Biology subjects

Paschoal, A. R.

Publications and source records attributed to Paschoal, A. R..

12 recordsLinked to original sources

bioRxiv2026

Comparative chromosome-scale genomics of the ayahuasca liana Banisteriopsis caapi reveals paleopolyploid ancestry and recent tandem-gene expansions as candidate signatures of ethnovariety differentiation

Background: Banisteriopsis caapi is the Amazonian liana that provides the {beta}-carboline-rich vine component of ayahuasca, a traditional Amazonian psychoactive preparation used in Indigenous and ceremonial contexts. The species encompasses culturally recognized ethnovarieties, some differing in stem architecture. Despite its cultural, ecological, and pharmacological importance, B. caapi remains largely unexplored at the genomic level. Here, we generated chromosome-scale genome assemblies for the caupuri and tucunaca ethnovarieties and used them to investigate genome evolution, structural variation, repeat composition, and candidate genomic features associated with ethnovariety-level differentiation. Results: Caupuri and tucunaca assemblies span 1.16 and 1.13 Gb, respectively, each organized into ten high-quality chromosome-scale pseudomolecules. Both genomes retain signatures of two ancient post-gamma duplication components, with related signals also detected in Malpighia emarginata, suggesting that at least part of this paleopolyploid history may predate the divergence of these Malpighiaceae lineages. Against this conserved chromosomal framework, the sampled caupuri and tucunaca genomes differ in structural variation, repeat composition, and gene-family profiles. Transposable element-associated insertions and deletions dominate the structural-variant landscape, while both genomes are highly repetitive, with approximately 78% of their assemblies composed of repetitive DNA largely shaped by LTR retrotransposons, including Gypsy/Retand and Copia/Ale, Copia/Tork, and Copia/Ivana lineages, together with non-autonomous large retrotransposon derivatives (LARDs). Most genes belong to shared orthogroups, indicating a largely conserved gene repertoire. However, candidate ethnovariety-biased differences emerge from the combined remodeling of gene families, LARD and LTR-associated genomic neighborhoods, and comparatively recent local expansions of tandem-duplicated gene arrays encoding signaling receptors, transporters, hormone-associated genes, cell wall and secondary wall functions, and meristem or organ-development pathways. Conclusions: These results indicate that the sampled caupuri and tucunaca genomes differ mainly through localized remodeling of shared genomic modules, including comparatively recent repeat turnover and local tandem-gene expansions. These assemblies provide the first large-scale nuclear genomic resource for B. caapi and a foundation for investigating paleopolyploid evolution in Malpighiaceae and candidate mechanisms of ethnovariety diversification.

genomics↗

bioRxiv2026

PanTEon: a cross-kingdom framework to guide the design of transposable element classifiers

Transposable elements (TEs) are major drivers of genome evolution, yet their annotation and classification remain inconsistent and hard to reproduce across species. Fragmented repeats, lineage-specific innovations, and heterogeneous taxonomies across databases and tools complicate comparisons and slow progress in TE biology. To address this, we developed PanTEon, a cross-kingdom deep learning framework for reproducible TE classification that combines a harmonized database with an open, modular benchmarking platform. The PanTEon Database is an automatically curated, taxonomically broad TE repository spanning animals, plants, and fungi. The PanTEon platform standardizes training, evaluation, and inference across nine Machine Learning methods, while remaining extensible to user-defined architectures. Using this framework, we benchmark state-of-the-art Machine Learning-based TE classifiers across TE superfamilies and major eukaryotic lineages and find that performance varies markedly by kingdom and superfamily. Ensemble approaches and phylum-specific models improve predictive F1 scores, but cross-species generalization remains a major challenge. Together, PanTEon Database and PanTEon platform provide a reproducible, scalable, and extensible foundation for TE classification, enabling standardized evaluation of future AI methods and supporting community-driven annotation efforts.

bioinformatics↗

bioRxiv2025

Protocol for GDT, Gene Dictionary Tool, to create and implement a gene dictionary across annotated genomes

