{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "Alignment plots\n",
    "===============\n",
    "\n",
    "Alignment plots are a quick way to visualize how RNAvigate is positionally\n",
    "aligning two data sets. The two sequences will be displayed along the x-axis,\n",
    "with dashes inserted where there are insertions/deletions. A simplified\n",
    "visualization of the alignment appears between the sequences, with grey bars\n",
    "indicating the sequence agreement. The x-axes show the positional alignment\n",
    "used by RNAvigate to compare sequences.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rnavigate as rnav\n",
    "from rnavigate.examples import rnasep_1, rmrp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two structure models differ only in that one contains structure cassettes\n",
    "on the 5' and 3' ends.\n",
    "\n",
    "This is what a good alignment looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1509.2x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = rnav.plot_alignment(\n",
    "    data1=(rnasep_1, \"ss_ct\"),\n",
    "    data2=(rnasep_1, \"ss_pdb\"),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RNAse P and RNAse MRP have structural conservation, but their sequences diverge substantially.\n",
    "\n",
    "Positions will be aligned even though there is very little sequence similarity\n",
    "\n",
    "This is what a bad alignment looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1366.4x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = rnav.plot_alignment(\n",
    "    data1=(rmrp, \"ss\"),\n",
    "    data2=(rnasep_1, \"ss_pdb\"),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot_alignment in module rnavigate.plotting_functions:\n",
      "\n",
      "plot_alignment(data1, data2, labels=None, plot_kwargs=None)\n",
      "    Plots the sequence alignment used to compare two sequences\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    data1 : tuple (rnavigate Sample, data keyword string)\n",
      "        a sample and data keyword to retrieve a sequence\n",
      "    data2 : tuple (rnavigate Sample, data keyword string)\n",
      "        another sample and data keyword to retrieve a second sequence\n",
      "    labels : list of 2 strings, defaults to \"sample.sample: data keyword\" for each\n",
      "        Labels used for each sample\n",
      "    plot_kwargs : dict, defaults to {}\n",
      "        passed to matplotlib.pyplot.subplots()\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    rnavigate.plots.Alignment\n",
      "        the Alignment plot object\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(rnav.plot_alignment)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "RNAvigate",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
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