Análise de dados públicos de expressão gênica de distúrbios do espectro do autismo

• Autor(a) principal: Pereira, Hudson
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UTFPR (da Universidade Tecnológica Federal do Paraná (RIUT))
• Texto Completo: http://repositorio.utfpr.edu.br/jspui/handle/1/30201
• topic: Ácido ribonucleico - Síntese; Autismo; Bioinformática; RNA - Synthesis; Autism; Bioinformatics; CNPQ::CIENCIAS EXATAS E DA TERRA; Engenharia/Tecnologia/Gestão

Autism Spectrum Disorder (ASD) syndrome is characterized by interaction difficulties, communication deviation and repetitive behaviors. This syndrome is also defined as loss of contact with reality, caused by impossibility or great difficulty in interpersonal communication. ASD can be classified according to severity into: mild, moderate and severe. Early diagnosis of autism is essential for effective treatment. Transcriptomic analyzes are a means of obtaining regulatory information to understand ASD. In this sense, this work presents the result of a meta-analysis on publicly available gene expression data from ASD in associated studies. The methodology applied consisted of using expression data obtained after a review of the literature on ASD, being, three sets of selected data, collected in the NCBI GEO portal in December/19, and analyzed via RNA-Seq data the key genes related to TEA The RNA- Seq analysis pipeline was used to: (i) extract data in SRA using fastq-dump, in Rstudio; (ii) evaluation and quality control via the Trimmomatic program, in which the quality cut of the sequences was performed; (iii) then, the data were aligned with the reference genome (GRCh38) using Salmon and applied to estimate quantification and transcription level; and (iv) txtimport was used to assemble the counting matrix, finally, we used DESeq for differential expression analysis. The scatter analysis of expression data was displayed graphically using Vulcan. Then, the PCA (Principal component analysis) technique for analysis of groups, together with the analysis of enriched genes, using the terms of the GO, we identified potentials, groups and functions of the analyzed genes, being possible to identify a total of ten genes differentially. expressed, being three genes highly expressed and seven genes with low expression. Of these genes, eight are protein-coding, and two are small RNAs. In addition, it was observed that some genes are related to another genetic disease, in this case schizophrenia.