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 *Palabras clave:* deep learning, corrección de errores, receptor de linfocitos T, secuenciación de ADN, inmunología
 
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 * Abstract
 
 Next generation sequencing (NGS) have revolutionised genomic research. These technologies perform sequencing of millions of fragments of DNA in parallel, which are pieced together using bioinformatics analyses. Although these techniques are commonly applied, they have non-negligible error rates that are detrimental to the analysis of regions with a high degree of polimorphism. In this study we propose a novel computational method, locimend, based on a /Deep Learning/ algorithm for DNA sequencing error correction. It is applied to the analysis of the complementarity determining region 3 (CDR3) of the T-cell receptor (TCR), generated in silico and subsequently subjected to a sequencing simulator in order to produce sequencing errors. Using these data, we trained a convolutional neural network (CNN) with the aim of generating a computational model that allows the detection and correction of sequencing errors.
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 *Keywords:* deep learning, error correction, DNA sequencing, T-cell receptor, immunology
 
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 * Introducción
 
 En los últimos años se ha