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ahwagner/Identifying Gene Categories from Guide To Pharmacology.ipynb

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Guide To Pharmacology Gene Categories
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Identifying Gene Categories from Guide To Pharmacology.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Purpose\n",
"===\n",
"Existing gene categories in DGIdb will not necessarily match new gene category sources, such as the IUPPHAR/BPS *Guide To Pharmacology*. Also, category scope will necessarily be different between some current DGIdb categories and any given categorization by a single new resource. This program describes our attempt to reconcile this in selecting new categories for the DGIdb.\n",
"\n",
"Python Environment\n",
"==="
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import requests\n",
"import csv\n",
"from matplotlib_venn import venn2\n",
"from collections import defaultdict\n",
"def dd(obj, nest=1):\n",
" if nest > 1:\n",
" return defaultdict(lambda: dd(obj, nest-1))\n",
" else:\n",
" return defaultdict(obj)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mapping Guide to Pharmacology to Existing Categories\n",
"===\n",
"Load gene categories and associated genes.\n",
"Categories are manually extracted from PostgreSQL and hard-coded.\n",
"Categories from GO only are excluded.\n",
"Druggable Genome is excluded.\n",
"Clinically Actionable is excluded.\n",
"Genes are pulled from DGIdb via API."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"categories = [\n",
" \"ABC TRANSPORTER\",\n",
" \"B30_2 SPRY DOMAIN\",\n",
" \"CYTOCHROME P450\",\n",
" \"DNA DIRECTED RNA POLYMERASE\",\n",
" \"EXCHANGER\",\n",
" \"FIBRINOGEN\",\n",
" \"G PROTEIN COUPLED RECEPTOR\",\n",
" \"ION CHANNEL\",\n",
" \"KINASE\",\n",
" \"LIPASE\",\n",
" \"METHYL TRANSFERASE\",\n",
" \"MYOTUBULARIN RELATED PROTEIN PHOSPHATASE\",\n",
" \"NEUTRAL ZINC METALLOPEPTIDASE\",\n",
" \"NUCLEAR HORMONE RECEPTOR\",\n",
" \"PHOSPHATIDYLINOSITOL 3 KINASE\",\n",
" \"PHOSPHOLIPASE\",\n",
" \"PROTEASE\",\n",
" \"PROTEASE INHIBITOR\",\n",
" \"PROTEIN PHOSPHATASE\",\n",
" \"PTEN FAMILY\",\n",
" \"SERINE THREONINE KINASE\",\n",
" \"SHORT CHAIN DEHYDROGENASE REDUCTASE\",\n",
" \"THIOREDOXIN\",\n",
" \"TRANSPORTER\",\n",
" \"TYROSINE KINASE\"\n",
" ]\n",
"genes = {}\n",
"request = \"http://dgidb.genome.wustl.edu/api/v1/genes_in_category.json?category=\"\n",
"for category in categories:\n",
" response = requests.get(request + category.lower())\n",
" genes[category] = response.text.strip('][').replace('\"','').split(',')\n",
" if not all (genes[category]):\n",
" raise ValueError (category, sorted(genes[category]))\n",
" \n",
"with open('targets_and_families.4_mar.2015.csv') as csvfile:\n",
" # Initialize reader\n",
" target_families = csv.DictReader(csvfile)\n",
" \n",
" # Initialize dictionaries / counters\n",
" family_members = dd(set)\n",
" gtp_genes = set()\n",
" category_family_count = dd(int, 2)\n",
" gene_family_count = dd(int, 2)\n",
" family_type = {}\n",
" \n",
" # Define Searchable Fields (from file header)\n",
" search_attributes = ['Type','Family name', 'Target name', \n",
" 'synonyms', 'HGNC name']\n",
" \n",
" # Evaluate each row for matches\n",
" for row_dict in target_families:\n",
" gene_symbol = row_dict['HGNC symbol']\n",
" gene_family = row_dict['Family name']\n",
