doc/sourmash-examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some sourmash command line examples!\n",
    "\n",
    "[sourmash](https://sourmash.readthedocs.io/en/latest/) is research software from the Lab for Data Intensive Biology at UC Davis. It implements MinHash and modulo hash.\n",
    "\n",
    "Below are some examples of using sourmash. They are computed in a Jupyter Notebook so you can run them yourself if you like!\n",
    "\n",
    "Sourmash works on *signature files*, which are just saved collections of hashes.\n",
    "\n",
    "Let's try it out!\n",
    "\n",
    "### Running this notebook.\n",
    "\n",
    "You can run this notebook interactively via mybinder; click on this button:\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/master?filepath=doc%2Fsourmash-examples.ipynb)\n",
    "\n",
    "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n",
    "\n",
    "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/master/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/master/binder/environment.yml) for installation dependencies.\n",
    "\n",
    "### What is this?\n",
    "\n",
    "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n",
    "\n",
    "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute scaled signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Ksetting num_hashes to 0 because --scaled is set\n",
      "\u001b[Kcomputing signatures for files: genomes/akkermansia.fa, genomes/shew_os185.fa, genomes/shew_os223.fa\n",
      "\u001b[KComputing signature for ksizes: [21, 31, 51]\n",
      "\u001b[KComputing only nucleotide (and not protein) signatures.\n",
      "\u001b[KComputing a total of 3 signature(s).\n",
      "\u001b[K... reading sequences from genomes/akkermansia.fa\n",
      "\u001b[Kcalculated 3 signatures for 1 sequences in genomes/akkermansia.fa\n",
      "\u001b[Ksaved 3 signature(s). Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from genomes/shew_os185.fa\n",
      "\u001b[Kcalculated 3 signatures for 1 sequences in genomes/shew_os185.fa\n",
      "\u001b[Ksaved 3 signature(s). Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from genomes/shew_os223.fa\n",
      "\u001b[Kcalculated 3 signatures for 1 sequences in genomes/shew_os223.fa\n",
      "\u001b[Ksaved 3 signature(s). Note: signature license is CC0.\n"
     ]
    }
   ],
   "source": [
    "!rm -f *.sig\n",
    "!sourmash compute -k 21,31,51 --scaled=1000 genomes/*.fa --name-from-first -f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This outputs three signature files, each containing three signatures (one calculated at k=21, one at k=31, and one at k=51)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "akkermansia.fa.sig  shew_os185.fa.sig   shew_os223.fa.sig\r\n"
     ]
    }
   ],
   "source": [
    "ls *.sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use these signature files for various comparisons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search multiple signatures with a query\n",
    "\n",
    "The below command queries all of the signature files in the directory with the `shew_os223` signature and finds the best Jaccard similarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloaded query: NC_011663.1 Shewanella baltica... (k=31, DNA)\n",
      "\u001b[Kloaded 3 signatures.                                                           \n",
      "\n",
      "2 matches:\n",
      "similarity   match\n",
      "----------   -----\n",
      "100.0%       NC_011663.1 Shewanella baltica OS223, complete genome\n",
      " 22.8%       NC_009665.1 Shewanella baltica OS185, complete genome\n"
     ]
    }
   ],
   "source": [
    "!sourmash search -k 31 shew_os223.fa.sig *.sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below command uses Jaccard containment instead of Jaccard similarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloaded query: NC_011663.1 Shewanella baltica... (k=31, DNA)\n",
      "\u001b[Kloaded 3 signatures.                                                           \n",
      "\n",
      "2 matches:\n",
      "similarity   match\n",
      "----------   -----\n",
      "100.0%       NC_011663.1 Shewanella baltica OS223, complete genome\n",
      " 37.3%       NC_009665.1 Shewanella baltica OS185, complete genome\n"
     ]
    }
   ],
   "source": [
    "!sourmash search -k 31 shew_os223.fa.sig *.sig --containment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing all-by-all queries\n",
    "\n",
    "We can also compare all three signatures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloaded 3 signatures total.                                                     \n",
      "\u001b[Kdownsampling to scaled value of 1000\n",
      "\u001b[K\n",
      "0-CP001071.1 Akke...\t[1. 0. 0.]\n",
      "1-NC_009665.1 She...\t[0.    1.    0.228]\n",
      "2-NC_011663.1 She...\t[0.    0.228 1.   ]\n",
      "min similarity in matrix: 0.000\n"
     ]
    }
   ],
   "source": [
    "!sourmash compare -k 31 *.sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and produce a similarity matrix that we can use for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloaded 3 signatures total.                                                     \n",