The Gene Dictionary Tool is a protocol for the creation and implementation of gene dictionaries across annotated genomes. Through a Python CLI (Command Line Interface), the user identifies what annotations are missing in the inputted dictionary. Via two Jupyter Notebooks, the user builds a gene dictionary based on attributes retrieved from inputted GFF3 files. The final output, a .gdict file, is Findable, Accessible, Interoperable, and Reusable (FAIR). We showcase GDTs functionalities by creating a gene dictionary for fungal mitochondrial genomes.

bioinformatics↗

bioRxiv2024

Generative Approaches for Nucleotide Sequences to Enhance Non-coding RNA Classification

Classifying non-coding RNA (ncRNA) sequences, particularly mirtrons, is essential for elucidating gene regulation mechanisms. However, the prevalent class imbalance in ncRNA datasets presents significant challenges, often resulting in overfitting and diminished generalization in machine learning models. In this study, GENerative Approaches for NUcleotide Sequences (GENNUS) is proposed, introducing novel data augmentation strategies using Generative Adversarial Networks (GANs) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance ncRNA classification performance. Our GAN-based methods effectively generate high-quality synthetic data that capture the intricate patterns and diversity of real mirtron sequences, eliminating the need for extensive feature engineering. Through four experiments, it is demonstrated that models trained on a combination of real and GAN-generated data improve classification accuracy compared to traditional SMOTE techniques or only with real data. Our findings reveal that GANs enhance model performance and provide a richer representation of minority classes, thus improving generalization capabilities across various machine learning frameworks. This work highlights the transformative potential of synthetic data generation in addressing data limitations in genomics, offering a pathway for more effective and scalable ncRNA classification methodologies.

bioinformatics↗

bioRxiv2024

CODARFE: Unlocking the prediction of continuous environmental variables based on microbiome

Despite the surge in data acquisition, there is a limited availability of tools capable of effectively analyzing microbiome data that identify correlations between taxonomic compositions and continuous environmental factors. Furthermore, existing tools also do not predict the environmental factors in new samples, underscoring the pressing need for innovative solutions to enhance our understanding of microbiome dynamics and fulfill the prediction gap. Here, we introduce CODARFE, a novel tool for sparse compositional microbiome-predictors selection and prediction of continuous environmental factors. We tested CODARFE against four state-of-the-art tools in two experiments. First, CODARFE outperformed predictor selection in 21 out of 24 databases in terms of correlation. Second, among all the tools, CODARFE achieved the highest number of previously identified bacteria linked to environmental factors for human data--that is, at least 7% more. We also tested CODARFE in a cross-study, using the same biome but under different external effects (e.g., ginseng field and cattle for arable soil, and HIV and crohns disease for human gut), using a model trained on one dataset to predict environmental factors on another dataset, achieving 11% of mean absolute percentage error. Finally, CODARFE is available in five formats, including a Windows version with a graphical interface, to installable source code for Linux servers and an embedded Jupyter notebook available at MGnify - https://github.com/alerpaschoal/CODARFE.

bioinformatics↗

bioRxiv2024

DeepSEA: an alignment-free deep learning tool for functional annotation of antimicrobial resistance proteins

Antimicrobial resistance (AMR) is one of the most concerning modern threats as it places a greater burden on health systems than HIV and malaria combined. Current surveillance strategies for tracking antimicrobial resistance (AMR) rely on genomic comparisons and depend on sequence alignment with strict similarity cutoffs of greater than 95%. Therefore, these methods have high false-negative error rates due to a lack of reference sequences with a representative coverage of AMR protein diversity. Deep learning (DL) has been used as an alternative to sequence alignment, as artificial neural networks (ANNs) can extract abstract features from data and therefore limit the need for sequence comparisons. Here, a convolutional neural network (CNN) was trained to differentiate antimicrobial resistance proteins from nonresistance proteins and functionally annotate them in nine resistance classes. Our model demonstrated higher recall values (>0.9) than the alignment-based approach for all protein classes tested. Additionally, our CNN architecture allowed us to investigate internal states and explain the model classification regarding protein domain feature importance related to antimicrobial molecule inactivation. Finally, we built an open-source bioinformatic tool that can be used to annotate antimicrobial resistance proteins and provide information on protein domains without sequence alignment.

