From b12ed6c95252254afd9110fd1ec0415c8d289ab6 Mon Sep 17 00:00:00 2001 From: coolneng Date: Mon, 28 Jun 2021 16:23:15 +0200 Subject: [PATCH] Set chapter as first header without blank pages --- Dissertation.org | 15 +++++++++++---- Dissertation.pdf | Bin 111055 -> 115338 bytes assets/babathesis.latex | 4 ++-- 3 files changed, 13 insertions(+), 6 deletions(-) diff --git a/Dissertation.org b/Dissertation.org index a7b578b..090b7fa 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -7,12 +7,15 @@ #+PANDOC_OPTIONS: citeproc:t #+PANDOC_OPTIONS: csl:assets/ieee.csl #+PANDOC_OPTIONS: pdf-engine:xelatex +#+PANDOC_OPTIONS: top-level-division:chapter #+PANDOC_METADATA: link-citations:t * Resumen 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 +\vspace{0.5cm} + *Palabras clave:* deep learning, corrección de errores, receptor de linfocitos T, secuenciación de ADN, inmunología \clearpage @@ -22,6 +25,8 @@ Las nuevas técnicas de secuenciación de ADN (NGS) han revolucionado la investi 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 +\vspace{0.5cm} + *Keywords:* deep learning, error correction, DNA sequencing, T-cell receptor, immunology \clearpage @@ -30,12 +35,14 @@ Next generation sequencing (NGS) have revolutionised genomic research. These tec La secuenciación de ADN es el proceso mediante el cual se determina el orden de los nucleótidos en una secuencia de ADN. En los años 70, Sanger \etal desarrollaron métodos para secuenciar el ADN mediante técnicas de terminación de cadena. cite:Sanger5463 Este avance revolucionó la biología, proporcionando las herramientas necesarias para descifrar genes, y posteriormente, genomas completos. +La demanda creciente de un mayor rendimiento llevó a una automatización y paralelización de las tareas de secuenciación. + La capacidad del sistema inmunitario adaptativo para responder a cualquiera de los numerosos antígenos extraños potenciales a los que puede estar expuesta una persona depende de los receptores altamente polimórficos expresados por las células B (inmunoglobulinas) y las células T (receptores de células T [TCR]). La especificidad de las células T viene determinada principalmente por la secuencia de aminoácidos codificada en los bucles de la tercera región determinante de la complementariedad (CDR3). cite:pmid19706884 -* Motivación -* Estado del arte -** NGS -** Bioinformática (Deep Learning) +** Estado del arte +*** NGS +*** Bioinformática (Deep Learning) +* Motivación y Objetivos * Metodología ** Tecnologías ** Pipeline diff --git a/Dissertation.pdf b/Dissertation.pdf index 84ce73afa165d587d0e41462cb80a9e442e475b7..06dab129c2a59aa202123c4fe4550698dae7efa9 100644 GIT binary patch delta 40833 zcmZ^pV{~SXvaMs=wr$(CZQFih+ji2iZQDu5PC7O^PVTqw-Dixm$2s@k`dMq%Tu;ra zvF48;qQf8qTYw8;0Bjrr0x+&_F6PGeFkYK+>hcMw%y7Mrv=5LJWbsF2D!)NNG@Nu7 zaSxex&RvQ`d=GCQAGod6$<)NNxZ3t)KM$)o&C>?wtAu27t&pn?>KgcrKkbgJb@t{Q zWtz(-$fZBXRymS&*3YQ~EVq=`P3Eh~8xSZ?dS43aCHy~Y0r~fr+Jd=8ZBtDibfpUQ zQ#jQd4LWq%Vof~udx&FP2?w#nLuZfJ9IkqisC-mLHCXixUMcjE#Msqb=6B=|^bAd+Ko^8L=1ko6e@I4S_t9{267PmH7 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