{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some common tools used in microbial genomic data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of microbial genomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are various tools and techniques for visualizing microbial genomes. Take for example, this figure from a paper I published in [2013](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0076376).\n",
    "\n",
    "![Genome Circle](plos_one_circle.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This figure was created using a tool known as **Circos**. See here: http://circos.ca/\n",
    "\n",
    "If you visit this page, you can see beautiful pictures of various circular drawings produced using this tool. See here for examples of published images created with this tool: http://circos.ca/intro/published_images/\n",
    "\n",
    "They have highlighted the figure I created on this page as well. This tool has been used not only in genomics but other fields such as economics and political science, for example. This tool has a pretty steep learning curve and it will take a bit of time to learn to use this tool. However, it comes with various tutorials and example data that you can start with. Then you can learn to adapt it to visualize your own data.\n",
    "\n",
    "You can visit this page (http://circos.ca/documentation/) to download Circos tutorials to try their examples. Circos is available through Bioconda so you should be able to install it using the `conda` command.\n",
    "\n",
    "Circos has been adapted into R packages as well. See [here](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-244) and [here](https://cran.r-project.org/web/packages/BioCircos/vignettes/BioCircos.html). A few known tools similar to or related to the original Circos are, [R Circos](https://cran.r-project.org/web/packages/RCircos/), [BioCircos](https://cran.r-project.org/web/packages/BioCircos/vignettes/BioCircos.html), and [Circlize](https://jokergoo.github.io/circlize_book/book/). R versions of these tools make it a lot easier for you to create circular visualizations of microbial genomes, especially those packages with clear examples to follow. I highly recommend you check out the circlize R package as it provides very detailed instructions and examples to follow.\n",
    "\n",
    "We will not be learning to use all these tools in class. This is for you to explore yourself and improve your skills in biological sequence analysis and data visualization. I am just showing you that these are useful tools for various reasons. You can definitely go on your own and start learning how to use these tools for your research projects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of gene segments or clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about figures like this one shown below, from [a study](https://pubmed.ncbi.nlm.nih.gov/33115833/) to identify a genomic region that might have been horizontally transferred from another organism?\n",
    "\n",
    "![gene clusters](gene_clusters_ex.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, figures like these shown are very informative as they can display the protein-coding regions in genomes and you can easily see if certain genes are conserved in one or more organisms. They are also useful to display genome rearrangements that may have occurred during the course of their genome evolution. \n",
    "\n",
    "This figure was created using the tool known as **EasyFig**. The paper describing this tool was published here: https://pubmed.ncbi.nlm.nih.gov/21278367/\n",
    "\n",
    "You can download this tool here: https://sourceforge.net/projects/easyfig/\n",
    "\n",
    "You can also use other tools such as [genoPlotR](http://genoplotr.r-forge.r-project.org/), which you have used a few weeks ago. `genoPlotR` provides many additional useful features such as being able to display phylogenetic trees to go along with figures of gene clusters or genomic regions. However, it is not an easy tool to learn to use and may require several hours of learning to use its R functions and commands.\n",
    "\n",
    "Another tool that just came out in recent years to compare and visualize syntenic gene clusters is known as **Clinker**. \n",
    "\n",
    "https://github.com/gamcil/clinker\n",
    "\n",
    "This tool is written in a completely different programming language: Javascript. It can draw similar figures to **genoPlotR** as you can see here:\n",
    "\n",
    "![](images/clinker.png)\n",
    "\n",
    "Again, be mindful that this tool is designed to display only a short segment of the genomes of two or more very similar organisms. You should not try to align whole genomes and visualize them as it may not work with this tool."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phylogenetic trees visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about display phylogenetic trees and associated information? You have already learned to use **FigTree**, a graphical tool to display phylogenetic trees and to save output PDF or image files from its display. Although this tool is quite useful, sometimes, you may have hundreds or thousands of taxa that may not display well on FigTree. In those cases, you will sometimes require to use more specialized tree-viewing tool or software. \n",
    "\n",
