Identificação Patogénica em Células Humanas Cancerígenas

• Autor(a) principal: Nuno Miguel de Albuquerque Martinho
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://repositorio-aberto.up.pt/handle/10216/90077
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

About 15% to 20% of cancers in humans are due to viral infections. These infections sometimes have their pathogenic origins in human cells. The presence of bacterias and viruses in human cells, such as human papillomavirus, hepatitis B, among other, increases the risk of developing cancer. These bacterias/viruses are formed from translation, at the ribosome, of the mRNA sequences, resulting in viral proteins.The investment and efforts in the area of Bioinformatics, specifically "Computational Transcriptomics" and "Sequencing and Genotyping Technologies", can help to understand the role of infections and external agents in the formation of cancers.Currently, there are several tools that help in the identification of non-human sequences present in the RNA. These tools allow from RNA mapping, differentiate the sequences between the human genome and bacterial/viral infections. These tools have different degrees of effectiveness depending on the sample and the purpose of the analysis. However, this differentiation, in isolation, does not have a big impact to the study and identification of non-pathogenic on human sequences. The whole process of implementation of these tools tend to be complex and difficult for researchers with a low level knowledge in the area of Informatics.As a possible solution for this problem arises the need to make a platform that automatically, does the mapping and alignment process of the RNA with bacterial/viral infections "datasets". The proposed solution is an online platform connected to a pipeline in a web service that analyzes the samples by applying successive existing tools (Tophat, Bowtie2, Samtools) on the user's sample. Being this mapping iterative and successive filter, it has a large computational weight,consume many resources, and the processing time is proportional to the number of "reads" to map. With the comparison of the sequences against the various "datasets" is expected to be possible to obtain graphic and understandable statistics of the samples gene's mapping . You can check genes with a high RPKM (Reads Per kilobase transcript of per Million mapped reads) and check regions of the genome where are a high number of active genes. The solution aims to be a useful tool in the study of infections and external agents in the formation of cancers.