From 91aaa82a86705067404abfdd433c01f9c1dfb84c Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 30 Jun 2021 00:48:42 +0200 Subject: [PATCH] Finalize biological principles introduction --- Dissertation.org | 15 +++--- Dissertation.pdf | Bin 470882 -> 316058 bytes assets/bibliography.bib | 68 +++++++++++++++------------ assets/figures/sequencing-errors.png | Bin 166370 -> 0 bytes 4 files changed, 48 insertions(+), 35 deletions(-) delete mode 100644 assets/figures/sequencing-errors.png diff --git a/Dissertation.org b/Dissertation.org index 78600af..ad4d5f7 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -31,13 +31,14 @@ Next generation sequencing (NGS) have revolutionised genomic research. These tec 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. El almacenamiento y la transmisión de información biológica son las únicas funciones conocidas del ADN. cite:book:lehninger -Hay muy pocos principios firmes en biología. A menudo se dice, de una forma u otra, que la única regla real es que no hay reglas, es decir, que se pueden encontrar excepciones a cada principio fundamental si se busca lo suficiente. El principio conocido como el Dogma central de la biología molecular parece ser una excepción a esta regla de excepción ubicua. cite:CRICK1970 El dogma central de la biología molecular establece que una vez que la información ha pasado a proteína no puede volver a salir; \ie la transferencia de información de ácido nucleico a ácido nucleico, o de ácido nucleico a proteína puede ser posible, pero la transferencia de proteína a proteína, o de proteína a ácido nucleico es imposible. cite:crick1958protein - #+CAPTION: Dogma central de la biología molecular #+ATTR_HTML: :height 25% :width 80% #+NAME: fig:central-dogma [[./assets/figures/central-dogma.png]] +Hay muy pocos principios firmes en biología. A menudo se dice, de una forma u otra, que la única regla real es que no hay reglas, es decir, que se pueden encontrar excepciones a cada principio fundamental si se busca lo suficiente. El principio conocido como el Dogma central de la biología molecular parece ser una excepción a esta regla de excepción ubicua. cite:CRICK1970 El dogma central de la biología molecular establece que una vez que la información ha pasado a proteína no puede volver a salir; \ie la transferencia de información de ácido nucleico a ácido nucleico, o de ácido nucleico a proteína puede ser posible, pero la transferencia de proteína a proteína, o de proteína a ácido nucleico es imposible. cite:crick1958protein + +Por lo tanto, si elucidamos la información contenida en el ADN, obtenemos información sobre las biomoléculas que realizan las diferentes tareas fisiológicas y metabólica (e.g. ARN, proteínas). ** Secuenciación de ADN @@ -59,10 +60,11 @@ Estas nuevas técnicas han demostrado su valor, con avances que han permitido se Aunque las tecnologías de secuenciación paralelas (NGS) han revolucionado el estudio de la variedad genómica entre especies y organismos individuales, la mayoría tiene una capacidad limitada para detectar mutaciones con baja frecuencia. Este tipo de análisis es esencial para detectar mutaciones en oncogenes (genes responsables de la transformación de una célula normal a maligna), pero se ve restringido por una tasa de errores de secuenciación no despreciables. En 2011, la tasa de errores por substitución (intercambio de un nucleótido por otro) era > 0.1%, y era similar en estudios posteriores. cite:Ma2019 -#+CAPTION: Tasa de error de secuenciación global de dos secuenciadores NovaSeq en la biblioteca de ADN de referencia COLO829. La mediana de las tasas de error (barras negras verticales) de cada tipo de desincorporación en bases de tipo salvaje conocidas se indica en el margen izquierdo del panel. cite:Davis2021 -#+ATTR_HTML: :height 30% :width 50% -#+NAME: fig:sequencing-errors -[[./assets/figures/sequencing-errors.png]] +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 + +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 Este problema se agrava en el análisis de repertorios inmunológicos, debido a nuestra limitada capacidad para distinguir entre la verdadera diversidad de los receptores de los linfocitos T (TCR) e inmunoglobulinas (IG) de los errores de PCR y secuenciación que son inherentes al análisis del repertorio. Los clonotipos resultantes pueden tener concentraciones drásticamente diferentes, lo que hace que los clonotipos menores sean indistinguibles de las variantes erróneas. cite:Shugay2014 @@ -79,6 +81,7 @@ 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. * Estado del arte ** Bioinformática diff --git a/Dissertation.pdf b/Dissertation.pdf index 5f79fede1c833cfb469005ba84d3d5ee012e77dd..314291c1dead6c61dd60b559c7d648a621f66e57 100644 GIT binary patch delta 49294 zcmZU)Q?