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bioRxiv2023

Estimating recent and historical effective population size of marine and freshwater sticklebacks

Effective population size (Ne) is a quantity of central importance in evolutionary biology and population genetics, but often notoriously challenging to estimate. Analyses of Ne are further complicated by the many interpretations of the concept and the alternative approaches to quantify Ne utilising different properties of the data. However, the alternative methods are also informative over different time scales, suggesting that a combination of approaches should allow piecing together the entire continuum of Ne, spanning from the recent to more distant past. To test this in practice, we inferred the Ne continuum for 45 populations of nine-spined sticklebacks (Pungitius pungitius) using whole-genome data with both LD- and coalescent-based methods. Our results show that marine populations exhibit the highest Ne values in contemporary, recent, and historical times, followed by coastal and freshwater populations. They also demonstrate the impact of both recent and historical gene flow on Ne estimates and show that simple summary statistics are informative in comprehending the events in the very recent past and aid in more accurate estimation of NeC, the contemporary Ne, as well as in reconstruction and interpretation of recent demographic histories. Although our sample size for large populations is limited, we found that GONE can provide reasonable Ne estimates. However, due to challenges in detecting subtle genetic drift in large populations, these estimates may represent the lower bound of Ne. Finally, we show that combining GONE and CurrentNe2, both sensitive to population structure, with MSMC2 provides a meaningful interpretation of Ne dynamics over time.

genomics↗

bioRxiv2023

Inference of Genomic Landscapes using Ordered Hidden Markov Models with Emission Densities (oHMMed)

BackgroundGenomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. ResultsWe apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. ConclusionsOur algorithms provide a biologically assumption-free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

bioinformatics↗

bioRxiv2023

On minimising tumoural growth under treatment resistance

AO_SCPLOWBSTRACTC_SCPLOWDrug resistance is a major challenge for curative cancer treatment, representing the main reason of death in patients. Evolutionary biology suggests pauses between treatment rounds as a way to delay or even avoid resistance emergence. Indeed, this approach has already shown promising preclinical and early clinical results, and stimulated the development of mathematical models for finding optimal treatment protocols. Due to their complexity, however, these models do not lend themself to a rigorous mathematical analysis, hence so far clinical recommendations generally relied on numerical simulations and ad-hoc heuristics. Here, we derive two mathematical models describing tumour growth under genetic and epigenetic treatment resistance, respectively, which are simple enough for a complete analytical investigation. First, we find key differences in response to treatment protocols between the two modes of resistance. Second, we identify the optimal treatment protocol which leads to the largest possible tumour shrinkage rate. Third, we fit the "epigenetic model" to previously published xenograft experiment data, finding excellent agreement, underscoring the biological validity of our approach. Finally, we use the fitted model to calculate the optimal treatment protocol for this specific experiment, which we demonstrate to cause curative treatment, making it superior to previous approaches which generally aimed at stabilising tumour burden. Overall, our approach underscores the usefulness of simple mathematical models and their analytical examination, and we anticipate our findings to guide future preclinical and, ultimately, clinical research in optimising treatment regimes.

cancer biology↗

bioRxiv2023

A Conserved Electrostatic Membrane-Binding Surface in Synaptotagmin-Like Proteins Revealed Using Molecular Phylogenetic Analysis and Homology Modeling

