From f9703a884ebeec5c276c59146be11d0b8150516c Mon Sep 17 00:00:00 2001 From: coolneng Date: Mon, 5 Jul 2021 02:01:23 +0200 Subject: [PATCH] Add system design and description --- Dissertation.org | 59 +++++++++++++++++++++-- Dissertation.pdf | Bin 1213749 -> 1284122 bytes assets/bibliography.bib | 91 ++++++++++++++++++++++++++++++++++++ assets/figures/pipeline.png | Bin 0 -> 63796 bytes 4 files changed, 147 insertions(+), 3 deletions(-) create mode 100644 assets/figures/pipeline.png diff --git a/Dissertation.org b/Dissertation.org index 6aeaf55..2cd9bea 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -282,9 +282,62 @@ El uso de /Deep Learning/ para la corrección de errores de secuenciación es un 4. Introducción al uso de Tensorflow y Keras para Deep Learning 5. Estudio de aplicación de Tensorflow/Keras a la corrección de errores de secuenciación en base a los datos sintetizados previamente. -* Métodos -** Tecnologías -** Pipeline +* Diseño y descripción del sistema + +La finalidad de este proyecto es el desarrollo de un /pipeline/, con el objetivo de crear un algoritmo de /Deep Learning/ capaz de corregir errores de secuenciación en secuencias del ADN, en particular, en la región CDR3 del TCR. + +El sistema se compone de 2 partes, dado que el algoritmo de /Deep Learning/ no es dependiente del /dataset/ generado /in silico/, y se podría entrenar con cualquier otro conjunto de datos. + +#+CAPTION: Descripción del proyecto +#+NAME: tab:pipeline +| Elemento | Finalidad | Lenguaje de programación | +|-------------+----------------------------------------+--------------------------| +| [[https://git.coolneng.duckdns.org/coolneng/locigenesis][locigenesis]] | Generación de secuencias de CDR3 | R | +| [[https://git.coolneng.duckdns.org/coolneng/locimend][locimend]] | Corrección de errores de secuenciación | Python | + +El diseño del sistema queda plasmado a continuación: + +#+CAPTION: Diseño del sistema. (*A*) Entrenamiento del algoritmo de /Deep Learning/. Como /input/ proporcionamos el número de secuencias, junto con el número de lecturas que deseamos que se simulen. Locigenesis generará 2 archivos en formato FASTQ, que contienen CDR3 con y sin errores de secuenciación, que son el /input/ de locimend para entrenar el modelo de /Deep Learning/, cuya salida es el conjunto de métricas del algoritmo. (*B*) Inferencia del modelo de /Deep Learning/ previamente entrenado y desplegado. Se provee como entrada una secuencia de ADN con errores de secuenciación, el algoritmo procesa ésta y devuelve una secuencia de ADN sin errores +#+ATTR_HTML: :height 50% :width 75% +#+NAME: fig:pipeline +[[./assets/figures/pipeline.png]] + +Procedemos a la