&iu)CyyII!##*_0mDI^G_$2a(Hn#lWho2w2T6j@1$#v4Ol8G~-eNkw(tuWF+ zZu%gJn-k-$@0XYJ$s_(_wEpbJIV>~g-%HW;Y<)NY=f49)37K#dXFWf>oJXKR?$PM^ zQy7~Y7X!(=Jt*T5j}X!}8$sC1j!5B91tLIBueyhMl63Mu!j#)zFrR-(Y|6+P7mbYm}$<9_mALlI^+z_X&wbO0S`~(BlOPqqK6_Q-tBusZzL< z!MaZX32I1mf$&=QmL*jVG9%xwDMZ9%2G8zZqQ^#)g4-FbExLb|OyJ 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Shao, Ying and Szlachta, Karol and Mulder, - Heather L. and Ren, Dongren and Rice, Stephen V. and Wang, - Zhaoming and Nakitandwe, Joy and Gout, Alexander M. and - Shaner, Bridget and Hall, Salina and Robison, Leslie L. and - Pounds, Stanley and Klco, Jeffery M. and Easton, John and Ma, - Xiaotu}, - title = {SequencErr: measuring and suppressing sequencer errors in - next-generation sequencing data}, - journal = {Genome Biology}, - year = 2021, - month = {Jan}, - day = 25, - volume = 22, - number = 1, - pages = 37, - abstract = {There is currently no method to precisely measure the - errors that occur in the sequencing instrument/sequencer, - which is critical for next-generation sequencing applications - aimed at discovering the genetic makeup of heterogeneous - cellular populations.}, - issn = {1474-760X}, - doi = {10.1186/s13059-020-02254-2}, - url = {https://doi.org/10.1186/s13059-020-02254-2} -} - @article{HEATHER20161, title = {The sequence of sequencers: The history of sequencing DNA}, journal = {Genomics}, @@ -507,3 +478,42 @@ pages = 8, year = 1958 } + +@article{10.1093/bioinformatics/btg109, + author = {Lee, Christopher}, + title = "{Generating consensus sequences from partial order multiple + sequence alignment graphs}", + journal = {Bioinformatics}, + volume = 19, + number = 8, + pages = {999-1008}, + year = 2003, + month = 05, + abstract = "{Motivation: Consensus sequence generation is important in + many kinds of sequence analysis ranging from sequence assembly + to profile-based iterative search methods. However, how can a + consensus be constructed when its inherent assumption—that the + aligned sequences form a single linear consensus—is not + true?Results: Partial Order Alignment (POA) enables + construction and analysis of multiple sequence alignments as + directed acyclic graphs containing complex branching + structure. Here we present a dynamic programming algorithm + (heaviest\_bundle) for generating multiple consensus sequences + from such complex alignments. The number and relationships of + these consensus sequences reveals the degree of structural + complexity of the source alignment. This is a powerful and + general approach for analyzing and visualizing complex + alignment structures, and can be applied to any alignment. We + illustrate its value for analyzing expressed sequence + alignments to detect alternative splicing, reconstruct full + length mRNA isoform sequences from EST fragments, and separate + paralog mixtures that can cause incorrect SNP + predictions.Availability: The heaviest\_bundle source code is + available at http://www.bioinformatics.ucla.edu/poaContact: + leec@mbi.ucla.edu*To whom correspondence should be + addressed.}", + issn = {1367-4803}, + doi = {10.1093/bioinformatics/btg109}, + url = {https://doi.org/10.1093/bioinformatics/btg109}, + eprint = {https://academic.oup.com/bioinformatics/article-pdf/19/8/999/642375/btg109.pdf}, +} diff --git a/assets/figures/sequencing-errors.png b/assets/figures/sequencing-errors.png deleted file mode 100644 index 0c520b357d287a79c67b213ce8bb8db44b221682..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 166370 zcmb5WWn5c96FwTOXp2j6hZct-#i0Z%P@ohmS}5*r!71)g+`YIv6f5o$q_{)T;6d); 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