From d8da37359b3b9b2e75ba97af5f4f230d6708da6b Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 30 Jun 2021 01:45:45 +0200 Subject: [PATCH] Add consensus sequence figure --- Dissertation.org | 7 ++-- Dissertation.pdf | Bin 316058 -> 722578 bytes assets/bibliography.bib | 45 ++++++++++++++++++++++++++ assets/figures/consensus-sequence.png | Bin 0 -> 401740 bytes 4 files changed, 50 insertions(+), 2 deletions(-) create mode 100644 assets/figures/consensus-sequence.png diff --git a/Dissertation.org b/Dissertation.org index ad4d5f7..011425f 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -62,7 +62,9 @@ Aunque las tecnologías de secuenciación paralelas (NGS) han revolucionado el e Para contrarrestar este obstáculo, varias técnicas mitigatorias se han puesto en marcha. Una de las más populares es el uso de una secuencia de consenso, que es un perfil estadístico a partir de un alineamiento múltiple de secuencias. Es una forma básica de descubrimiento de patrones, en la que un alineamiento múltiple de secuencias más amplio se resume en las características que se conservan. Este tipo de análisis permite determinar la probabilidad de cada base en cada posición de una secuencia. cite:10.1093/bioinformatics/btg109 -# TODO Find an appropriate figure +#+CAPTION: Alineamiento múltiple de secuencias. La secuencia de consenso aparece en la parte inferior y está formada por los nucleótidos que más aparecen en cada posición. cite:Nagar2013 +#+label: fig:consensus-sequence +[[./assets/figures/consensus-sequence.png]] Todas las técnicas de consenso monocatenarias reducen los errores en dos o tres órdenes de magnitud, lo que es mucho mayor que cualquier enfoque computacional o bioquímico anterior, y permiten identificar con precisión variantes raras por debajo del 0.1% de abundancia. Sin embargo, persisten algunos errores. Los errores que se producen durante la primera ronda de amplificación pueden propagarse a todas las demás copias escapando la corrección. cite:Salk2018 @@ -82,7 +84,8 @@ En el timo, durante el desarrollo de los linfocitos T, se selecciona al azar un Debido a la diversidad de uniones, las moléculas de anticuerpos y TCR muestran la mayor variabilidad, que forman la región determinante de la complementariedad 3 (CDR3). De hecho, debido a la diversidad de uniones, el número de secuencias de aminoácidos que están presentes en las regiones CDR3 de las de las moléculas de Ig y TCR es mucho mayor que el número que pueden ser codificadas por segmentos de genes de la línea germinal. cite:abbas_lichtman_pillai_2017 Frente a la evidencia recaudada, diversos métodos computacionales basados en la inteligencia artificial se aplican para aliviar estos impedimentos. - +** Inteligencia artificial +** Hacia el Deep Learning * Estado del arte ** Bioinformática ** Deep Learning diff --git a/Dissertation.pdf b/Dissertation.pdf index 314291c1dead6c61dd60b559c7d648a621f66e57..a3279fc9b7a34b93cf7bba995430cd393f4d4a18 100644 GIT binary patch delta 433231 zcmY&eV^pS5yY8(w+qP|cvaP8mPsUUeCOp};YqB}n=2TOYZF{m#-&x;}bN)Shuk~o% z_l14!ol%6D5Y6QG3xtIN;zM}KyQ zm9?fKEfm>W4BqK=w9uear)`LLATN9I%8Y}K1nslls7L***4iAZy|tVNLK*Os{29>VcdT6)Wb83VwRye2#q 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