    "Take for example, look at this tree below from a paper [I published in 2020](https://mbio.asm.org/content/11/1/e02975-19.long).\n",
    "\n",
    "![mBio figure](mbio_figure.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These trees contain thousands of taxa and will not display well on FigTree. For trees like these, you may want to use a tool known as **iTOL**. See here (https://itol.embl.de/) to visit this web-based tool. This tool can display thousands of tree branches as shown and it provides many useful features such as  heatmaps, charts, or graphs to go along with the phylogenetic tree. It will also allow you to export the high-quality vector drawing of these tree figures into formats such as SVG, PDF, or EPS, so that you can use other tools like Adobe Illustrator or Inkscape to touch up and improve the figures for publication.\n",
    "\n",
    "Sometimes, you need to do more than just displaying phylogenetic trees. You can use phylogenetic/phylogenomic trees to detect horizontal gene transfer events, measure distances between clades of interest (for example, to understand their evolutionary distances), or even perform statistical tests to prove/disprove your hypotheses. For those purposes, you cannot just use a simple phylogenetic displaying tool like FigTree or even iTOL. You need more programmatic tools. Some of these tools are available in both Python and R languages. \n",
    "\n",
    "In Python, there is a tool known as **ETE Toolkit** (see here: http://etetoolkit.org/). It is a Python framework for analysis and visualization of phylogenetic trees. If you visit this page, you will see that the tool can not only display beautiful trees but it can also annotate trees, compare tree topologies, test hypotheses, etc. It provides command-line tools but also a Python library that you can use to develop your own Python scripts to display phylogenetic trees. Python code and tutorial (with examples) can be found here: http://etetoolkit.org/docs/latest/tutorial/index.html\n",
    "\n",
    "For example, I can use ETE Python library and display a tree within the Jupyter environment. See examples below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ete3 import Tree, TreeStyle\n",
    "\n",
    "t = Tree( \"((a,b),c);\" )\n",
    "circular_style = TreeStyle()\n",
    "circular_style.mode = \"c\" # draw tree in circular mode\n",
    "circular_style.scale = 20\n",
    "\n",
    "t.render(\"%%inline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ete3 import Tree, TreeStyle\n",
    "t = Tree()\n",
    "t.populate(30)\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = True\n",
    "ts.mode = \"c\"\n",
    "ts.arc_start = -180 # 0 degrees = 3 o'clock\n",
    "ts.arc_span = 180\n",
    "t.render(\"%%inline\", tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, in just a few simple code, you can display tree structures very easily and without having to mess around with things manually. Tools like ETE Toolkit are very useful if you have thousands of individual trees to display and it would not be feasible to use a graphical tool like FigTree to draw each of the tree figure. \n",
    "\n",
    "Besides ETE Toolkit, there is another Python-based tool known as **Dendropy** (See here: https://dendropy.org/). Dendropy is more of a utility tool for phylogenetic tree analyses rather than for displaying publication-quality tree figures. \n",
    "\n",
    "In R, there are several tools that allow you to do similar kinds of analyses but also display beautiful tree figures. One of the most well-known R packages is known as **Ape**, which stands for \"Analysis of Phylogenetics and Evolution\". You can find it [here](http://ape-package.ird.fr/) and [here](https://cran.r-project.org/web/packages/ape/index.html).\n",
    "\n",
    "Books have been written about this tool and you can see one of the books here: \n",
    "\n",
    "http://ape-package.ird.fr/APER.html\n",
    "\n",
    "Another, more recent, R package is known as **ggtree**. See here: \n",
    "\n",
    "https://guangchuangyu.github.io/software/ggtree/\n",
    "\n",
    "The paper describing ggtree can be found [here](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12628). It is similar to ETE Toolkit in terms of the functionality and features it provides, though it is mostly a visualization tool. You can even use it together with other tools such as `phyloseq` you used a few weeks ago to display trees and associated features such as abundances of certain taxa. \n",
    "\n",
    "For more detailed examples and use cases, see this website: \n",
    "\n",
    "https://guangchuangyu.github.io/ggtree-book/chapter-ggtree.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, to reiterate my points made earlier, these are just a few examples of some visualization tools and techniques available and they are by no means comprehensive. Bioinformatic tools are being developed and published every day and some of the tools mentioned here today may become obsolete in a few months. This is the nature of how quickly this field is moving forward. So I would highly encourage you to explore new tools and try to use them yourself and I hope that at least some of the tools you have learned to use in this course will become useful for your own research in the near future."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
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}