Protein structure prediction has emerged as a core technology for understanding biomolecules and their interactions. Here, we combine homology-based structure prediction with molecular phylogenetic analysis to study the evolution of electrostatic membrane binding among vertebrate synaptotagmin-like proteins (Slps). Slp family proteins play key roles in the membrane trafficking of large dense-core secretory vesicles. Our previous experimental and computational study found that the C2A domain of Slp-4 (also called granuphilin) binds with high affinity to anionic phospholipids in the cytoplasmic leaflet of the plasma membrane through a large positively charged protein surface centered on a cluster of phosphoinositide-binding lysine residues. Because this surface contributes greatly to Slp-4 C2A domain membrane binding, we hypothesized that the net charge on the surface might be evolutionarily conserved. To test this hypothesis, the known C2A sequences of Slp-4 among vertebrates were organized by class (from mammalia to pisces) using molecular phylogenetic analysis. Consensus sequences for each class were then identified and used to generate homology structures, from which Poisson-Boltzmann electrostatic potentials were calculated. For comparison, homology structures and electrostatic potentials were also calculated for the five human Slp protein family members. The results demonstrate that the charge on the membrane-binding surface is highly conserved throughout the evolution of Slp-4, and more highly conserved than many individual residues among the human Slp family paralogs. Such molecular phylogenetic-driven computational analysis can help to describe the evolution of electrostatic interactions between proteins and membranes which are crucial for their function. Impact statementThe interior surface of eukaryotic plasma membranes is negatively charged, and many proteins that bind to it have correspondingly evolved a positively charged face. Here, we use techniques from evolutionary biology and computational biophysics to study the conservation of this positively charged surface in an important protein family. We find that the overall surface charge is highly conserved, more so than individual amino acids, consistent with its important role in electrostatic interaction with the membrane.

bioinformatics↗

bioRxiv2023

Genetic variations in foraging habits and their developmental noise in Drosophila

Investigating the causes and consequences of niche partitioning in populations is a significant goal in ecology and evolutionary biology. Studies have examined genetic and environmentally induced variations in resource utility and their ecological implications. However, few have explored developmental noise or instability as a factor contributing to variation in resource utility. Here, we studied genetic variation, and developmental noise in foraging traits of Drosophila lutescens, a wild fruit fly. Using 70 iso-female lines from a single population, we observed two foraging behavior traits - locomotive activity and resource preferences - in an experimental "8"-shaped arena with two different fruit juices in each chamber. The mean locomotive speed and relative preference for orange juice over grape juice varied significantly among iso-female lines, indicating genetic variation in foraging behavior. Additionally, the degree of variation within iso-female lines also varied, showing relatively higher heritability. While the locomotive speed and resource preferences of each line did not correlate with each other, the strength of variation within iso-female lines for locomotive speed showed a significant correlation with that for resource preferences. This suggests that the degree of developmental noise in both locomotive activity and resource preferences is governed by a shared genetic basis. Consequently, developmental noise can contribute to increased phenotypic variation in resource utility within a population and may evolve through natural selection.

animal behavior and cognition↗

bioRxiv2023

Uncertainty propagation in matrix population models: gaps, importance, and guidelines

O_LIMatrix population models (MPMs), which describe the demographic behaviour of a population based on age or stage through discrete time, are popular in ecology, evolutionary biology, and conservation biology. MPMs provide a tool for guiding management decisions and can give insight into life history trade-offs, patterns of senescence, transient dynamics, and population trajectories. These models are parameterised with estimates of vital rates (e.g. survival and reproduction) and can have multiple layers of underlying statistical analyses, all of which introduce uncertainty. For accurate and transparent results, this uncertainty should be propagated through to quantities derived from the MPMs. However, full propagation is not always achieved, leading to omitted uncertainty, and negative consequences for the reliability of inferences drawn. C_LIO_LIWe summarised the state-of-the-art regarding vital rate uncertainty reporting and propagation, by reviewing papers using MPMs from 2010-2019. We then used reported uncertainties as the basis for a simulation study to explore the impact of uncertainty omission on inferences drawn from the analysis of MPMs. We simulated four scenarios of vital rate propagation and evaluated their impact on population growth rate estimates. C_LIO_LIAlthough around 78% of MPM papers report some kind of uncertainty in their findings, only half of those report uncertainty in all aspects. Additionally, only 31% of papers fully propagate uncertainty through to derived quantities. Our simulations demonstrate that, even under a median uncertainty level, incomplete propagation introduces bias. Omitting uncertainty may substantially alter conclusions, particularly for results showing small changes in population size. Biased conclusions were most common when uncertainty in the most influential vital rates for population growth were omitted. C_LIO_LIWe suggest comprehensive guidelines for reporting and propagating uncertainty in MPMs. Standardising methods and reporting will increase the reliability of MPMs and enhance the comparability of different models. These guidelines will improve the accuracy, transparency, and reliability of population projections, increasing our confidence in results that can inform conservation efforts, ultimately contributing to biodiversity preservation. C_LI

ecology↗

bioRxiv2023

Consideration of a liquid mutation accumulation experiment to measure mutation rates by successive serial dilution.

