From 9160e873171b20229a8a4392f9262d4b93ad6210 Mon Sep 17 00:00:00 2001 From: coolneng Date: Fri, 2 Jul 2021 03:35:40 +0200 Subject: [PATCH] Place abstracts before the table of contents --- Dissertation.org | 19 +------------------ Dissertation.pdf | Bin 1035657 -> 1035346 bytes assets/abstract.yaml | 4 ++++ assets/babathesis.latex | 14 +++++++++++++- 4 files changed, 18 insertions(+), 19 deletions(-) create mode 100644 assets/abstract.yaml diff --git a/Dissertation.org b/Dissertation.org index db83dae..322e36c 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -9,25 +9,8 @@ #+PANDOC_OPTIONS: pdf-engine:xelatex #+PANDOC_OPTIONS: top-level-division:chapter #+PANDOC_OPTIONS: mathjax:t +#+PANDOC_OPTIONS: metadata-file:assets/abstract.yaml #+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 - -* 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 - -\vspace{0.5cm} - -*Keywords:* deep learning, error correction, DNA sequencing, T-cell receptor, immunology - * Introducción El ácido desoxirribonucleico (ADN) y el ácido ribonucleico (ARN) son los repositorios moleculares de la información genética. La estructura de cada proteína, y en última instancia de cada biomolécula y componente celular, es producto de la información programada en la secuencia de nucleótidos de una célula. La capacidad de almacenar y transmitir la información genética de una generación a otra es una condición fundamental para la vida. Un segmento de una molécula de ADN que contiene la información necesaria para la síntesis de un producto biológico funcional, ya sea una proteína o un ARN, se denomina gen. 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z(Fa6bj*Q)IzN@K2TE9%_E&I6G+5dxd3*ZzZFa7K8Pj0Ksbfz#`w)*5NHItyn?zBvMB}$c(_3F;FZ>C9K zPIz9XY`{j(sqj4zU)2VkMw>g68+a4ehe?dW$A_HSoVj&t>*G4#|Bf?&ujlu?I0Cv5 zi=Ap8_>SV^D}Ca(>sw`-49=w|$X$>97X4q(J%b?YZOGLNTV=cu&5W}`iMlWvqc3n5 zDM)W@E1sxjc`K-$eN3s-K+uOE#avv+thluYY4KPak?d{wEijSa!FMsoWY^|V*-c*> z?lzcS%FAesoVKD-x-D{SvCPAlf#{YKk!EuV&kacDm+UO~h7jEsFx8j4=cu?@$h{T#SIDi*oU39Ia!N!Mud}aj{yNS0pmW7yI&h$qxq5Kia?H|1VkeC+h$J diff --git a/assets/abstract.yaml b/assets/abstract.yaml new file mode 100644 index 0000000..6237246 --- /dev/null +++ b/assets/abstract.yaml @@ -0,0 +1,4 @@ +spanish-abstract: "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." +spanish-keywords: "deep learning, corrección de errores, receptor de linfocitos T, secuenciación de ADN, inmunología" +english-abstract: "Next generation sequencing (NGS) techniques 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." +english-keywords: "deep learning, error correction, DNA sequencing, T-cell receptor, immunology" diff --git a/assets/babathesis.latex b/assets/babathesis.latex index 2d2b4c4..06ee4eb 100644 --- a/assets/babathesis.latex +++ b/assets/babathesis.latex @@ -438,7 +438,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Import bibliographies -\addbibresource{bibliography.bib} +\addbibresource{$bibliography$} \begin{document} % UGR titlepage @@ -480,6 +480,18 @@ \end{titlepage} \frontmatter + \chapter*{Resumen} + \begin{center} + $spanish-abstract$ \\ + \vspace{0.5cm} + \textbf{Palabas clave:} $spanish-keywords$ + \end{center} + \chapter*{Abstract} + \begin{center} + $english-abstract$ \\ + \vspace{0.5cm} + \textbf{Keywords:} $english-keywords$ + \end{center} \tableofcontents \listoftables{} \listoffigures{}