Biology subjects

de Souza, S. J.

Publications and source records attributed to de Souza, S. J..

7 recordsLinked to original sources

bioRxiv2026

From Data Curation to Risk Reporting: A Pipeline for Polygenic Risk Scores

Polygenic risk scores (PRS) have emerged as a powerful tool for quantifying genetic susceptibility to complex traits and diseases. However, their calculation and interpretation require standardized data curation, robust statistical methods, and clear reporting strategies. In this work, we present an integrated pipeline designed to address these challenges. The pipeline begins with the construction of a curated genotype/phenotype database derived from public repositories, ensuring that only phenotypes with appropriate metadata, statistical distributions, and ethical suitability are retained. The final dataset comprises 2,346 phenotypes covering 38,256,468 unique SNPs. These phenotypes serve as the final analytical units for PRS calculation, risk stratification, and individual-level interpretation. The generated reports integrate sample-level results, phenotype categorization, risk classification, study references, and variant tables, providing a structured and interpretable output for end users. Together, the curated database and reporting framework establish a comprehensive toolbox for PRS analysis, enhancing reproducibility, transparency, and usability in both research and clinical contexts.

bioinformatics↗

bioRxiv2026

GTX-GUT: A Standardized Metagenomic Workflow for Gut Microbiome Profiling and Clinical Associations

The human gut microbiome plays a central role in host physiology and disease, yet metagenomic analysis pipelines remain fragmented across sample preparation, taxonomic classification, and clinical interpretation stages, complicating reproducibility and translational use. Here we present GTX-GUT, a fully automated, containerized Snakemake pipeline for 16S rRNA gut microbiome profiling that integrates quality control, taxonomic classification (QIIME2/DADA2 against Greengenes 13.8), diversity and compositional metrics benchmarked against a curated healthy reference population, enterotype classification, a clinical association module spanning 11 disease categories, and automated natural-language report generation. We validated the pipeline using the ZymoBIOMICS mock community, showing that BBDuk preprocessing substantially reduced genus-level quantification error (Mean Absolute Error reduced from 7.34 to 1.58 percentage points; Pearson's $r$ improved from 0.576 to 0.833). Application to a human sample from a patient with type 2 Diabetes Mellitus recovered a dysbiotic signature consistent with the literature, including reduced Firmicutes abundance, elevated Bacteroidetes and Proteobacteria, and a predominance of clinical associations within metabolic and gastrointestinal categories. These results demonstrate that GTX-GUT provides a reproducible, end-to-end framework linking raw sequencing data to clinically interpretable output, with direct applicability to research and translational microbiome studies.

bioinformatics↗

bioRxiv2025

Developmental Dysregulation of Synaptic and Myelin-Related Genes in Frontal Cortex and Serum Infrared Spectroscopy Signature in the Valproic Acid Model of Autism

Neural circuits emerge during development through dynamic interactions between genetic instructions and environmental cues that shape cell fate, connectivity, and the timing of myelination. In developmental disorders such as autism, abnormalities in sensory processing, social cognition, and motor behavior are thought to arise from disruptions in these processes. Here, we investigated early-life molecular changes following prenatal exposure to valproic acid (VPA), an environmentally induced model of autism. We integrated cortical gene expression analysis using RNA sequencing/qPCR, alternative splicing profiling, in situ myelin quantification, plasma serotonin determination, and machine learning-assisted classification of blood serum molecular profiles using infrared spectroscopic (FTIR) data. Our findings revealed downregulation of myelin-associated genes and upregulation of synapse-related genes in the frontal cortex of young VPA-exposed rats. qPCR confirmed reduced cortical expression of Mobp and PLP1 along with increased Penk and C1ql3 expression. Alternative splicing analysis identified numerous novel transcript variants, enriched in synaptic-related genes, indicating widespread post-transcriptional remodeling in VPA animals. These molecular alterations were accompanied by a significant reduction in myelin content within the cingulate and motor cortex of adult animals. Peripheral molecular profiling showed elevated plasma serotonin in VPA-treated animals and demonstrated that a support vector machine trained on serum FTIR spectra classified VPA-exposed animals with 85% accuracy. Collectively, our findings suggest that prenatal VPA exposure induces early dysregulation of myelin organization, synaptic gene networks, and RNA splicing programs, potentially leading to long-term impairments in neuronal communication and processing efficiency. Furthermore, our results highlight serum spectroscopic signatures as promising peripheral biomarkers for autism, warranting further investigation.