The mutation-accumulation (MA) experiment is a fixture of evolutionary biology, though it is laborious to perform. MA experiments typically take between months and years to acquire sufficient mutations to measure DNA mutation rates and mutation spectra. MA experiments for many organisms rely on colony formation on agar plates and repetitive streaking, an environment which at first glance appears somewhat contrived, a poor imitation of real environmental living conditions. We propose that a fully liquid-phase mutation-accumulation experiment may at times more accurately reflect the environment of an organism. We note also that whereas automation of streaking plates is a daunting prospect, automation of liquid handling and serial dilution is already commonplace. In principle, this type of MA experiment can be automated so as to reduce the human capital requirements of measuring mutation rates. We demonstrate that a liquid MA recapitulates the mutation rate estimated for MMR- E. coli in liquid LB culture vs. plate LB culture. We detect a modified mutation spectrum with a transition skew of 4:1 of A:T[->]G:C vs G:C[->]A:T mutations, highlighting the potential role of tautomerization as a DNA mutation mechanism. We also find that using a plate reader to measure OD600 as a proxy for cell growth to be incapable of measuring carrying capacity for MA lines burdened with many mutations.

microbiology↗

bioRxiv2023

Keywords to success: a practical guide to maximise the visibility and impact of academic papers

In a growing digital landscape, enhancing the discoverability and resonance of scientific articles is essential. Here, we offer ten recommendations to amplify the discoverability of studies in scientific databases. Particularly, we argue that the strategic use and placement of key terms in the title, abstract, and keyword sections can boost indexing and appeal. By surveying 237 journals in ecology and evolutionary biology, we found that current author guidelines may unintentionally limit article discoverability. Our survey of 5842 studies revealed that authors frequently exhaust abstract word limits -- particularly those capped under 250 words. This suggests that current guidelines may be overly restrictive and not optimised to increase the dissemination and discoverability of digital publications. Additionally, 91.9% of studies used redundant keywords in the title or abstract, undermining optimal indexing in databases. We encourage adopting structured abstracts to maximise the incorporation of key terms in titles, abstracts, and keywords. In addition, we encourage the relaxation of abstract and keyword limitations in journals with strict guidelines, and the inclusion of multilingual abstracts to broaden global accessibility. These evidence-based recommendations to editors are designed to improve article engagement and facilitate evidence synthesis, thereby aligning scientific publishing with the modern needs of academic research.

scientific communication and education↗

bioRxiv2023

GUANinE v0.9: Benchmark Datasets for Genomic AISequence-to-Function Models

Computational genomics increasingly relies on machine learning methods for genome interpretation, and the recent adoption of neural sequence-to-function models highlights the need for rigorous model specification and controlled evaluation, problems familiar to other fields of AI. Research strategies that have greatly benefited other fields -- including benchmarking, auditing, and algorithmic fairness -- are also needed to advance the field of genomic AI and to facilitate model development. Here we propose a genomic AI benchmark, GUANinE, for evaluating model generalization across a number of distinct genomic tasks. Compared to existing task formulations in computational genomics, GUANinE is large-scale, de-noised, and suitable for evaluating pretrained models. GUANinE v1.0 primarily focuses on functional genomics tasks such as functional element annotation and gene expression prediction, and it also draws upon connections to evolutionary biology through sequence conservation tasks. The current GUANinE tasks provide insight into the performance of existing genomic AI models and non-neural baselines, with opportunities to be refined, revisited, and broadened as the field matures. Finally, the GUANinE benchmark allows us to evaluate new self-supervised T5 models and explore the tradeoffs between tokenization and model performance, while showcasing the potential for self-supervision to complement existing pretraining procedures.

