Machine Learning Algorithms applied for Virus Identification in Dark Matter sequencing data from Next Generation Sequencing

• Grant number: 23/12155-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: July 01, 2024
• End date: March 31, 2025
• Field of knowledge: Biological Sciences - Microbiology - Applied Microbiology
• Principal Investigator: Svetoslav Nanev Slavov
• Grantee: Gabriel Montenegro de Campos
• Host Institution: Hemocentro de Ribeirão Preto. Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da USP (HCMRP). Ribeirão Preto , SP, Brazil
• Associated research grant: 17/23205-8 - Evaluation of the impact of emerging and reemerging viruses in the field of hemotherapy and stem cell transplantation by multiple-research approach, AP.JP

Abstract

Metagenomic methods are one of the most powerful tools for identifying emerging or little-known viruses. With the advent of new generation sequencing technologies (NGS) and taxonomic classifiers, it became possible to identify their content, associating the sequences found with their proper taxa. However, there are still sequences that are not associated with their proper taxa, which are known as "dark matter". Dark matter is one of the main obstacles to a complete understanding of the metagenome. The content present in dark matter has the potential to discover new intra-phylum and inter-phylum relationships, as well as increase knowledge of biology and, in addition, discover new pathogens that may infect humans. Therefore, the objective of this work is to identify the viral content present in the unclassified part of the sequences. For this purpose, machine learning algorithms applied to samples from plasma from patients with oncological diseases, immunosuppression and hemorrhagic diathesis without known cause will be used. Using this approach, this project aims to improve bioinformatics analysis and understanding of metagenomic viral abundance in various clinical conditions. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE CAMPOS, GABRIEL MONTENEGRO; SANTOS, HAZERRAL DE OLIVEIRA; LIMA, ALEX RANIERI JERONIMO; LEITE, ANDERSON BRANDAO; RIBEIRO, GABRIELA; BERNARDINO, JARDELINA DE SOUZA TODAO; DO NASCIMENTO, JEAN PHELLIPE MARQUES; SOUZA, JULIANA VANESSA CAVALCANTE; DE LIMA, LOYZE PAOLA OLIVEIRA; LIMA, MARLON BRENO ZAMPIERI; et al. Unveiling viral pathogens in acute respiratory disease: Insights from viral metagenomics in patients from the State of Alagoas, Brazil. PLoS Neglected Tropical Diseases, v. 18, n. 9, p. 11-pg., 2024-09-01. (21/11944-6, 17/23205-8, 22/00910-6, 23/12155-0, 22/14958-0)

DE CAMPOS, GABRIEL MONTENEGRO; COSTA, THALITA CRISTINA DE MELLO; SILVEIRA, ROBERTA MARANINCHI; VALENCA, IAN NUNES; BEZERRA, RAFAEL DOS SANTOS; DARRIGO JUNIOR, LUIZ GUILHERME; VIEIRA, ANA CAROLINA DE JESUS; MESQUITA, CAMILA CAMPOS; LAURINDO, PATRICIA DA SILVA; CUNHA, RENATO GUERINO; et al. Viral Metagenomics in Patients Who Underwent Allogeneic Hematopoietic Stem Cell Transplantation (HSCT): A Brazilian Experience. MICROORGANISMS, v. 12, n. 12, p. 13-pg., 2024-12-01. (19/08528-0, 19/07861-8, 23/12155-0, 21/11944-6, 17/23205-8)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

CAMPOS, Gabriel Montenegro de. Application of machine learning algorithms for virus identification in dark matter data from next-generation sequencing. 2025. Master's Dissertation - Universidade de São Paulo (USP). Faculdade de Medicina de Ribeirão Preto (PCARP/BC) Ribeirão Preto.