exposición de cada parte del /pipeline/, por separado, resaltando las tecnologías usadas y las funcionalidades. + +** locigenesis + +locigenesis es una herramienta que genera un receptor de células T (TCR) humano, lo pasa por una herramienta de simulación de lectura de secuencias y extrae las regiones CDR3. + +El objetivo de este proyecto es generar tanto secuencias de CDR3 con y sin errores de secuenciación, con el fin de crear /datasets/ para entrenar un algoritmo de /Deep Learning/. + +*** Tecnologías + +- immuneSIM: generación /in silico/ de repertorios de BCR y TCR, humanos y de ratón cite:Weber_2020 +- CuReSim: simulador de secuenciación que emula la técnica /Ion Torrent/ cite:Caboche_2014 +- Biostrings: manipulación de secuencias ómicas cite:Biostrings + +*** Funcionalidades + +El programa realiza, parametrizado por 2 parámetros de entrada (número de secuencias diferentes y número de lecturas por el simulador de secuenciación), los siguientes pasos: + +1. Generación de las secuencias de la cadena \beta de diversos repertorios inmunológicos del TCR +2. Exportación de las secuencias a un archivo en formato FASTQ (tanto CDR3 como la secuencia completa) +3. Simulación de una secuenciación mediante CuReSim, y almacenamiento de las secuencias con errores +4. Alineamiento de las secuencias completas con errores, y extracción de CDR3 a partir de una heurística +5. Exportación de las secuencias de CDR3 con errores y sin errores en archivos con formato FASTQ + +** locimend + +locimend es un algoritmo de /Deep Learning/ que corrige errores de secuenciación de secuencias de ADN. + +El objetivo de este proyecto es crear un modelo que pueda inferir la secuencia correcta de ADN, a partir de una secuencia de ADN con errores. Se trata de una reducción de ruido, aplicada a un problema de genómica. + +*** Tecnologías + +- Tensorflow: creación y ejecución de algoritmos de /machine learning/ cite:tensorflow2015-whitepaper +- Biopython: manipulación de secuencias ómicas cite:Cock_2009 + +*** Funcionalidades ** Reproducibilidad * Resultados * Conclusiones diff --git a/Dissertation.pdf b/Dissertation.pdf index bb58bed00703e0c55c859f78aa7d05027188694c..d2c41cf2faa76398a18271fe12781a6076271db2 100644 GIT binary patch delta 128096 zcma&N1yGgU_Bbpcl7e)1cZ0NqbVzq8AuZiJN{4{tp}V^e4blzL-JQ}6-$CE^&i&1Q z=9_zFHnZ8W_S&o0+UL}S$Rw64e65DU#sheTgb*DZ?M;lV5nboHGzUuOx!){aF`UDg zN~4%bL4U)B9Yb=QUONh5-nix~VjO!iFuwMl;bB|M^cRteqdH0EKqYp+YbcOEm9$p3 zeMuQKak-0R;I?|g8v#1HbG_emy1UU>z(f%1wkeyICm^etRiG?hLjnm4OSw_g4FR!m zr*B>3_4r$CJ`0KJWlW1&i72)na;#u>bql^aw~tWnn%D? zJUpE^VpmMbdw)Y_V7+|JlPy3d@gjibEJ1vHYdfOtFaqR$dnL#jV)XV41>tn-wxqz1 z)b&-)mm(NAex2J0&~u1UZ`TZ=$LxS|Z>h}eZ~ML(=ibW70qJ`bCqMPKAB@=(a9>5k zExV&|mJVQEFIWg?e%Zcb`*8JfMd|G>gXgfHWo}01?;jn8cjt7x$rU}0_-+gG!b4Xl z2@?