genomics↗

bioRxiv2023

Using the pan-genomic framework for the discovery of genomic islands in the haloarchaeon Halorubrum ezzemoulense

In this study, we use pan-genomics to characterize the organized variability from the widely dispersed halophilic archaeal species Halorubrum ezzemoulense. We include a multi-regional sampling of newly sequenced, high-quality draft genomes. Using the pan-genome graph of the species, we discover 50 genomic islands which represent rare accessory genetic capabilities available to members of the species. 19 of these islands are likely the remnant of mobile genetic elements and are enriched for genomic dark matter. 10 islands encode for niche adapting solute transporters, with a cosmopolitan but limited distribution throughout the strains. We also observe rearrangements which have led to the insertion/recombination/replacement of mutually exclusive genomic islands in equivalent genome positions ("homeocassettes"). These conflicting islands encode for similar functions, but homologs from islands located between the same core genes exhibit extreme divergence on the amino acid level. Homeocasettes provide variations for a homologous function, which may confer a greater range of adaptability to the species group. We observe some islands that appear geographically restricted; however, we also observe the coexistence of genomes, in a single geographic region, with and without certain genomic islands, demonstrating the retention and spread of rare genes in the pan-genome. ImportanceUnderstanding the evolution of genome content is a key puzzle in evolutionary biology. Despite its importance, this area hasnt received thorough investigation. This is especially true of Archaeal organisms, which constitute a large fraction of Earths diversity, but are often referred to as the "forgotten" or "third" domain of life. This study dives into those questions by finding rare genes amongst a group of closely related Archaeal species, and describes how their transfer, utilization and persistence may contribute to the speciation and specialization of the group.

microbiology↗

bioRxiv2023

Dietary restriction and life-history trade-offs: insights into mTOR pathway regulation and reproductive investment in birds

Resources are needed for growth, reproduction and survival, and organisms must trade-off limited resources among competing processes. Nutritional availability in organisms is sensed and monitored by nutrient-sensing pathways that can trigger physiological changes or alter gene expression. Previous studies have proposed that one such signalling pathway, the mechanistic target of rapamycin (mTOR), underpins a form of adaptive plasticity when individuals encounter constraints in their energy budget. Despite the fundamental importance of this process in evolutionary biology, how nutritional limitation is regulated through the expression of genes governing this pathway and its consequential effects on fitness remains understudied, particularly in birds. We used dietary restriction to simulate resource depletion and examined its effects on body mass, reproduction and gene expression in Japanese quails (Coturnix japonica). Quails were subjected to ad libitum (ADL) feeding or 20%, 30%, and 40% restriction levels for two weeks. All restricted groups exhibited reduced body mass, whereas reductions in the number and mass of eggs were observed only under more severe restrictions. Dietary restriction led to decreased expression of mTOR and insulin-like growth factor 1 (IGF1), whereas the ribosomal protein S6 kinase 1 (RPS6K1) and autophagy-related genes (ATG9A and ATG5) were upregulated. The pattern in which mTOR respond to restriction was similar to what has been seen in body mass. Regardless of the treatment, proportionally higher reproductive investment was associated with individual variation in mTOR expression. These findings reveal the connection between dietary intake and the expression of mTOR and related genes in this pathway.

molecular biology↗

bioRxiv2023

A PhyloFisher utility for nucleotide-based phylogenomic matrix construction; nucl_matrix_constructor.py

