examples/varcode-quick_start.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Varcode - Quick Start\n",
    "\n",
    "`Varcode` is a library for working with genomic variant data in Python and predicting the impact of those variants on protein sequences.\n",
    "What this means is that this library can help you annotate gene variants, *i.e.* changes in DNA with their potential effect on the protein/transcript that they encode for.\n",
    "This notebook highlights the basic functionality provided by `varcode` in a nut-shell.\n",
    "If you haven't installed `varcode` yet, \n",
    "please refer to the original [README](https://github.com/hammerlab/varcode) file for specific instructions on how to install the library.\n",
    "\n",
    "## Working with variants and their effects\n",
    "Let's start with a really simple example to demonstrate what `varcode` can do for you.\n",
    "Imagine that the sequencing core facility has just sent you the sequencing results of that sample you submitted earlier to them.\n",
    "And now, they sent you a list of variants that they inferred using their analysis pipeline.\n",
    "You open the file just to see that they haven't annotated the variants,\n",
    "so you don't know whether these variants are within a gene\n",
    "and if so whether they affect the protein sequence or not.\n",
    "Enter `varcode`, which is specifically designed to solve this annotation problem.\n",
    "\n",
    "For the sake of the example,\n",
    "let's simplify things and assume that we are interested in annotating this variant, \n",
    "a nucleotide change from an `A` into a `T` at the `1,404,553,136`th base of `chromosome 7`.\n",
    "To annotate this variant, we first have to model it within `varcode` using the `Variant` class.\n",
    "We are going to assume that this coordinate is consistent with the human assembly [GRCh37](http://grch37.ensembl.org/index.html).\n",
    "`Varcode` stands on the shoulders of [`PyEnsembl`](https://github.com/hammerlab/pyensembl), a Python interface to Ensembl reference genome metadata.\n",
    "\n",
    "So to start things off, let's import the following classes that are relevant to our example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from varcode import Variant\n",
    "from pyensembl import ensembl_grch37"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a new `Variant` that will represent our variant of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myVariant = Variant(contig=7, start=140453136, ref=\"A\", alt=\"T\", ensembl=ensembl_grch37)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we defined this variant,\n",
    "we can start annotating it;\n",
    "but let's start with this trivial example,\n",
    "where we ask for a short descriptive description of the variant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chr7 g.140453136A>T'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myVariant.short_description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this is our variation, but expressed using the offical variation nomenclature.\n",
    "\n",
    "How about asking about the gene this variant is in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Cached file Homo_sapiens.GRCh37.75.gtf from URL ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/Homo_sapiens.GRCh37.75.gtf.gz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Gene(id=ENSG00000157764, name=BRAF, biotype=protein_coding, location=7:140419127-140624564)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myVariant.coding_genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like this variant lies within the [BRAF gene](http://grch37.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000157764;r=7:140419127-140624564);\n",
    "but what about the potential effects of this variant to the product of this gene?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Cached file Homo_sapiens.GRCh37.75.cdna.all.fa from URL ftp://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh37.75.cdna.all.fa.gz\n",
      "INFO:root:Cached file Homo_sapiens.GRCh37.75.pep.all.fa from URL ftp://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/pep/Homo_sapiens.GRCh37.75.pep.all.fa.gz\n",
      "/usr/local/lib/python3.4/site-packages/Bio/Seq.py:151: BiopythonWarning: Biopython Seq objects now use string comparison. Older versions of Biopython used object comparison. During this transition, please use hash(id(my_seq)) or my_dict[id(my_seq)] if you want the old behaviour, or use hash(str(my_seq)) or my_dict[str(my_seq)] for the new string hashing behaviour.\n",
