diff --git a/report.org b/report.org index 12bfef5..cd44802 100644 --- a/report.org +++ b/report.org @@ -9,13 +9,8 @@ #+exclude_tags: noexport #+options: H:7 #+options: toc:nil -#+MACRO: conditional-header (eval (concat "#+header: :results output " (print-to-string org-export-current-backend))) -# ref. conditional-header https://emacs.stackexchange.com/a/64340/41374 - - # ref. https://write.as/dani/writing-a-phd-thesis-with-org-mode - #+name: acronyms | key | abbreviation | full form | |------------+--------------+--------------------------------------------| @@ -53,9 +48,12 @@ #+end_center #+begin_export latex +{ +\hypersetup{linkcolor=black} \tableofcontents \listoffigures \listoftables +} #+end_export [[printglossaries:]] @@ -154,19 +152,21 @@ In this step, the typical tool involved is =BLAST= (Basic Local Alignment Search Several =BLAST= metrics can be used as an homology measure, such as bitscore, identity percentage, E-value or variations of these. The choice of metrics can affect the results of graph clustering in the following step, and we should therefore chose them carefully [cite:@gibbonsEvaluationBLASTbasedEdgeweighting2015]. **** Identification of gene families -Based on the homology links between each pair of genes, we construct a undirected weighted graph whose vertices correspond to genes and edges to homology links between them. +Based on the homology links between each pair of genes, we construct an undirected weighted graph whose vertices correspond to genes and edges to homology links between them. We apply a graph clustering algorithm on the graph in order to infer the gene families corresponding to densely connected communities of vertices. -FTAG Finder proposes three clustering algorithm alternatives: single linkage, Markov Clustering [cite:@vandongenNewClusterAlgorithm1998] or Walktrap [cite:@ponsComputingCommunitiesLarge2005]. -**** Detection of TAGs -The final step of FTAG Finder consists in the identification of gls:TAG from the gene families and the positions of genes. -For a given chromosome, the tool seeks genes belonging to the same family and located close to each other. The tool allows a maximal number of genes between the homologous genes, with a parameter set by the user. Ref:fig:tag-definitions is a schematic representation of some possible gls:TAG positioning on a genome associated with their definition in FTAG Finder /Find Tags/ step. #+begin_export latex \fladdfig{ \includegraphics[width=.9\linewidth]{./figures/tag-definition.pdf} \caption[Schematic representation of TAG definitions]{\label{fig:tag-definitions} Schematic representation of TAG definitions. Several genes are represented on a linear chromosome. The red box represent a singleton gene. Orange boxes represent a TAG with two duplicate genes seperated by 7 other genes ($\mathrm{TAG}_7$). Four green boxes constitute a TAG, the gene at the extremities are seperated by three genes ($\mathrm{TAG}_3$. The two blue boxes represents a TAG with two genes next to each other $\mathrm{TAG}_0$. The bended edges represents the homology links between each pair of genes of a TAG.}} #+end_export + +FTAG Finder proposes three clustering algorithm alternatives: single linkage, Markov Clustering [cite:@vandongenNewClusterAlgorithm1998] or Walktrap [cite:@ponsComputingCommunitiesLarge2005]. +**** Detection of TAGs +The final step of FTAG Finder consists in the identification of gls:TAG from the gene families and the positions of genes. +For a given chromosome, the tool seeks genes belonging to the same family and located close to each other. The tool allows a maximal number of genes between the homologous genes, with a parameter set by the user. Cref:fig:tag-definitions is a schematic representation of some possible gls:TAG positioning on a genome associated with their definition in FTAG Finder /Find Tags/ step. + * Objectives for the internship ** Scientific questions The underlying question of FTAG Finder is the study of the evolutionary fate of duplicate genes in Eukaryotes. @@ -216,6 +216,10 @@ Principle: construct vertex communities based on where an agent would get stuck # LocalWords: speciation Subfunctionalization Neofunctionalization # LocalWords: Pseudogenization +# Local Variables: +# eval: (progn (org-babel-goto-named-src-block "startup") (org-babel-execute-src-block) (outline-hide-sublevels 1)) +# End: + * Setup :noexport: #+name: startup @@ -225,7 +229,3 @@ Principle: construct vertex communities based on where an agent would get stuck #+RESULTS: startup : Loaded ./setup.el - -# Local Variables: -# eval: (progn (org-babel-goto-named-src-block "startup") (org-babel-execute-src-block) (outline-hide-sublevels 1)) -# End: diff --git a/report.pdf b/report.pdf index e8b614d..446230e 100644 --- a/report.pdf +++ b/report.pdf @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7e81782fdc5a634f39286860e802b7cafa7581f2db4925ff9041a3cd220f0cbc -size 193053 +oid sha256:0aad7b5d59abf2860c3b7502c18047f29f8eb5538d4e162f30568a3d6521e5a6 +size 193201 diff --git a/sty/lamme2024.sty b/sty/lamme2024.sty index 656d8a1..6ce047c 100644 --- a/sty/lamme2024.sty +++ b/sty/lamme2024.sty @@ -84,7 +84,7 @@ linkcolor=primaryLink, anchorcolor=primaryLink, citecolor=primaryCite, - linktoc=page + %linktoc=page } \newcommand*{\glsplainhyperlink}[2]{% \begingroup%