Biology subjects

Cerri, R.

Publications and source records attributed to Cerri, R..

4 recordsLinked to original sources

bioRxiv2024

Transposable Elements and piRNAs interaction prediction with Predictive Bi-Clustering Trees

PIWI-interacting RNAs (piRNAs) are a class of noncoding RNAs whose actions range from regulating gene expression to silencing Transposable Elements, characterized for being from 21 to 35 nucleotides long, displaying a uracil bias at the 5 end, and a 2-O-methylation at the 3 end. Transposable Elements (TEs) are genetic elements that move within host genomes. TE replication can promote harmful recombination events by generating breaks in DNA double strands, in addition to interfering with expression. Silencing of these elements by piRNAs occurs in the germ line in most animals and is essential for maintaining genome integrity. In this work, the problem of in silico interaction prediction between piRNAs and TEs was addressed by a decision tree-based algorithm, namely Predictive Bi-Clustering Trees (PBCT). In order to improve the algorithms performance, the piRNA-TE interaction matrix was reconstructed using a Beta-distribution-rescored Neighborhood Regularized Logistic Matrix Factorization (NRLMF{beta}) algorithm. PBCT was tested in 5-fold and 10-fold cross-validation configurations, both with the original interaction matrix (BICT) and the interaction matrix reconstructed by NRLMF{beta} (BICTR). Although not being able to predict positive interactions satisfactorily given the huge dataset imbalance, advantages could be observed when using matrix factorization. Comparatively, in the BICT method, PBCT presented higher values of AUROC and AUPRC. However, in the BICTR method, PBCT was able to correctly predict more positive interactions, which are, in fact, the primary interest of this study. Potential biological applications and ways to improve the algorithms performance were also discussed. Author summarypiRNAs and transposable elements are biomolecules that interact in the germ lime in most animals, such that piRNAs silence these elements to keep genome integrity. However, detecting which piRNA interacts with which TE is a laborious task with low results, given that the rules that govern these interactions still need to be fully elicited. In this paper, we addressed the interaction prediction pair piRNA-TE using a multi-label decision-tree-like algorithm called PBCT applied to in vivo known interactions. Given that it is a Positive-Unlabeled Learning problem, since we cannot be sure of a biological negative interaction, we reconstructed the interaction matrix employing an NRLMF{beta} algorithm. We compared the results given the original interaction matrix and the reconstructed matrix. The results with this algorithm and parameters could have been better, even though the reconstruction has proven fruitful. Further, we addressed our problem with other multi-label learning approaches and briefly compared them. We also discussed potential biological applications and ways to improve the algorithms performance.

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↗

bioRxiv2021

Draft genome of the Brazilian railroad worm Phrixothrix hirtus E.Olivier (Phengodidae: Coleoptera)

The Neotropical region is the richest in bioluminescent Coleoptera species, however, its bioluminescence megadiversity is still underexplored in terms of genomic organization and evolution, mainly within the Phengodidae family. The railroad worm Phrixothrix hirtus is an important biological model and symbolic species due to its bicolor bioluminescence, being the only organism that produces true red light among bioluminescent terrestrial species. Here, we performed the partial genome assembly of P. hirtus, combining short and long reads generated with Illumina sequencing, providing an important source of genomic information and a framework for comparative genomic analyses for the evaluation of the bioluminescent system in Elateroidea. The estimated genome size has [~]3.4Gb, 32% of GC content, and 67% of repetitive elements, being the largest genome described in the Elateroidea superfamily. Several events of gene family expansions associated with anatomical development and morphogenesis, as well as distinct odorant-binding receptors and retrotransposable elements were found in this genome. Similar molecular functions and biological processes are shared with other studied species of Elateriformia. Common genes putatively associated with bioluminescence production and control, including two luciferase genes that displayed 7 exons and 6 introns, and genes that could be involved in luciferin biosynthesis were found, indicating that there are no clear differences about the presence or absence of gene families associated with bioluminescence in Elateroidea. In P. hirtus the conversion of L- to D-luciferin seems to involve additional steps using a Palmitoyl-CoA thioesterase instead of an Acyl-CoA synthetase, which was found in Lampyridae species. HighlightsO_LIFirst draft genome assembly of Phengodidae, the largest one described in Coleoptera; C_LIO_LIGene family expansions associated with anatomical development and morphogenesis; C_LIO_LIBioluminescent control and luciferin biosynthesis genes are common within Elateroidea; C_LIO_LIDespite similar bioluminescent system, metabolic routes may have evolved independently; C_LI

genomics↗

bioRxiv2020

Investigating Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

PIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

bioinformatics↗