" gene_type = row_dict['Type']\n",
" family_members[gene_family].add(gene_symbol)\n",
" family_members[gene_type + ' [type]'].add(gene_symbol)\n",
" family_type[gene_family] = gene_type + ' [type]'\n",
" gtp_genes.add(gene_symbol)\n",
" for category in categories:\n",
" \n",
" # Category gene symbol matching\n",
" if gene_symbol in genes[category]:\n",
" gene_family_count[category][gene_family] += 1\n",
" gene_family_count[category][gene_type + ' [type]'] += 1\n",
" \n",
" # Category name matching\n",
" search_fields = [ row_dict[x].lower() for x in search_attributes ]\n",
" if any([ category.lower() in x for x in search_fields ]):\n",
" category_family_count[category][gene_family] += 1\n",
" category_family_count[category][gene_type + ' [type]'] += 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Initial Category Name Matches\n",
"---\n",
"First, how did exact string matching of DGIdb categories to gene/target descriptions perform?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DGIdb category name matches\n",
"===========================\n",
"Category Matches Family/Type\n",
"---------------------------------------------------------\n",
"ABC TRANSPORTER 1/ 12 ( 8%) ABCA subfamily\n",
"ABC TRANSPORTER 5/ 11 ( 45%) ABCB subfamily\n",
"ABC TRANSPORTER 3/ 3 (100%) ABCD subfamily of peroxisomal ABC transporters\n",
"ABC TRANSPORTER 1/ 5 ( 20%) ABCG subfamily\n",
"ABC TRANSPORTER 10/ 507 ( 2%) transporter [type]\n",
"CYTOCHROME P450 3/ 3 (100%) CYP1 family\n",
"CYTOCHROME P450 7/ 7 (100%) CYP11, CYP17, CYP19, CYP20 and CYP21 families\n",
"CYTOCHROME P450 16/ 16 (100%) CYP2 family\n",
"CYTOCHROME P450 7/ 7 (100%) CYP24, CYP26 and CYP27 families\n",
"CYTOCHROME P450 4/ 4 (100%) CYP3 family\n",
"... (446 additional records)\n"
]
}
],
"source": [
"# Pretty formatting for category match header output\n",
"def print_cat_header(category_family_count, header):\n",
" category_length = max([len(x) for x in category_family_count])\n",
" fstring = '{0:{length}} {1:16} {2}'\n",
" fstring_row = '{0:{length}} {1:4}/{2:4} ({3:3}%) {4}' \n",
" print(header)\n",
" print('=' * len(header))\n",
" out = fstring.format('Category','Matches','Family/Type', \n",
" length=category_length)\n",
" print(out + \"\\n\" + '-' * len(out))\n",
" return fstring_row\n",
"\n",
"# Pretty formatting for category match output\n",
"def print_match(fstring_row, category_family_count, head=None):\n",
" i = 0\n",
" j = 0\n",
" category_length = max([len(x) for x in category_family_count])\n",
" for category in sorted(category_family_count):\n",
" for family in sorted(category_family_count[category]):\n",
" if head is not None and i == head:\n",
" j+=1\n",
" continue\n",
" else:\n",
" i+=1\n",
" observed = category_family_count[category][family]\n",
" total = len(family_members[family])\n",
" percentage = round(observed/total*100)\n",
" print(fstring_row.format(category, observed, \n",
" total, percentage, family, \n",
" length=category_length))\n",
" if j > 0:\n",
" print('... ({0} additional records)'.format(j))\n",
" \n",
"fstring_row = print_cat_header(category_family_count, \n",
" 'DGIdb category name matches')\n",
"print_match(fstring_row, category_family_count, head=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"It appears that a number of these direct matches have 100% of the family members in the DGIdb category. These make for good initial keys in a `GuideToPharmacology -> DGIdb category` mapping."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total matches: 456 Incomplete matches: 103\n"