bioinformatics↗

bioRxiv2024

Classification of LTR Retrotransposons via Interaction Prediction

Transposable Elements (TEs) are genetic sequences that can relocate within the genome, thus promoting genetic diversity. Classifying TEs in eukaryotes involves a hierarchy formed by classes, subclasses, orders, superfamilies, families, and subfamilies. According to this taxonomy, LTR retrotransposons (LTR-RT) constitute an order. The primary objective of this study is to explore the classification of LTR retrotransposons at the superfamily level. This was achieved by predicting interactions between LTR-RT sequences and conserved protein domains using Predictive Bi-Clustering Trees (PBCTs). Two datasets were used to investigate the relationships among different superfamilies. The first one comprised LTR retrotransposon sequences assigned to Copia, Gypsy, and Bel-Pao superfamilies, whereas the second dataset included consensus sequences of the conserved domains for each superfamily. Therefore, the PBCT decision tree tests could relate to both sequence and class attributes. In the classification process, interaction is interpreted as either the presence or absence of a domain in a given LTR-RT sequence. Subsequently, this sequence is classified into the superfamily with the highest number of predicted domains. Precision-recall curves were adopted as evaluation metrics for the method, and its performance was compared to some of the most commonly used models in the task of transposable element classification. Experiments on D. melanogaster and A. thaliana showed that PBCTs are promising and comparable to other methods, especially in the classification of the Gypsy superfamily.

bioinformatics↗

bioRxiv2023

Genomic decoding of Theobroma grandiflorum (cupuassu) at chromosomal scale: Evolutionary insights for horticultural innovation

BackgroundTheobroma grandiflorum (Malvaceae), known as cupuassu, is a tree indigenous to the Amazon Basin, valued for its large fruits and seed-pulp, contributing notably to the Amazonian bioeconomy. The seed-pulp is utilized in desserts and beverages, and its seed butter is used in cosmetics. Here, we present the sequenced telomere-to-telomere cupuassu genome, disclosing features of the genomic structure, evolution, and phylogenetic relationships within the Malvaceae. ResultsThe cupuassu genome spans 423 Mb, encodes 31,381 genes distributed in the ten chromosomes, and it exhibits approximately 65% gene synteny with the T. cacao genome, reflecting a conserved evolutionary history, albeit punctuated with unique genomic variations. The main changes are pronounced by bursts of long-terminal repeats retrotransposons expansion at post-species divergence, retrocopied and singleton genes, and gene families displaying distinctive patterns of expansion and contraction. Furthermore, positively selected genes are evident, particularly among retained and dispersed, tandem and proximal duplicated genes associated to general fruit and seed traits and defense mechanisms, supporting the hypothesis of potential episodes of subfunctionalization and neofunctionalization following duplication, and impact from distinct domestication process. These genomic variations may underpin the differences observed in fruit and seed morphology, ripening, and disease resistance between cupuassu and the other Malvaceae species. ConclusionsSequencing the cupuassu genome offers a foundational resource for both breeding and conservation efforts, yielding insights into the evolution and diversity within the genus Theobroma. Core ideasO_LITelomere-to-telomere sequencing of the Theobroma grandiflorum genome elucidates a 65% synteny with T. cacao. C_LIO_LIRetrotransposon expansion identified as a pivotal factor in post-divergence genomic evolution between Theobroma species. C_LIO_LIComparative genomics has revealed genes associated with key agronomic traits, providing evolutionary insights. C_LIO_LIPositive selection pressure in retained duplicated genes implicated in adaptive functions and fruit-seed traits diversity. C_LIO_LICupuassu genome as a genetic resource for breeding and to boosts Brazillian Amazonian bioeconomy. C_LI AUTHOR SUMMARYCupuassu, a fruit-bearing tree from the Amazon, is prized for its nutritious fruits and seeds, widely used in food and cosmetics. In this study, we sequenced the complete genome of cupuassu to understand its development, unique traits, and genetic relationship with cacao and other related species. The cupuassu genome shares a high similarity with cacao, but it also exhibits distinctive features. Notably, repetitive DNA elements have significantly influenced its genomic structure. Furthermore, specific genes responsible for its fruit and seed characteristics, as well as disease resistance, were identified. Overall, this research not only deepens our knowledge of cupuassu genetics but also illuminates broader aspects of plant evolution and diversity in the Amazon. It lays the groundwork for advanced breeding programs and promises to contribute significantly to the Amazonian bioeconomy. Ultimately, these findings have important implications for agriculture and conservation, highlighting the intricate processes of plant adaptation and evolution.

