From db2d910b7ff98845c506bdf05f8c02a2e8506779 Mon Sep 17 00:00:00 2001 From: coolneng Date: Sun, 27 Jun 2021 02:59:50 +0200 Subject: [PATCH] Add keywords --- docs/Dissertation.org | 7 +++++++ docs/Dissertation.pdf | Bin 420494 -> 420989 bytes 2 files changed, 7 insertions(+) diff --git a/docs/Dissertation.org b/docs/Dissertation.org index 4ee9acb..3b842ee 100644 --- a/docs/Dissertation.org +++ b/docs/Dissertation.org @@ -15,12 +15,19 @@ Las nuevas técnicas de secuenciación de ADN (NGS) han revolucionado la investigación en genómica. Estas tecnologías se basan en la secuenciación de millones de fragmentos de ADN en paralelo, cuya reconstrucción se basa en técnicas de bioinformática. Aunque estas técnicas se apliquen de forma habitual, presentan tasas de error significantes que son detrimentales para el análisis de regiones con alto grado de polimorfismo. En este estudio se implementa un nuevo método computacional, locimend, basado en /Deep Learning/ para la corrección de errores de secuenciación de ADN. Se aplica al análisis de la región determinante de complementariedad 3 (CDR3) del receptor de linfocitos T (TCR), generada /in silico/ y posteriorimente sometida a un simulador de secuenciación con el fin de producir errores de secuenciación. Empleando estos datos, entrenamos una red neuronal convolucional (CNN) con el objetivo de generar un modelo computacional que permita la detección y corrección de los errores de secuenciación. # TODO Add results +*Palabras clave:* deep learning, corrección de errores, receptor de linfocitos T, secuenciación de ADN, inmunología + * 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. # TODO Add results +*Keywords:* deep learning, error correction, DNA sequencing, T-cell receptor, immunology + * Introducción + + + * Motivación * Estado del arte ** NGS diff --git a/docs/Dissertation.pdf b/docs/Dissertation.pdf index 7ec5569c4a19f1a7ca4c94764d90974ccc980e81..ef03d671333e57cfa5325976997816e0b0f5d567 100644 GIT binary patch delta 70722 zcmV(-K-|BM)fxTR8GwWVgaWh!_Rs_~Fft&Q@NWetf1O!dm!mcke(ztwBb?K%NC*Vv zZ8kH>Niy4Kcc%9#od?>cZDqN5j7g{e{Z^?;7}Rzq*%t#UQIfv8eT8MTi)8e}%|9M) zzWF}OBPB&8D-}IFM}-unF;S71Lgi`nu#J9+|5V;{BAJ&CuhuAvzN%^P{pPpVPv34exh{odGYrl#N6 z`l8l%@9&elOco|i&7Cwbma62}ho5dfJluS_QE+$}sq6h4f2>=Ww8BdV$*M{73y&<;C*Aot^A zL)RzgoJJk=3c*wQt*8?h8BY1a|&~hquurPQRAZ(@tCs9^7kFjL+Vq>f2oirl zFLD9b6CKe$Zy_lni|aKhy`?t+B3h`gJ3nvgYZ8)VOQU@=!84w@e{N}nAy|^3mR@vD 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