]
}
],
"source": [
"dgidb_category_map = dd(set)\n",
"\n",
"def map_and_get_remainder(dict_of_counts):\n",
" remainder = dd(int, 2)\n",
" for category in sorted(dict_of_counts):\n",
" for family in sorted(dict_of_counts[category]):\n",
" observed = dict_of_counts[category][family]\n",
" total = len(family_members[family])\n",
" if observed == total:\n",
" dgidb_category_map[family].add(category)\n",
" else:\n",
" remainder[category][family] = observed\n",
" return remainder\n",
"\n",
"category_remainder = map_and_get_remainder(category_family_count)\n",
"\n",
"def print_reduction(initial, remainder, header=''):\n",
" if header:\n",
" print(header)\n",
" initial_length = sum([ len(initial[category]) \n",
" for category in initial ])\n",
" remainder_length = sum([ len(remainder[category]) \n",
" for category in remainder ])\n",
" out = 'Total matches: {0} Incomplete matches: {1}'\n",
" print(out.format(initial_length, remainder_length))\n",
" \n",
"print_reduction(category_family_count, category_remainder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That really reduced the size of the list! Before we start manually reviewing, though, we should see if we can knock a few of these out through gene matching.\n",
"\n",
"Initial Gene to Category Mapping\n",
"---\n",
"First, a look at how the gene symbols from the DGIdb categories mapped to Guide To Pharmacology."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DGIdb category genes mapping\n",
"============================\n",
"Category Matches Family/Type\n",
"------------------------------------------------------------------\n",
"ABC TRANSPORTER 4/ 12 ( 33%) ABCA subfamily\n",
"ABC TRANSPORTER 11/ 11 (100%) ABCB subfamily\n",
"ABC TRANSPORTER 11/ 11 (100%) ABCC subfamily\n",
"ABC TRANSPORTER 2/ 3 ( 67%) ABCD subfamily of peroxisomal ABC transporters\n",
"ABC TRANSPORTER 2/ 5 ( 40%) ABCG subfamily\n",
"ABC TRANSPORTER 1/ 1 (100%) CFTR\n",
"ABC TRANSPORTER 9/ 9 (100%) Ca<sup>2+</sup>-ATPases\n",
"ABC TRANSPORTER 2/ 2 (100%) Cu<sup>+</sup>-ATPases\n",
"ABC TRANSPORTER 5/ 14 ( 36%) F-type ATPase\n",
"ABC TRANSPORTER 3/ 3 (100%) H<sup>+</sup>/K<sup>+</sup>-ATPases\n",
"... (897 additional records)\n"
]
}
],
"source": [
"fstring_row = print_cat_header(gene_family_count, \n",
" 'DGIdb category genes mapping')\n",
"print_match(fstring_row, gene_family_count, head=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, a rather extensive list. Let's pare it down with the same 100% auto-mapping strategy."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total matches: 907 Incomplete matches: 244\n"
]
}
],
"source": [
"gene_remainder = map_and_get_remainder(gene_family_count)\n",
"print_reduction(gene_family_count, gene_remainder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Better, but still a number of incomplete matches to review.\n",
"\n",
"Reciprocal Filtering\n",
"---\n",
"Let's use our mapping from both categories to reduce the remaining partial family matches."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Category matches:\n",
"Total matches: 456 Incomplete matches: 70\n",
"Gene matches:\n",
"Total matches: 907 Incomplete matches: 212\n",
"Total family mappings: 452/573 (79%)\n",
"Total genes mapped at least once: 1900/2712 (70%)\n"