      "  \"the new string hashing behaviour.\", BiopythonWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<EffectCollection with 4 elements>\n",
       "  -- Substitution(variant=chr7 g.140453136A>T, transcript_name=BRAF-001, transcript_id=ENST00000288602, effect_description=p.V600E)\n",
       "  -- IncompleteTranscript(variant=chr7 g.140453136A>T, transcript_name=BRAF-005, transcript_id=ENST00000479537)\n",
       "  -- IncompleteTranscript(variant=chr7 g.140453136A>T, transcript_name=BRAF-003, transcript_id=ENST00000496384)\n",
       "  -- IncompleteTranscript(variant=chr7 g.140453136A>T, transcript_name=BRAF-002, transcript_id=ENST00000497784)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myEffects = myVariant.effects()\n",
    "myEffects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What the above list tells us is that this variation can potentially affect four different **BRAF** transcripts\n",
    "and out of four, one will result in a `Substitution`, \n",
    "*i.e.* a coding mutation which causes simple substitution of one amino acid for another.\n",
    "For the other transcripts, `IncompleteTranscript` type implies that \n",
    "`varcode` can't determine effect since transcript annotation is incomplete (often missing either the start or stop codon).\n",
    "\n",
    "That is all great, but dealing with multiple effects is hard and we usually want to pick the one that causes the most dramatic change in the gene product.\n",
    "`Varcode` provides an easy way to get to this **worst effect**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Substitution(variant=chr7 g.140453136A>T, transcript_name=BRAF-001, transcript_id=ENST00000288602, effect_description=p.V600E)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topPriorityEffect = myEffects.top_priority_effect()\n",
    "topPriorityEffect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So overall, this what we have learned about the variant using `varcode`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mutation chr7 g.140453136A>T leads to a Substitution in gene BRAF (p.V600E)\n"
     ]
    }
   ],
   "source": [
    "print ('The mutation %s leads to a %s in gene %s (%s)' % (myVariant.short_description, type(topPriorityEffect).__name__, topPriorityEffect.gene_name, topPriorityEffect.short_description))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, `varcode` can provide you with the altered protein sequence,\n",
    "which becomes important especially for analyses that use the variation information within the context of a few aminoacids surrounding the mutated location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('LATVKSR', SingleLetterAlphabet())"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variantLocation = topPriorityEffect.aa_mutation_start_offset\n",
    "topPriorityEffect.original_protein_sequence[variantLocation-3:variantLocation+4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('LATEKSR', SingleLetterAlphabet())"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topPriorityEffect.mutant_protein_sequence[variantLocation-3:variantLocation+4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See that valenine (`V`) changing into a glutamine (`E`)?\n",
    "That is the effect of our variant within the aminoacid context.\n",
    "That was easy, right?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing variants from a MAF or VCF file\n",
    "Now that we know how to work with a single variant\n",
    "and extract annotations for it,\n",
    "it is now time for us to learn how to work with collections of variants all together.\n",
    "\n",
    "In the previous section, \n",
    "we assumed that there was a single variation that we wanted to annotate;\n",
    "but in the real world,\n",
    "we usually receive the variant calls from a sequencing analysis as a file\n",
    "that is formatted with either [Mutation Annotation Format](https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+(MAF)+Specification) (MAF) or [Variant Call Format](http://www.1000genomes.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-format-version-40) (VCF).\n",
    "`Varcode` has built-in methods to load variants described by either of the formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from varcode import load_maf, load_vcf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following loads mutations from the MAF file distributed within [our code repository](https://github.com/hammerlab/pyensembl/tree/master/test/data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VariantCollection from 'tcga_ov.head.maf' with 4 elements>\n",