^&2}kMo{az8S5~CAu7i8n`aAy4KT=er1y!XzO<5Ff!h-W`wX1d3rk|`kt%)*K} z+HCHYlw>xTND5@R>I+Mpwcm`9{vyLsmNI`Mq>JChrKanJck(K^nKa{RBK11FR|%8% z{t>%tE|~Ghm@Qbdf!~qgC+7XXEEKn$u}jHo)@UmjY14Xzi(YDR71&E@ zk7Cq(z%~^uFfp9|E_+Tv;Zml0_Yt|NGBs659@b0i=+nkaKW{-&p=`Ys8A{HCgd*#D z7(M>>q(XWpiJFp$)BS!5A;Wty@(CJs7?zsfJTa~zuxx~Bq{AQUuw8{~6M+Ik3#iWr z;z(Ai4>_FQqc)~XaTn~NSq*KeR{|%D7)LK9aK$*Rmp_Y5YawYS@e^+>71;1~(c@*! zdad};RDA0picwEZV^-&5?w=J^nvM)HiCw=Pg|m}V)4ozpJxovgU7oO3@gWWW9jU4q zJWu$Zuq;=2IJq#H%r7m?qH4ehRQxaq_gJP&@SBnw$wp6Sv5Jjr8kz-TG}S?sUAy|p zFtpQVk~ZE{9?wa??H~}|un5(v$hpl^-Pc@TOXg?Oa=vAaMec>*r`vQ{O-u<{s8a zFDB8q9SaCIA-8RJux=Es3CU8YE0dO-7&_oKN#^D(4%4BH(x^{nRAK}qD_#)`i8ZCYK1k#?^Z{-vBBRh2JbsPCENX6$=HcRfrqr(=Bz;a?Jk zGr$p9JoI16xRtbW$V^o!Wp|doWJCmiU z-*?WHJFOEKm)CMMY-Jx#c(d%f+xTQ#=z;vB_{%J;+eqzw`j!6Wo#OHf`2ARS5L7K1 z1{4Vz(Y5DP@tLWQ)yF=ompzLAnyOL_-&pYcUT(>kYyR=@Rh@Y_6S3}vY9c$v6soCBeHOmn@{ z4y|A6g9NfV-$w9w9@19$^ zG;i(?bT0=5Zzwj>aooZSrvX{T4)!fSZ%+zomIdtIvj#2f2Bh zVGpi2GN0)4IoZ4JcQy!~NcOafP9t=I8&9aeL}g+M`>hpOd)sL1up`O4HU$entvEHk z=Xuzuq2mf~^Ws>59G;Qg+mLAvkJ2Z=TT94GJv55EKR*t1_c43)CyPwyN*w4gB2*X1 ziBn!Wd)iN9X=QG0A`IeX_>QV8=My_tyzAo#OIOl;r}GGSLH{t1>B}M%)B>d*-Sfvg zF78tupR2dBm6|JjD(0QGx|lTB+bG2ydwEQIFqdvjDU)Z(FhB~TF%h?GtxH;@kG9$s zuD2-+QoCqfC{@&M&+(>{vAwX2WPAl=ac)HNDVCHf8jDs2Y%sO3SrZQPe&_M@tms`A z*zmk4f^SUuO0l^S)I@Zv@MyAM74fD67Synyg~`+BJ^3-Kj2kG1?n(@s;KUk-u4IYf zu_o;5y(O-Etudu~Y~X;nWX^|I)}NxV##XQ~*F@`L$9c|*6^=PaHb0<(;LHsuR-GG3 zvzp|`QeNk7QtRXwZEBX)b4eM2meb}Bg(bUC$B=gKPh%QhWHWzQ{AHkgm}D>Qwa+)A z4Zq_^LQPfg94))NZNt9Xd}Q&XU5t=Pk1BiM*hy$7UU3ROMSEp}W2hsQ7ha}>4Q}E? 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+ journal = {BMC Genomics}, + year = 2014, + volume = 15, + number = 1, + pages = 264, + issn = {1471-2164}, + doi = {10.1186/1471-2164-15-264}, + url = {http://dx.doi.org/10.1186/1471-2164-15-264}, + publisher = {Springer Science and Business Media LLC} +} + +@Article{Weber_2020, + author = {Weber, Cédric R and Akbar, Rahmad and Yermanos, Alexander + and Pavlović, Milena and Snapkov, Igor and Sandve, Geir K and + Reddy, Sai T and Greiff, Victor}, + title = {immuneSIM: tunable multi-feature simulation of B- and + T-cell receptor repertoires for immunoinformatics + benchmarking}, + journal = {Bioinformatics}, + year = 2020, + editor = {Schwartz, RussellEditor}, + volume = 36, + number = 11, + month = {Apr}, + pages = {3594–3596}, + issn = {1460-2059}, + doi = {10.1093/bioinformatics/btaa158}, + url = {http://dx.doi.org/10.1093/bioinformatics/btaa158}, + publisher = {Oxford University Press (OUP)} +} + +@Article{Cock_2009, + author = {Cock, P. J. A. and Antao, T. and Chang, J. T. and Chapman, + B. A. and Cox, C. J. and Dalke, A. and Friedberg, I. and + Hamelryck, T. and Kauff, F. and Wilczynski, B. and et al.