genomics↗

bioRxiv2023

The genome and population genomics of allopolyploid Coffea arabica reveal the diversification history of modern coffee cultivars

Coffea arabica, an allotetraploid hybrid of C. eugenioides and C. canephora, is the source of approximately 60% of coffee products worldwide, and its cultivated accessions have undergone several population bottlenecks. We present chromosome-level assemblies of a di-haploid C. arabica accession and modern representatives of its diploid progenitors, C. eugenioides and C. canephora. The three species exhibit largely conserved genome structures between diploid parents and descendant subgenomes, with no obvious global subgenome dominance. We find evidence for a founding polyploidy event 350,000-610,000 years ago, followed by several pre-domestication bottlenecks, resulting in narrow genetic variation. A split between wild accessions and cultivar progenitors occurred [~]30.5 kya, followed by a period of migration between the two populations. Analysis of modern varieties, including lines historically introgressed with C. canephora, highlights their breeding histories and loci that may contribute to pathogen resistance, laying the groundwork for future genomics-based breeding of C. arabica.

genomics↗

bioRxiv2022

A long non-coding RNA is a key factor in the evolution of insect eusociality

Insect sociality is a major evolutionary transition based on the suppression of worker reproduction in favor of the reproductive monopoly of the queen. In the honey bee (Apis mellifera) model organism, the development of the two female caste phenotypes, queen and worker, is triggered by differences in their larval diets. However, the mechanistic details underlying their respective developmental trajectories, as well as the maintenance of sterility in the adult workers, are still not fully understood. Here we show that the long non-coding RNA lncov1 interacts with the Tudor staphylococcus nuclease (Tudor-SN) protein to form a regulatory module that promotes apoptosis in the ovaries of worker larvae. In adult workers, the lncov1/Tudor-SN module responds positively to environmental cues that suppress reproductive capacity. As lncov1 is considerably conserved in the Apidae, we propose that, by promoting worker sterility, the lncov1/Tudor-SN module has likely played critical roles in the social evolution of bees.

evolutionary biology↗

bioRxiv2022

Impact of sequencing technologies on long non-coding RNA computational identification

The correct annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs), is still an important critial challenge in genome analyses. One crucial issue in lncRNA transcript annotation is the transcriptome resource that supports lncRNA loci. Long-read technologies now bring the potential to improve the quality of transcriptome annotation. Consequently, long non-coding RNAs (lncRNA) are probably the most benefited class of transcripts that would have improved annotation using this novel technology. However, there is a gap regarding benchmarking studies that highlighted if the direct use of lncRNA predictors in long-reads makes more precise identification of these transcripts. Considering that these lncRNA tools were not trained with these reads, we want to address: how is the performance of these tools? Are they also able to efficiently identify lncRNAs? We could provide evidence of where and how to make potential better approaches for the lncRNA annotation by understanding these issues. Keywords: Non-coding RNAs, high-throughput sequencing technologies, coding, methods, benchmarking, tools, NGS, transcripts

bioinformatics↗

bioRxiv2020

TERL: Classification of Transposable Elements by Convolutional Neural Networks

Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. They are capable of transpose and generate multiple copies of themselves throughout genomes. These sequences can produce a variety of effects on organisms, such as regulation of gene expression. There are several types of these elements, which are classified in a hierarchical way into classes, subclasses, orders and superfamilies. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology based search, which could be inefficient for classifying non-homologous sequences. Here we propose a pipeline, transposable elements representation learner (TERL), that use four preprocessing steps, a transformation of one-dimensional nucleic acid sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks (CNNs). CNN is used to classify TE sequences because it is a very flexible classification method, given it can be easily retrained to classify different categories and any other DNA sequences. This classification method tries to learn the best representation of the input data to correctly classify it. CNNs can also be accelerated via GPUs to provide fast results. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for the superfamily sequences from RepBase and 95.7% and 91.5% for the order sequences from RepBase respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. We also show a way to preprocess sequences and prepare train and test sets. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system, is on average 162 times and four orders of magnitude faster than TEclass and PASTEC respectively and on a real-world scenario obtained better accuracy, recall, and specificity than the other methods.

bioinformatics↗