]
}
],
"source": [
"def mapped(family, category):\n",
" return family in dgidb_category_map and category in dgidb_category_map[family]\n",
"\n",
"def remove_mapped(dict_of_counts):\n",
" remainder = dd(dict)\n",
" for category in dict_of_counts:\n",
" for family in dict_of_counts[category]:\n",
" if not mapped(family, category): \n",
" if family.endswith('[type]'):\n",
" remainder[category][family] = dict_of_counts[category][family]\n",
" elif not mapped(family_type[family], category):\n",
" remainder[category][family] = dict_of_counts[category][family]\n",
" return remainder\n",
"\n",
"gene_remainder_filtered = remove_mapped(gene_remainder)\n",
"category_remainder_filtered = remove_mapped(category_remainder)\n",
"\n",
"def print_totals():\n",
" print_reduction(category_family_count, \n",
" category_remainder_filtered, \n",
" \"Category matches:\")\n",
" print_reduction(gene_family_count, \n",
" gene_remainder_filtered, \n",
" \"Gene matches:\")\n",
" observed = len(dgidb_category_map)\n",
" total = len(family_members)\n",
" out = 'Total family mappings: {0}/{1} ({2}%)'\n",
" print(out.format(observed,total,round(observed/total*100)))\n",
" obs_g = set()\n",
" for f, gs in family_members.items():\n",
" if f in dgidb_category_map:\n",
" for g in gs:\n",
" obs_g.add(g)\n",
" total = len(gtp_genes)\n",
" observed = len(obs_g)\n",
" out = 'Total genes mapped at least once: {0}/{1} ({2}%)'\n",
" print(out.format(observed,total,round(observed/total*100)))\n",
" \n",
"print_totals()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like this is as small as it gets. 80% of families map to at least one existing category with the default rules. Nice!\n",
"\n",
"Manual Review\n",
"---\n",
"Here's where it gets messy.\n",
"\n",
"###Review of Category Name Matching\n",
"First, evaluation of partial family matches by category name."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DGIdb category matches, after filtering\n",
"=======================================\n",
"Category Matches Family/Type\n",
"---------------------------------------------------------\n",
"ABC TRANSPORTER 1/ 12 ( 8%) ABCA subfamily\n",
"ABC TRANSPORTER 1/ 5 ( 20%) ABCG subfamily\n",
"ABC TRANSPORTER 10/ 507 ( 2%) transporter [type]\n",
"CYTOCHROME P450 2/ 10 ( 20%) Prostaglandin synthases\n",
"CYTOCHROME P450 59/1140 ( 5%) enzyme [type]\n",
"EXCHANGER 2/ 3 ( 67%) SLC19 family of vitamin transporters\n",
"EXCHANGER 1/ 3 ( 33%) SLC34 family of sodium phosphate co-transporters\n",
"EXCHANGER 1/ 13 ( 8%) SLC7 family\n",
"EXCHANGER 3/ 6 ( 50%) Sodium-dependent HCO<sub>3</sub><sup>-</sup> transporters\n",
"EXCHANGER 49/ 507 ( 10%) transporter [type]\n",
"FIBRINOGEN 1/ 27 ( 4%) Integrins\n",
"FIBRINOGEN 1/ 241 ( 0%) catalytic_receptor [type]\n",
"G PROTEIN COUPLED RECEPTOR 5/ 91 ( 5%) Class A Orphans\n",
"G PROTEIN COUPLED RECEPTOR 1/ 6 ( 17%) Other 7TM proteins\n",
"G PROTEIN COUPLED RECEPTOR 8/ 398 ( 2%) gpcr [type]\n",
"ION CHANNEL 1/ 6 ( 17%) 5-HT<sub>3</sub> receptors\n",
"ION CHANNEL 2/ 24 ( 8%) Connexins and Pannexins\n",
"ION CHANNEL 1/ 5 ( 20%) Hexose transporter family\n",
"ION CHANNEL 2/1140 ( 0%) enzyme [type]\n",
"ION CHANNEL 12/ 85 ( 14%) lgic [type]\n",
"ION CHANNEL 5/ 48 ( 10%) other_ic [type]\n",
"ION CHANNEL 3/ 507 ( 1%) transporter [type]\n",
"KINASE 1/ 11 ( 9%) 2.1.1.- Protein arginine N-methyltransferases\n",
"KINASE 1/ 10 ( 10%) Adenosine turnover\n",