       "  -- Variant(contig=1, start=1650797, ref=A, alt=G, genome=GRCh37)\n",
       "  -- Variant(contig=1, start=23836447, ref=C, alt=A, genome=GRCh37)\n",
       "  -- Variant(contig=1, start=231401797, ref=A, alt=C, genome=GRCh37)\n",
       "  -- Variant(contig=11, start=124617502, ref=C, alt=G, genome=GRCh37)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mafVariants = load_maf(\"../test/data/tcga_ov.head.maf\")\n",
    "mafVariants  # should load 4 variants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and this should load variants from one of the VCF files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VariantCollection from 'somatic_hg19_14muts.vcf' with 14 elements>\n",
       "  -- Variant(contig=1, start=53513530, ref=A, alt=C, genome=GRCh37)\n",
       "  -- Variant(contig=1, start=228295398, ref=G, alt=T, genome=GRCh37)\n",
       "  -- Variant(contig=10, start=49658590, ref=T, alt=C, genome=GRCh37)\n",
       "  -- Variant(contig=10, start=51585166, ref=G, alt=T, genome=GRCh37)\n",
       "  -- Variant(contig=10, start=96709040, ref=A, alt=C, genome=GRCh37)\n",
       "  -- Variant(contig=10, start=119134281, ref=G, alt=T, genome=GRCh37)\n",
       "  -- Variant(contig=11, start=118244286, ref=., alt=., genome=GRCh37)\n",
       "  -- Variant(contig=12, start=14794076, ref=C, alt=A, genome=GRCh37)\n",
       "  -- Variant(contig=12, start=25398284, ref=C, alt=G, genome=GRCh37)\n",
       "  -- Variant(contig=12, start=42778752, ref=T, alt=A, genome=GRCh37)\n",
       "  -- Variant(contig=14, start=31144202, ref=A, alt=C, genome=GRCh37)\n",
       "  -- Variant(contig=16, start=25704209, ref=G, alt=A, genome=GRCh37)\n",
       "  -- Variant(contig=17, start=7577548, ref=., alt=A, genome=GRCh37)\n",
       "  -- Variant(contig=17, start=36731197, ref=C, alt=AAT, genome=GRCh37)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcfVariants = load_vcf(\"../test/data/somatic_hg19_14muts.vcf\")\n",
    "vcfVariants  # should load 14 variants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can even extract summary statistics from these variant collections\n",
    "if you would like to have a quick look at the overall variants and the altered genes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'SCP2': 1, 'HS3ST4': 1, 'SCFD1': 1, 'TP53': 1, 'GUCY2C': 1, 'CYP2C9': 1, 'PDZD8': 1, 'MRPL55': 1, 'NCOA4': 1, 'UBE4A': 1, 'SRCIN1': 1, 'ARHGAP22': 1, 'KRAS': 1, 'PPHLN1': 1})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcfVariants.gene_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Cached file Homo_sapiens.GRCh37.75.gtf from URL ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/Homo_sapiens.GRCh37.75.gtf.gz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'VSIG2': 1, 'GNPAT': 1, 'E2F2': 1, 'RP1-283E3.8': 1, 'CDK11A': 1})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mafVariants.gene_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering Variants\n",
    "Having a collection of variants is great,\n",
    "but you often need to filter them down to see, for example, whether any of the variants affect your gene of interest (**TP53**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EffectCollection with 11 elements>\n",
       "  -- FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-001, transcript_id=ENST00000269305, effect_description=p.G245fs)\n",
       "  -- FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-019, transcript_id=ENST00000359597, effect_description=p.G245fs)\n",
       "  -- FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-018, transcript_id=ENST00000413465, effect_description=p.G245fs)\n",
       "  -- FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-005, transcript_id=ENST00000420246, effect_description=p.G245fs)\n",
       "  -- FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-002, transcript_id=ENST00000445888, effect_description=p.G245fs)\n",
       "  -- FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-004, transcript_id=ENST00000455263, effect_description=p.G245fs)\n",
       "  -- NoncodingTranscript(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-006, transcript_id=ENST00000504290)\n",
       "  -- NoncodingTranscript(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-008, transcript_id=ENST00000504937)\n",
       "  -- IncompleteTranscript(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-015, transcript_id=ENST00000509690)\n",
       "  -- NoncodingTranscript(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-007, transcript_id=ENST00000510385)\n",