neuroscience↗

bioRxiv2020

A new genome-wide method to identify genes with bimodal gene expression

A new method is presented to detect bimodality in gene expression data using the Gaussian Mixture Models to cluster samples in each mode. We have used the method to search for bimodal genes in data from 25 tumor types available from The Cancer Genome Atlas. The method identified 554 genes with bimodal gene expression, of which 46 were identified in more than one cancer type. To further illustrate the impact of the method, we show that 96 out of the 554 genes with bimodal expression patterns presented different prognosis when patients belonging to the two expression peaks are compared. The software to execute the method and the corresponding documentation are available at https://github.com/LabBiosystemUFRN/Bimodality_Genes.

genomics↗

bioRxiv2020

Characterization and analysis of the transcriptome in Arapaima gigas using multi-tissue RNA-sequencing

Arapaima gigas is a giant bony tongue air-breathing fish, and a promising species for aquaculture due to its particular features. However, there is still a lack of information on its biology and few transcriptome studies are available. Our aim was to characterize the transcriptome of arapaima in order to shed light on molecular networks contributing to its unique traits. Through RNA-sequencing, we generated a transcriptome from eight tissues (brain, pituitary, heart, muscle, kidney, lung, ovary, and testis) collected from arapaima adults specimens. Using a genome-guided strategy associated with homologous protein evidence, 57,706 transcripts were assembled, which aligned to 23,353 high confidence protein-coding genes. The analysis revealed a global view of expression patterns, as well as it allowed us to identify tissue-specific gene clusters, transcription factors within the clusters, and to compare expression patterns between male and female. These analyses has generated tissue-specific and sex-biased transcriptome profiles, which will be helpful to understand its molecular biology, evolution, and also guide future functional studies of the arapaima.

genomics↗

bioRxiv2019

neoANT-HILL: an integrated tool for identification of potential neoantigens

Cancer neoantigens have attracted great interest in immunotherapy due to their ability to elicit antitumoral immune responses. These antigens are formed due to somatic mutations in the cancer genome that result in alterations of the original protein. Although current technological advances in neoantigen identification, it remains a challenging and a large number of false-positive continue to exist. In the current work, we present neoANT-HILL, an automatized user-friendly tool that integrates several immunogenomic analysis to improve neoantigens detection from NGS data. The program input can be a file with somatic mutations called and/or RNA-seq data. Our tool was applied on somatic mutations of melanoma dataset from TCGA and found that neoANT-HILL was able to predicted potential neoantigens. The software is available on github at https://github.com/neoanthill/neoANT-HILL.

bioinformatics↗

bioRxiv2018

Building comprehensive MS-friendly databases for proteomic analysis of bacterial species of unknown genetic background

In proteomics, peptide information within mass spectrometry data from a specific organism sample is routinely challenged against a protein sequence database that best represent such organism. However, if the species/strain in the sample is unknown or poorly genetically characterized, it becomes challenging to determine a database which can represent such sample. Building customized protein sequence databases merging multiple strains for a given species has become a strategy to overcome such restrictions. However, as more genetic information is publicly available and interesting genetic features such as the existence of pan- and core genes within a species are revealed, we questioned how efficient such merging strategies are to report relevant information. To test this assumption, we constructed databases containing conserved and unique sequences for ten different species. Features that are relevant for probabilistic-based protein identification by proteomics were then monitored. As expected, increase in database complexity correlates with pangenomic complexity. However, Mycobacterium tuberculosis and Bortedella pertusis generated very complex databases even having low pangenomic complexity or no pangenome at all. This suggests that discrepancies in gene annotation is higher than average between strains of those species. We further tested database performance by using mass spectrometry data from eight clinical strains from Mycobacterium tuberculosis, and from two published datasets from Staphylococcus aureus. We show that by using an approach where database size is controlled by removing repeated identical tryptic sequences across strains/species, computational time can be reduced drastically as database complexity increases.

bioinformatics↗