Biology subjects

Domingues, D. S.

Publications and source records attributed to Domingues, D. S..

4 recordsLinked to original sources

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↗

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↗