Phylogenies built from multiple genes have become a common component of evolutionary biology studies. Molecular phylogenomic matrices used to build multi-gene phylogenies can be built from either nucleotide or protein matrices. Nucleotide-based analyses are often more appropriate for addressing phylogenetic questions in evolutionarily shallow timescales (i.e., less than 100 million years) while protein-based analyses are often more appropriate for addressing deep phylogenetic questions. PhyloFisher is a phylogenomic software package written in Python3. The manually curated PhyloFisher database contains 240 protein-coding genes from 304 eukaryotic taxa. Here we present nucl_matrix_constructor.py, an expansion of the PhyloFisher starting database, and an update to PhyloFisher that maintains DNA sequences. This combination will allow users the ability to easily build nucleotide phylogenomic matrices while retaining the benefits of protein-based pre-processing used to identify contaminants and paralogy.

bioinformatics↗

bioRxiv2023

RCPedia: A global resource for studying and exploring retrocopies in diverse species

MotivationGene retrocopies, or processed pseudogenes, arise from the reverse transcription and genomic insertion of processed mRNA transcripts. These elements have significantly contributed to genetic diversity and novelties throughout the evolution of many species. However, the study of retrocopies has been challenging, owing to the absence of comprehensive, complete, and user-friendly databases for diverse species. ResultsHere, we introduce an improved version of RCPedia, an integrative database meticulously designed for the study of retrocopies. RCPedia offers an extensive catalog of retrocopies identified across 44 species, which includes 13 primates, 4 rodents, 6 chiropterans, 12 other mammals, 4 birds, turtle, lizard, frog, zebrafish, and drosophila. The database offers the most complete compilation of retrocopies per species, accompanied by detailed genomic annotations, expression data, and links to other data portals. Furthermore, RCPedia features a streamlined representation of data and an efficient querying system, establishing it as an invaluable tool for researchers in the fields of genomics, evolutionary biology and transposable elements. In summary, RCPedia aims to enhance the investigation of retrocopies and their pivotal roles in shaping the genomic landscapes of diverse species. AvailabilityRCPedia is available at https://www.rcpediadb.org

bioinformatics↗

bioRxiv2024

Phylogenomics analyses of all species of Swordtails (Genus Xiphophorus) highlights hybridization precedes speciation

Hybridization has been recognized as an important driving force for evolution, however studies of the genetic consequence and its cause are still lagging behind in vertebrates due to the lack of appropriate experimental systems. Fish of the central American genus Xiphophorus were proposed to have evolved with multiple ancient and ongoing hybridization events, and served as a valuable research model in evolutionary biology and in biomedical research on human disease for more than a century. Here, we provide the complete genome resource and its annotation of all 26 Xiphophorus species. On this dataset we resolved the so far conflicting phylogeny. Through comparative genomic analyses we investigated the molecular evolution of genes related to melanoma, for a main sexually selected trait and for the genetic control of puberty timing, which are predicted to be involved in pre-and postzygotic isolation and thus to influence the probability of interspecific hybridization in Xiphophorus. We demonstrate dramatic size-variation of some gene families across species, despite the reticulate evolution and short divergence time. Finally, we clarify the hybridization history in the genus Xiphophorus genus, settle the long dispute on the hybridization origin of two Southern swordtails, highlight hybridizations precedes speciation, and reveal the distribution of hybridization ancestry remaining in the fused genome.

genomics↗

bioRxiv2024

Genotype sampling for deep-learning assisted experimental mapping of fitness landscapes

MotivationExperimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitness needs to be measured for any one protein. Deep learning may help to predict the fitness of many genotypes from a smaller neural network training sample of genotypes with experimentally measured fitness. Here I use a recently published experimentally mapped fitness landscape of more than 260,000 protein genotypes to ask how such sampling is best performed. ResultsI show that multilayer perceptrons, recurrent neural networks (RNNs), convolutional networks, and transformers, can explain more than 90 percent of fitness variance in the data. In addition, 90 percent of this performance is reached with a training sample comprising merely {approx}103 sequences. Generalization to unseen test data is best when training data is sampled randomly and uniformly, or sampled to minimize the number of synonymous sequences. In contrast, sampling to maximize sequence diversity or codon usage bias reduces performance substantially. These observations hold for more than one network architecture. Simple sampling strategies may perform best when training deep learning neural networks to map fitness landscapes from experimental data.

