and GIY-YIG endonucleases. The mitochondrial genome contains 39 introns. Phylogenetic analyses based on 15 core PCGs using Bayesian inference (BI) and maximum likelihood (ML) methods revealed that this R. longispora is most closely related to Ramaria flavescens and Ramaria ichnusensis. This study provides foundational data for mitochondrial genome research in the Ramaria genus and offers important references for taxonomic and evolutionary studies of this group.

Mitochondrial genome↗
PubMed2026

GAMMA: gap-aware motif mining under incomplete labeling with applications to MHC motifs.

MOTIVATION: Sequence motif identification is crucial for understanding molecular recognition, particularly in immune responses involving peptide binding to major histocompatibility complex (MHC) Class I molecules for antigen presentation to T cells. Traditionally, MHC Class I binding motifs are assumed to be contiguous and span nine amino acids. However, structural evidence suggests that binding may involve nonadjacent residues, challenging the assumptions of existing methods. RESULTS: In this study, we propose Gap-Aware Motif Mining Algorithm (GAMMA), a probabilistic framework designed to identify noncontiguous motifs under conditions of incomplete labeling. GAMMA employs Bayesian inference with Markov chain Monte Carlo sampling to jointly estimate motif parameters, binding locations, and the relative spacing between binding positions. Through extensive simulations and real-world applications to MHC Class I peptide datasets, GAMMA outperforms existing motif discovery tools such as GLAM2 in accurately localizing binding residues and identifying the underlying motifs. Notably, our results suggest that the true number of binding residues may be eight, fewer than the commonly assumed nine. In addition, for longer peptides, the model captures increased flexibility in the central region, consistent with structural observations that peptides may bulge in the middle. AVAILABILITY AND IMPLEMENTATION: The raw data and the source codes are available on GitHub (https://github.com/RanLIUaca/GAMMAmotif).

Amino Acid Motifs↗
PubMed2025

Detecting Interspecific Positive Selection Using Convolutional Neural Networks.

Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. To address these issues, we trained and tested convolutional neural network models on simulated data and achieved higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident when performing inference on noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore the generalizability of our convolutional neural network models to unseen evolutionary scenarios and identify future avenues to achieve broader utility. Once trained, our convolutional neural network model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multigene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.

Neural Networks, Computer↗
PubMed2026

Mapping Sub-National Respiratory Virus Circulation in Cambodia Using Metatranscriptomic Sequencing: A Multi-Center Hospital-Based Surveillance Study.

BACKGROUND: Genomic surveillance can guide early detection of and response to emerging epidemics. Metatranscriptomic sequencing was used to investigate sub-national respiratory virus circulation in Cambodia from 2020 to 2023. METHODS: Nasopharyngeal swabs were collected from individuals aged 2 months to 65 years with influenza-like illness in four Cambodian hospitals. Metatranscriptomic data were generated by short-read RNA sequencing. Bernoulli space-time scan statistics were used to identify temporal virus clusters. Bayesian inference of phylogenetic trees was used to compute divergence times for temporally clustered, highly represented viruses (influenza A/H3N2 and B, Betacoronavirus 1, respiratory syncytial virus [RSV] A and B), and publicly available global influenza virus genomes. RESULTS: Of 1093 individuals, 499 (45.7%) had detectable respiratory viruses belonging to 68 distinct species. Moderate (N > 20) discrete time-clusters were noted of RSV-A (37 cases), Betacoronavirus 1 (21 cases), RSV-B (22 cases), and A/H3N2 (30 cases). The posterior median of time to most recent common ancestor ranged from 0.71 years (95% HPD 0.38-1.10) for Betacoronavirus 1 and 1.31 years (95% HPD 0.60-3.20) for A/H3N2, to 2.75 years (1.82-4.26) for RSV-A and 4.79 years (2.39-7.74) for RSV-B. A/H3N2 and influenza B virus genomes mapped to clades 3C.2a1b.2a.2a and Victoria 1A.3a.2, respectively, and inter-mixed with concurrent global strains. CONCLUSIONS: Multiple respiratory viruses circulated at a sub-national level in Cambodia from 2020 to 2023 despite pandemic disruptions. Influenza virus population diversity decreased during the height of lockdown but recovered in mid-2022. Re-emerging influenza strains were distinct from historically circulating strains and clustered with contemporaneous global variants, suggesting multiple external introductions.

Humans↗
PubMed2025

Contrasting Patterns of Connectivity Between Populations of Euphotic and Mesophotic Hydroids in Reunion Island Support the Deep Reef Refuge Hypothesis.

In the context of coral reef decline, mesophotic coral ecosystems (MCEs, 30-150 m) offer hope for the recovery of degraded euphotic reefs. The Deep Reef Refuge Hypothesis (DRRH) postulates the potential of mesophotic reefs to reseed euphotic reefs. This hypothesis needs to be further tested by estimating connectivity along the depth gradient. Mesophotic data are lacking worldwide, particularly in the southwestern Indian Ocean (SWIO). Here, using a total of 2218 samples collected at depths ranging from 10 to 103 m, we estimated the connectivity of 7 hydroid species sampled at euphotic, upper, and lower mesophotic depths around Reunion Island using a multi-species comparative framework. Population genetic analyses using 8-17 microsatellite markers per species (80 markers in total) as well as Bayesian inference were performed to estimate population structure and contemporary migration rates to highlight connectivity patterns and directionality of gene flow between depths. The results revealed three main genetic patterns depending on the species: a horizontal stepping stone pattern between areas around the island, a vertical stepping stone pattern between adjacent depths, and a quasi-panmictic pattern. Each species showed some specificity within these patterns, but overall, at least 4 of the 7 species support the assumption of vertical connectivity from the Deep Reef Refuge Hypothesis, highlighting the importance of studying multiple species. The existence of vertical connectivity between euphotic and mesophotic depths in the southwestern Indian Ocean confirms the importance of mesophotic coral ecosystems for conservation efforts and our global understanding of coral reef ecosystem dynamics.

Animals↗