From 0ca89ef0f64a448cbddf395ab21ed731343da201 Mon Sep 17 00:00:00 2001 From: coolneng Date: Tue, 13 Jul 2021 01:18:36 +0200 Subject: [PATCH] Use metropolis as slides theme --- Presentation.org | 47 ++++++++++++++++++++++++----------------------- Presentation.pdf | Bin 307294 -> 237851 bytes 2 files changed, 24 insertions(+), 23 deletions(-) diff --git a/Presentation.org b/Presentation.org index 94f5419..66991c6 100644 --- a/Presentation.org +++ b/Presentation.org @@ -3,6 +3,7 @@ #+SUBTITLE: Trabajo de Fin de Grado #+LANGUAGE: es #+PANDOC_OPTIONS: pdf-engine:xelatex +#+PANDOC_METADATA: theme:metropolis * Introducción ** Descripción - Proyecto de bioinformática @@ -42,17 +43,19 @@ Hay distintos tipos de errores de secuenciación del ADN: - La región más variable es *CDR3* #+CAPTION: Generación de diversidad en el TCR -#+ATTR_HTML: :height 55% +#+ATTR_HTML: :height 40% [[./assets/figures/VDJ.png]] + * Objetivos del proyecto -- Detección de errores de secuenciación del ADN en las secuencias de CDR3 -- Corrección de estos errores, tanto sustituciones como /indels/ +Detección y corrección de errores de secuenciación del ADN en las secuencias de CDR3 -** Requisitos +- Aplicación a errores de sustitución e /indels/ + +** Etapas -- Desarrollo de un algoritmo de /Deep Learning/ - Generación de un /dataset/ para entrenar el algoritmo +- Desarrollo de un algoritmo de /Deep Learning/ - Creación de una interfaz que permita utilizar el algoritmo * Estructura del proyecto @@ -63,30 +66,35 @@ El proyecto se divide en 2 partes: - locimend: Corrección de los errores de secuenciación del ADN #+CAPTION: Pipeline -#+ATTR_HTML: :height 80% :width 70% +#+ATTR_HTML: :height 65% :width 65% [[./assets/figures/pipeline.png]] -Optamos por esta segmentación debido a que el algoritmo de /Deep Learning/ es generalizable, y se podría entrenar con otro /dataset/ de secuencias de ADN. +Optamos por esta segmentación debido a que el algoritmo de /Deep Learning/ es generalizable, y se podría entrenar con otro /dataset/ * locigenesis -- locigenesis es una herramienta que genera una secuencia de receptores de células T (TCR) humano para posteriormente aplicarle una herramienta de simulación de secuenciación (CuReSim) y, finalmente, extraer las regiones CDR3 tras la introducción de errores. +- locigenesis es una herramienta que genera una secuencia de receptores de células T (TCR) humano para posteriormente aplicarle una herramienta de simulación de secuenciación (CuReSim) y, finalmente, extraer las regiones CDR3 tras la introducción de errores - Obtención de CDR3 con y sin errores de secuenciación - La simulación de secuenciación se realiza con el TCR completo, y se extrae CDR3 mediante alineamiento con las secuencias de referencia y ciertas heurísticas -- Lenguaje de programación: R + +| Tecnologías | Descripción | +|-------------+------------------------------------------------------------------| +| immuneSIM | Generación /in silico/ de repertorios de BCR y TCR | +| CuReSim | Simulador de secuenciación que emula la tecnología /Ion Torrent/ | +| Biostrings | Manipulación de secuencias biológicas | * locimend -- locimend es un algoritmo de /Deep Learning/ que corrige errores de secuenciación de secuencias de ADN. -- Creación de un modelo que pueda inferir la secuencia correcta de ADN, a partir de una secuencia de ADN con errores. +- locimend es un algoritmo de /Deep Learning/ que corrige errores de secuenciación de secuencias de ADN +- Creación de un modelo que pueda inferir la secuencia correcta de ADN, a partir de una secuencia de ADN con errores - Arquitectura: /deep feedforward network/ -#+CAPTION: Arquitectura de la red neuronal -#+ATTR_HTML: :height 40% :width 90% -#+NAME: fig:locimend -[[./assets/figures/locimend.png]] +| Tecnologías | Descripción | +|-------------+----------------------------------------------------------| +| Tensorflow | Creación y ejecución de algoritmos de /machine learning/ | +| Biopython | Manipulación de secuencias biológicas | +| FastAPI | /Framework/ web para la creación de APIs | -- Lenguaje de programación: Python * Paralelización - Ciertas etapas en el sistema son cuellos de botella, para superar este impedimento empleamos la paralelización @@ -129,13 +137,6 @@ content: application/json { "sequence": "TGTGCCAGCAGCTTAGCGGACAGTTCGGGGCAGAGCAGTAC" } -// POST http://localhost:8000 -// HTTP/1.1 200 OK -// date: Mon, 12 Jul 2021 22:14:35 GMT -// server: uvicorn -// content-length: 56 -// content-type: application/json -// Request duration: 1.018606s #+END_SRC * Resultados diff --git a/Presentation.pdf b/Presentation.pdf index d94f4184975d83794f88bbf95d246b2b18aec86b..a5c7afacca8a02f3ee3396d5aad76a964675c069 100644 GIT binary patch delta 50083 zcmZs?Q*>rsw+0y7uGqHCif!Ar@y538N-DN(JE_>Vt*#$u^!d8a-MZK}d#pLI3=mEuY-;1Ike&ySq&))$FvvB&oa5|Aaqd>*Et(K3eO=%^ z$vZP2cNP_pQ0GfSc`>(~9zI525$yJ()_1eeJN?83Z5TstIP0-A%UUm#`pPy1g;M|S>QGE 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