      "\u001b[Kdownsampling to scaled value of 1000\n",
      "\u001b[K\n",
      "0-CP001071.1 Akke...\t[1. 0. 0.]\n",
      "1-NC_009665.1 She...\t[0.    1.    0.228]\n",
      "2-NC_011663.1 She...\t[0.    0.228 1.   ]\n",
      "min similarity in matrix: 0.000\n",
      "\u001b[Ksaving labels to: genome_compare.mat.labels.txt\n",
      "\u001b[Ksaving distance matrix to: genome_compare.mat\n"
     ]
    }
   ],
   "source": [
    "!sourmash compare -k 31 *.sig -o genome_compare.mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloading comparison matrix from genome_compare.mat...\n",
      "\u001b[K...got 3 x 3 matrix.\n",
      "\u001b[Kloading labels from genome_compare.mat.labels.txt\n",
      "\u001b[Ksaving histogram of matrix values => genome_compare.mat.hist.png\n",
      "\u001b[Kwrote dendrogram to: genome_compare.mat.dendro.png\n",
      "\u001b[Kwrote numpy distance matrix to: genome_compare.mat.matrix.png\n",
      "0\tCP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome\n",
      "1\tNC_009665.1 Shewanella baltica OS185, complete genome\n",
      "2\tNC_011663.1 Shewanella baltica OS223, complete genome\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!sourmash plot genome_compare.mat\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename='genome_compare.mat.matrix.png') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and for the R aficionados, you can output a CSV version of the matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloaded 3 signatures total.                                                     \n",
      "\u001b[Kdownsampling to scaled value of 1000\n",
      "\u001b[K\n",
      "0-CP001071.1 Akke...\t[1. 0. 0.]\n",
      "1-NC_009665.1 She...\t[0.    1.    0.228]\n",
      "2-NC_011663.1 She...\t[0.    0.228 1.   ]\n",
      "min similarity in matrix: 0.000\n"
     ]
    }
   ],
   "source": [
    "!sourmash compare -k 31 *.sig --csv genome_compare.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome\",\"NC_009665.1 Shewanella baltica OS185, complete genome\",\"NC_011663.1 Shewanella baltica OS223, complete genome\"\r",
      "\r\n",
      "1.0,0.0,0.0\r\n",
      "0.0,1.0,0.22846441947565543\r\n",
      "0.0,0.22846441947565543,1.0\r\n"
     ]
    }
   ],
   "source": [
    "!cat genome_compare.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is now a file that you can load into R and examine - see [our documentation](https://sourmash.readthedocs.io/en/latest/other-languages.html) on that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## working with metagenomes\n",
    "\n",
    "Let's make a fake metagenome:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Ksetting num_hashes to 0 because --scaled is set\n",
      "\u001b[Kcomputing signatures for files: fake-metagenome.fa\n",
      "\u001b[KComputing signature for ksizes: [31]\n",
      "\u001b[KComputing only nucleotide (and not protein) signatures.\n",
      "\u001b[KComputing a total of 1 signature(s).\n",
      "\u001b[K... reading sequences from fake-metagenome.fa\n",
      "\u001b[Kcalculated 1 signatures for 3 sequences in fake-metagenome.fa\n",
      "\u001b[Ksaved 1 signature(s). Note: signature license is CC0.\n"
     ]
    }
   ],
   "source": [
    "!cat genomes/*.fa > fake-metagenome.fa\n",
    "!sourmash compute -k 31 --scaled=1000 fake-metagenome.fa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `sourmash gather` command to see what's in it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K== This is sourmash version 2.0.0a12.dev48+ga92289b. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kselect query k=31 automatically.\n",
      "\u001b[Kloaded query: fake-metagenome.fa... (k=31, DNA)\n",
      "\u001b[Kloaded 3 signatures.                                                           \n",
      "\n",
      "\n",
      "overlap     p_query p_match\n",
      "---------   ------- -------\n",
      "499.0 kbp     38.4%  100.0%    CP001071.1 Akkermansia muciniphila AT...\n",
      "494.0 kbp     38.0%  100.0%    NC_009665.1 Shewanella baltica OS185,...\n",
      "490.0 kbp     23.6%   62.7%    NC_011663.1 Shewanella baltica OS223,...\n",
      "\n",
      "found 3 matches total;\n",
      "the recovered matches hit 100.0% of the query\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!sourmash gather fake-metagenome.fa.sig shew*.sig akker*.sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other pointers\n",
    "\n",
    "[Sourmash: a practical guide](https://sourmash.readthedocs.io/en/latest/using-sourmash-a-guide.html)\n",
    "\n",
    "[Classifying signatures taxonomically](https://sourmash.readthedocs.io/en/latest/classifying-signatures.html)\n",
    "\n",
    "[Pre-built search databases](https://sourmash.readthedocs.io/en/latest/databases.html)\n",
    "\n",
    "## A full list of notebooks\n",
    "\n",
    "[An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb)\n",
    "\n",
    "[Some sourmash command line examples!](sourmash-examples.ipynb)\n",
    "\n",
    "[Working with private collections of signatures.](sourmash-collections.ipynb)\n",
    "\n",
    "[Using the LCA_Database API.](using-LCA-database-API.ipynb)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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