Biology subjects

Peres da Silva, R.

Publications and source records attributed to Peres da Silva, R..

5 recordsLinked to original sources

bioRxiv2024

Direct high-throughput deconvolution of unnatural bases via nanopore sequencing and bootstrapped learning

The discovery of non-canonical bases (NCBs) in viruses and the development of synthetic xeno-nucleic acids (XNAs) to expand the genetic alphabet has spawned interest in many applications, from viral genomics, to synthetic biology and DNA storage. However, the inability to read non-canonical bases in a direct, high-throughput manner has been a significant limitation to its study and applicability. Here we demonstrate that XNA templates containing non-canonical bases can be directly and robustly sequenced (>2.3 million reads/flowcell, similar to DNA controls) on a MinION sequencer from Oxford Nanopore Technologies to obtain signal data that is significantly distinct from DNA controls (median fold-change >6x). To enable training of machine learning models that deconvolve these signals and basecall non-canonical and canonical bases, we developed a framework to synthesize a complex pool of 1,024 NCB-containing oligonucleotides with diverse 6-mer sequence contexts and high purity (>90% NCB-insertion on average). Bootstrapped models to assist in data preparation, and data augmentation with spliced reads to provide high context diversity, enabled learning of a generalizable model to call canonical as well as non-canonical bases with high accuracy (>80%) and specificity (99%). These results highlight the versatility of nanopore sequencing as a platform for interrogating nucleic acids for viral genomic and xenobiology applications, and the potential to transform the study of genetic material beyond those that use canonical bases.

synthetic biology↗

bioRxiv2023

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes

BackgroundWith the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database. ResultsWe present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is >7x faster than MetaMaps and GeNet and >2x faster than MEGAN-LR and MMseqs2. ConclusionThis proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.

bioinformatics↗

bioRxiv2022

CRISPR-based tools for genetic manipulation in pathogenic Sporothrix species

Sporothrix brasiliensis is an emerging fungal pathogen frequently associated with zoonotic transmission of sporotrichosis. Although certain virulence factors have been proposed as potential sporotrichosis determinants, the scarcity of molecular tools for reverse genetics studies on Sporothrix has significantly impeded the dissection of mechanisms underlying the disease. Here, we demonstrate that PEG-mediated protoplast transformation is a powerful method for heterologous expression in S. brasiliensis, S. schenckii and S. chilensis. Combined with CRISPR/Cas9 gene editing, this transformation protocol allowed the deletion of the putative DHN-melanin synthase gene pks1, which is a proposed virulence factor of Sporothrix species. To improve in locus integration of deletion constructs, we deleted the KU80 homologue that is critical for non-homologous end-joining DNA repair. The use of S. brasiliensis {Delta}ku80 strains enhanced homologous-directed repair during transformation resulting in increased targeted gene deletion. In conclusion, our CRISPR/Cas9-based transformation protocol provides an efficient tool for targeted gene manipulation in Sporothrix species.

molecular biology↗

bioRxiv2020

TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings

Over the last decade, large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data, limitations in the number of data points available, and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning (MTL) techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). We describe a novel multi-task unsupervised domain adaptation method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared domain/task feature representations. TUGDAs ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of negative transfer for in vitro models (63% for drugs with limited data and 94% overall) compared to state-of-the-art methods. For domain adaptation to in vivo settings, TUGDA improved performance for 6 out of 12 drugs in patient-derived xenografts, and 7 out of 22 drugs in TCGA patient datasets, despite being trained in an unsupervised fashion. TUGDAs ability to avoid negative transfer thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. Availabilityhttps://github.com/CSB5/TUGDA

bioinformatics↗

bioRxiv2019

Extracellular vesicle-mediated RNA release in Histoplasma capsulatum

Eukaryotic cells, including fungi, release extracellular vesicles (EVs). These lipid bilayered compartments play essential roles in cellular communication and pathogenesis. EV composition is complex and includes proteins, glycans, pigments, and RNA. RNA classes with putative roles in pathogenesis have been described in EVs produced by fungi. Here we describe the RNA content in EVs produced by the G186AR and G217B strains of Histoplasma capsulatum, an important human fungal pathogen. A total of 124 mRNA were identified in both strains. In this set of RNA classes, 93 transcripts were enriched in EVs from the G217B strain, while 31 enriched in EVs produced by the G186AR strain. This result suggests that there are important strain-specific properties in the mRNA composition of fungal EVs. We also identified short fragments (25-40 long) that were strain-specific, with a greater number of them identified in EVs produced by the G217B strain. Remarkably, the most enriched processes were stress responses and translation. Half of these fragments aligned to the reverse strand of the transcript, suggesting the occurrence of miRNA-like molecules in fungal EVs. We also compared the transcriptome profiles of H. capsulatum with the RNA composition of EVs and no correlation was observed. Altogether, our study provided information about the RNA molecules present in H. capsulatum EVs, and the differences in composition between the G186AR and G217B strains. In addition, we showed that the correlation between the most expressed transcripts in the cell and their presence in the EVs, reinforcing the idea that the RNAs were directed to the EVs by a regulated mechanism.\n\nImportanceExtracellular vesicles (EVs) play important roles in cellular communication and pathogenesis. The RNA molecules in EVs have been implicated in a variety of processes. In pathogenic fungi, EV-associated RNA classes have recently been described; however, only a few studies describing the RNA in fungal EVs are available. An improved knowledge on EV-associated RNA will contribute to the understanding of their role during infection. In this study, we described the RNA content in EVs produced by two isolates of Histoplasma capsulatum. Our results add this important pathogen to the current short list of fungal species with the ability to use EVs for the extracellular release of RNA.

microbiology↗