}, + title = {Biopython: freely available Python tools for computational + molecular biology and bioinformatics}, + journal = {Bioinformatics}, + year = 2009, + volume = 25, + number = 11, + month = {Mar}, + pages = {1422–1423}, + issn = {1460-2059}, + doi = {10.1093/bioinformatics/btp163}, + url = {http://dx.doi.org/10.1093/bioinformatics/btp163}, + publisher = {Oxford University Press (OUP)} +} + +@misc{tensorflow2015-whitepaper, + title = { {TensorFlow}: Large-Scale Machine Learning on + Heterogeneous Systems}, + url = {https://www.tensorflow.org/}, + note = {Software available from tensorflow.org}, + author = { Mart\'{\i}n~Abadi and Ashish~Agarwal and Paul~Barham and + Eugene~Brevdo and Zhifeng~Chen and Craig~Citro and + Greg~S.~Corrado and Andy~Davis and Jeffrey~Dean and + Matthieu~Devin and Sanjay~Ghemawat and Ian~Goodfellow and + Andrew~Harp and Geoffrey~Irving and Michael~Isard and Yangqing + Jia and Rafal~Jozefowicz and Lukasz~Kaiser and + Manjunath~Kudlur and Josh~Levenberg and Dandelion~Man\'{e} and + Rajat~Monga and Sherry~Moore and Derek~Murray and Chris~Olah + and Mike~Schuster and Jonathon~Shlens and Benoit~Steiner and + Ilya~Sutskever and Kunal~Talwar and Paul~Tucker and + Vincent~Vanhoucke and Vijay~Vasudevan and Fernanda~Vi\'{e}gas + and Oriol~Vinyals and Pete~Warden and Martin~Wattenberg and + Martin~Wicke and Yuan~Yu and Xiaoqiang~Zheng}, + year = 2015, +} + +@misc{Biostrings, + title = {Biostrings: Efficient manipulation of biological strings}, + author = {H. Pagès and P. Aboyoun and R. Gentleman and S. DebRoy}, + year = 2019, + note = {R package version 2.50.2}, +} diff --git a/assets/figures/pipeline.png b/assets/figures/pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..4b88af3dccde4602afb18416027d49974de44eef GIT binary patch literal 63796 zcmce-bySpH*glGh3Mh>th!WD>E!`k6ba!_QjUq9$baxKj9ZGk1OXrMq4*VYUec$h# z^{w?==dZID>iDqd*?ryjeO>ns{46Vm_5%L}5)u-cgt)K*64K*KB&3H0&mIC#y6p&9 zk&qtvnh6PgmJkwpZ)V-49UC-wf8dDs*2`cOi zK`4&86GQz8N+Js>C0&@0#m}GNodw^5D@$IM_`JV(u)4XKy-iGZOHWw^Ib5kDg2Q-W zOAiFI+Ca<;wAn5Bco!PR8Hh?0-f{Uho^LXl}*D4PPdq* zUZgZ}o_@!6;-&YgOI40T*ByGwk=UyatBl|1#BX|>W;fV}L;{|qzF}k9ab0C7SKZgG z|EAXaIWP}G5>z1H^4&@8S5v22@r3GL>WyDUQ%%KyQ)0WgE{2r2fNuzXU;YolXys6B zEVH?A_lp1m?;uU4`aa!t)lWE7q}{J>ky{&CQKA^xDY20ov@Nd<9zTOU9Bi=iCYpCX ze*ByNk&61fjujGfTK4q3`uel&`KNRx^NCH!nlPkqat(&!Ve5I6*e`xuzwuOj`MU0j z+tk96`Zb3q64Qjz(NhkN$D+Z}&9FllY-s70cYGI)ocGSY=_kABBCssaZN=3bkdWv| z?*BY+$>Dbf9wIwR$cP|sAU{TD!mR#%kOn+@<0zu)C}d-0Wn}G$BxG--=V)a39&F}l 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