       "  -- IncompleteTranscript(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-013, transcript_id=ENST00000514944)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tp53Mutations = vcfVariants.groupby_gene_name()[\"TP53\"].effects()\n",
    "tp53Mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or, for example, you might want to drop all mutations that do not affect a protein sequence or variants that fall in a non-coding genomic region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/site-packages/Bio/Seq.py:151: BiopythonWarning: Biopython Seq objects now use string comparison. Older versions of Biopython used object comparison. During this transition, please use hash(id(my_seq)) or my_dict[id(my_seq)] if you want the old behaviour, or use hash(str(my_seq)) or my_dict[str(my_seq)] for the new string hashing behaviour.\n",
      "  \"the new string hashing behaviour.\", BiopythonWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('ENSG00000110344', Insertion(variant=chr11 g.118244286, transcript_name=UBE4A-002, transcript_id=ENST00000431736, effect_description=p.340ins)), ('ENSG00000165650', Substitution(variant=chr10 g.119134281G>T, transcript_name=PDZD8-001, transcript_id=ENST00000334464, effect_description=p.T153K)), ('ENSG00000162910', Substitution(variant=chr1 g.228295398G>T, transcript_name=MRPL55-201, transcript_id=ENST00000366731, effect_description=p.R103S)), ('ENSG00000017373', FrameShift(variant=chr17 g.36731197C>AAT, transcript_name=SRCIN1-002, transcript_id=ENST00000578925, effect_description=p.V117fs)), ('ENSG00000128805', Substitution(variant=chr10 g.49658590T>C, transcript_name=ARHGAP22-002, transcript_id=ENST00000417912, effect_description=p.S544G)), ('ENSG00000092108', Substitution(variant=chr14 g.31144202A>C, transcript_name=SCFD1-006, transcript_id=ENST00000458591, effect_description=p.E391D)), ('ENSG00000138293', Substitution(variant=chr10 g.51585166G>T, transcript_name=NCOA4-205, transcript_id=ENST00000452682, effect_description=p.C438F)), ('ENSG00000141510', FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-001, transcript_id=ENST00000269305, effect_description=p.G245fs)), ('ENSG00000070019', Substitution(variant=chr12 g.14794076C>A, transcript_name=GUCY2C-001, transcript_id=ENST00000261170, effect_description=p.A670S)), ('ENSG00000133703', Substitution(variant=chr12 g.25398284C>G, transcript_name=KRAS-004, transcript_id=ENST00000256078, effect_description=p.G12A))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcfEffects = vcfVariants.effects()\n",
    "nonSilentMutations = vcfEffects.drop_silent_and_noncoding()\n",
    "nonSilentMutations.top_priority_effect_per_gene_id()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or, you might want to get all effects whose priority falls below an `Insertion`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('ENSG00000110344', Insertion(variant=chr11 g.118244286, transcript_name=UBE4A-002, transcript_id=ENST00000431736, effect_description=p.340ins)), ('ENSG00000138109', ExonicSpliceSite(exon=Exon(exon_id=ENSE00002485068, gene_name=CYP2C9, contig=10, start=96708865, end=96709041), alternate_effect=Substitution(variant=chr10 g.96709040A>C, transcript_name=CYP2C9-001, transcript_id=ENST00000260682, effect_description=p.K273T))), ('ENSG00000141510', FrameShift(variant=chr17 g.7577548_7577549insA, transcript_name=TP53-001, transcript_id=ENST00000269305, effect_description=p.G245fs)), ('ENSG00000017373', FrameShift(variant=chr17 g.36731197C>AAT, transcript_name=SRCIN1-002, transcript_id=ENST00000578925, effect_description=p.V117fs)), ('ENSG00000116171', ExonicSpliceSite(exon=Exon(exon_id=ENSE00003489518, gene_name=SCP2, contig=1, start=53513530, end=53513609), alternate_effect=Substitution(variant=chr1 g.53513530A>C, transcript_name=SCP2-001, transcript_id=ENST00000371514, effect_description=p.D490A)))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from varcode import Insertion\n",
    "vcfEffects.filter_by_effect_priority(Insertion).top_priority_effect_per_gene_id()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "Here, we went over how you can `varcode` to annotate variants (either one-by-one or as a batch from a collection)\n",
    "and filter their effects based on biological criteria.\n",
    "For more examples on methods provided by `varcode`,\n",
    "we suggest taking a look at our code tests.\n",
    "A significant portion of the `varcode` terminology is inherited from [PyEnsembl](https://github.com/hammerlab/pyensembl);\n",
    "therefore, you can also take a look at its documentation for further clarification on internal workings of this library.\n"
   ]
  }
 ],
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