Biology subjects

Braga, M. P.

Publications and source records attributed to Braga, M. P..

5 recordsLinked to original sources

bioRxiv2025

Discovering the unseen: a performance comparison of taxonomic classification methods under unknown DNA barcodes

O_LIDNA barcoding and metabarcoding have emerged as cost-efficient, standardized methods for characterizing local biodiversity. Based on the sequencing of a small targeted gene fragment, it is theoretically possible to identify a wide diversity of taxa by comparing them with reference sequence databases. However, a key challenge for accurate taxonomic classification is the incompleteness of such databases, leading to most query sequences lacking species-level matches. C_LIO_LIWhere species-level matches are missing, it may be possible to classify query sequences to a higher taxonomic level, such as genus or family, based on the similarity of related reference taxa. The challenge then lies in confidently recognizing whether the sequence belongs to an unobserved (here, "novel") taxon on a given taxonomic level. C_LIO_LIIn this study, we evaluated the performance and utility of several methods for taxonomic classification. Methods were assessed based on the classification accuracy of both observed and novel taxa, training time, space requirements, and run time. We did this for two cases: the COI barcode for arthropods, and the ITS barcode for fungi, with the latter representing an instance with substantially greater sequence similarity variation within classes. To test classification of novel taxa, we used well-curated datasets with partially distinct taxonomic distribution between the training and test set. Novel taxa occurred at all evaluated taxonomic levels, such as novel species in observed genera and novel genera in observed families. We further assessed the effect on performance when shifting from full-length barcodes to shorter sequences as generated through metabarcoding in the testing dataset. C_LIO_LIThis study sheds light on the strengths and limitations of different classification algorithms across varied ecological contexts and provides guidance for researchers in selecting suitable algorithms for DNA barcoding and metabarcoding applications. In particular, it demonstrates the supreme performance of phylogenetic placement methods such as EPA-ng for classification of arthropod COI barcodes, and composition-based classifiers such as SINTAX, RDP-NBC, and IDTAXA for fungal ITS. C_LI

bioinformatics↗

bioRxiv2023

TreePPL: A Universal Probabilistic Programming Language for Phylogenetics

We present TreePPL, a universal probabilistic programming language (PPL) designed for probabilistic modeling and inference in phylogenetics. In TreePPL, the model is expressed as a computer program, which can generate simulations from the model conditioned on some input data. Specialized inference machinery then uses this program to estimate the posterior probability distribution. The aim is to allow the user to focus on describing the model, and provide the inference machinery for free. The TreePPL modeling language is meant to be familiar to users of R or Python, and utilizes a functional programming style that facilitates the application of generic inference algorithms. The model program can be conveniently compiled and run from a Python or R environment, which can be used for pre-processing, feeding the model with the observed data, controlling and running the inference, and receiving and post-processing the output data. The inference machinery is generated by a compiler framework developed specifically for supporting domain-specific modeling and inference, the Miking CorePPL framework. It currently supports a range of inference strategies--including sequential Monte Carlo, Markov chain Monte Carlo, and combinations thereof--and is based on several recent innovations that are important for efficient PPL inference on phylogenetic models. It also allows advanced users to implement novel inference strategies for models described using TreePPL or other domain-specific modeling languages. We briefly describe the TreePPL modeling language and the Python environment, and give some examples of modeling and inference with TreePPL. The examples illustrate how TreePPL can be used to address a range of common problem types considered in statistical phylogenetics, from diversification and tree inference to complex trait evolution. A few major challenges remain to be addressed before the phylogenetic model space is adequately covered by efficient automatic inference techniques, but several of them are being addressed in ongoing work on TreePPL. We end the paper by discussing how probabilistic programming can facilitate further use of machine learning in addressing important challenges in statistical phylogenetics.

bioinformatics↗

bioRxiv2022

Evolution and diversification dynamics of butterflies

Butterflies are a diverse and charismatic insect group that are thought to have diversified via coevolution with plants and in response to dispersals following key geological events. These hypotheses have been poorly tested at the macroevolutionary scale because a comprehensive phylogenetic framework and datasets on global distributions and larval hosts of butterflies are lacking. We sequenced 391 genes from nearly 2,000 butterfly species to construct a new, phylogenomic tree of butterflies representing 92% of all genera and aggregated global distribution records and larval host datasets. We found that butterflies likely originated in what is now the Americas, [~]100 Ma, shortly before the Cretaceous Thermal Maximum, then crossed Beringia and diversified in the Paleotropics. The ancestor of modern butterflies likely fed on Fabaceae, and most extant families were present before the K/Pg extinction. The majority of butterfly dispersals occurred from the tropics (especially the Neotropics) to temperate zones, largely supporting a "cradle" pattern of diversification. Surprisingly, host breadth changes and shifts to novel host plants had only modest impacts.

evolutionary biology↗

bioRxiv2021

Evolution of butterfly-plant networks over time, as revealed by Bayesian inference of host repertoire

The study of herbivorous insects underpins much of the theory that concerns the evolution of species interactions. In particular, Pieridae butterflies and their host plants have served as a model system for studying evolutionary arms-races. To learn more about the coevolution of these two clades, we reconstructed ancestral ecological networks using stochastic mappings that were generated by a phylogenetic model of host-repertoire evolution. We then measured if, when, and how two ecologically important structural features of the ancestral networks (modularity and nestedness) evolved over time. Our study shows that as pierids gained new hosts and formed new modules, a subset of them retained or recolonized the ancestral host(s), preserving connectivity to the original modules. Together, host-range expansions and recolonizations promoted a phase transition in network structure. Our results demonstrate the power of combining network analysis with Bayesian inference of host-repertoire evolution to understand changes in complex species interactions over time.

ecology↗

bioRxiv2019

Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution

Intimate ecological interactions, such as those between parasites and their hosts, may persist over long time spans, coupling the evolutionary histories of the lineages involved. Most methods that reconstruct the coevolutionary history of such associations make the simplifying assumption that parasites have a single host. Many methods also focus on congruence between host and parasite phylogenies, using cospeciation as the null model. However, there is an increasing body of evidence suggesting that the host ranges of parasites are more complex: that host ranges often include more than one host and evolve via gains and losses of hosts rather than through cospeciation alone. Here, we develop a Bayesian approach for inferring coevolutionary history based on a model accommodating these complexities. Specifically, a parasite is assumed to have a host repertoire, which includes both potential hosts and one or more actual hosts. Over time, potential hosts can be added or lost, and potential hosts can develop into actual hosts or vice versa. Thus, host colonization is modeled as a two-step process, which may potentially be influenced by host relatedness or host traits. We first explore the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameters. We then use our approach, implemented in the program RevBayes, to infer the coevolutionary history between 34 Nymphalini butterfly species and 25 angiosperm families.

evolutionary biology↗