bioinformatics↗

bioRxiv2024

Can't carve air, can't weave names: Plant species used in Colombian artisan crafts are not equally assessed for vulnerability at the international and national level

Humanity has maintained cultural connections with our environments for time immemorial. Plants and artisan crafts are a prime example, as craft purpose, skill, design, and species used can vary greatly between communities and the loss of a critical plant species can result in a loss of access to cultural craft practices. To mitigate global biodiversity loss, conservationists are faced with the challenge of assessing species vulnerability to extinction and prioritizing species for conservation funding using information instruments, like the IUCN red list. This process does not necessarily consider a species cultural importance. In this paper we sought to address this gap for plant species used in artisan crafts in Colombia. We aim to answer the following: (1) how represented are endemic species in artisan crafts; (2) how threatened are artisan craft species according to (a) international and (b) national vulnerability status? We used the number of species-associated common names as a proxy for cultural awareness. We found that continentally regional species were far more represented in Colombian artisan crafts than national endemics. We also found a strong positive relationship between number of common names and national vulnerability assessment status, but no statistically significant relationship for international vulnerability status. Based on our results, well-known plants used in Colombian artisan crafts are more likely to be assessed nationally than internationally. While the IUCN is thorough in their recommendations, more can be done to prioritize the inclusion of conservation assessments for species based on their contributions to cultural diversity. Positionality statementWe are 14 scientists and practitioners who are deeply committed to the conservation of nature and culture in a changing world. We are trained in diverse fields including ecology, evolutionary biology, botany, music, anthropology, law, and public health. We all have postgraduate academic education (Masters or PhDs underway) but most of us are early career scholars. Six of us grew up in Colombia and we represent many places including Mexico, the United States, Ireland, Chile, Brazil, Germany, and Viet Nam. None of us self-identify as Indigenous.

ecology↗

bioRxiv2024

EyeHex toolbox for complete segmentation of ommatidia in fruit fly eyes

Variation in Drosophila compound eye size is studied across research fields, from evolutionary biology to biomedical studies, requiring the collection of large datasets to ensure robust statistical analyses. To address this, we present EyeHex, a tool for automatic segmentation of fruit fly compound eyes from brightfield and scanning electron microscopy (SEM) images. EyeHex features two integrated modules: the first utilizes machine learning to generate probability maps of the eye and ommatidia locations, while the second, a hard-coded module, leverages the hexagonal organization of the compound eye to map individual ommatidia. This iterative segmentation process, which adds one ommatidium at a time based on registered neighbors, ensures robustness to local perturbations. EyeHex also includes an analysis tool that calculates key metrics of the eye, such as ommatidia count and diameter distribution across the eye. With minimal user input for training and application, EyeHex achieves exceptional accuracy (>99.6% compared to manual counts on SEM images) and adapts to different fly strains, species, and image types. EyeHex offers a cost-effective, rapid, and flexible pipeline for extracting detailed statistical data on Drosophila compound eye variation, making it a valuable resource for high-throughput studies.

developmental biology↗

bioRxiv2024

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences, from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-sectional, longitudinal, and phylogenetic data, which may have no, uncertain, or precisely specified sampling times. HyperTraPS-CT allows positive and negative interactions between arbitrary subsets of features (not limited to pairwise interactions), supporting Bayesian and maximum-likelihood inference approaches to identify these interactions, consequent pathways, and predictions of future and unobserved features. We also introduce a range of visualisations for the inferred outputs of these processes and demonstrate model selection and regularisation for feature interactions. We apply this approach to case studies on the accumulation of mutations in cancer progression and the acquisition of anti-microbial resistance genes in tuberculosis, demonstrating its flexibility and capacity to produce predictions aligned with applied priorities.

bioinformatics↗