"KINASE 1/ 9 ( 11%) ClC family\n",
"KINASE 1/ 4 ( 25%) GDNF receptor family\n",
"KINASE 2/ 15 ( 13%) Lanosterol biosynthesis pathway\n",
"KINASE 1/ 42 ( 2%) S1: Chymotrypsin\n",
"KINASE 2/ 28 ( 7%) Transient Receptor Potential channels\n",
"KINASE 63/ 241 ( 26%) catalytic_receptor [type]\n",
"KINASE 490/1140 ( 43%) enzyme [type]\n",
"KINASE 1/ 48 ( 2%) other_ic [type]\n",
"KINASE 2/ 142 ( 1%) vgic [type]\n",
"LIPASE 1/ 2 ( 50%) ChaK subfamily\n",
"LIPASE 4/ 7 ( 57%) Endocannabinoid turnover\n",
"LIPASE 9/ 14 ( 64%) Hydrolases\n",
"LIPASE 1/ 6 ( 17%) Sphingomyelin phosphodiesterase\n",
"LIPASE 1/ 28 ( 4%) Transient Receptor Potential channels\n",
"LIPASE 50/1140 ( 4%) enzyme [type]\n",
"LIPASE 1/ 142 ( 1%) vgic [type]\n",
"NUCLEAR HORMONE RECEPTOR 1/ 49 ( 2%) nhr [type]\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) 3.1.1.- Carboxylic Ester Hydrolases\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) ChaK subfamily\n",
"PHOSPHOLIPASE 3/ 7 ( 43%) Endocannabinoid turnover\n",
"PHOSPHOLIPASE 5/ 14 ( 36%) Hydrolases\n",
"PHOSPHOLIPASE 1/ 28 ( 4%) Transient Receptor Potential channels\n",
"PHOSPHOLIPASE 44/1140 ( 4%) enzyme [type]\n",
"PHOSPHOLIPASE 1/ 142 ( 1%) vgic [type]\n",
"PROTEASE 2/ 3 ( 67%) Protein kinase N (PKN) family\n",
"PROTEASE 92/1140 ( 8%) enzyme [type]\n",
"PROTEASE 4/ 398 ( 1%) gpcr [type]\n",
"PROTEIN PHOSPHATASE 2/ 3 ( 67%) Aurora kinase (Aur) family\n",
"PROTEIN PHOSPHATASE 2/ 11 ( 18%) C14: Caspase\n",
"PROTEIN PHOSPHATASE 1/ 9 ( 11%) ClC family\n",
"PROTEIN PHOSPHATASE 1/ 2 ( 50%) Fak family\n",
"PROTEIN PHOSPHATASE 1/ 2 ( 50%) Fer family\n",
"PROTEIN PHOSPHATASE 1/ 7 ( 14%) Lipid phosphate phosphatases\n",
"PROTEIN PHOSPHATASE 19/ 241 ( 8%) catalytic_receptor [type]\n",
"PROTEIN PHOSPHATASE 7/1140 ( 1%) enzyme [type]\n",
"PROTEIN PHOSPHATASE 1/ 48 ( 2%) other_ic [type]\n",
"SERINE THREONINE KINASE 1/ 4 ( 25%) Other-unique family\n",
"SERINE THREONINE KINASE 3/1140 ( 0%) enzyme [type]\n",
"TRANSPORTER 2/ 28 ( 7%) Transient Receptor Potential channels\n",
"TRANSPORTER 5/ 48 ( 10%) other_ic [type]\n",
"TRANSPORTER 2/ 142 ( 1%) vgic [type]\n",
"TYROSINE KINASE 1/ 4 ( 25%) GDNF receptor family\n",
"TYROSINE KINASE 1/ 2 ( 50%) SCY1 family\n",
"TYROSINE KINASE 1/ 3 ( 33%) WEE family\n",
"TYROSINE KINASE 38/ 241 ( 16%) catalytic_receptor [type]\n",
"TYROSINE KINASE 34/1140 ( 3%) enzyme [type]\n"
]
}
],
"source": [
"fstring_row = print_cat_header(category_remainder_filtered,\n",
" 'DGIdb category matches, after filtering')\n",
"print_match(fstring_row, category_remainder_filtered)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Manual Review Mappings for Category Name Matching:\n",
"* [ABCA and ABCG subfamilies](http://www.ncbi.nlm.nih.gov/books/NBK3/) to the ABC TRANSPORTER category.\n",
"* [SLC19](http://www.ncbi.nlm.nih.gov/pubmed/14770311) to the EXCHANGER category.\n",
"* gpcr [type] to the G PROTEIN COUPLED RECEPTOR category.\n",
"* lgic [type] to the ION CHANNEL category.\n",
"* other_ic [type] to the ION CHANNEL category.\n",
"* nhr [type] to the NUCLEAR HORMONE RECEPTOR category."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cmap(family, category):\n",
" return dgidb_category_map[family].add(category)\n",
"\n",
"cmap('ABCA subfamily','ABC TRANSPORTER')\n",
"cmap('ABCG subfamily','ABC TRANSPORTER')\n",
"cmap('SLC19 family of vitamin transporters','EXCHANGER')\n",
"cmap('gpcr [type]','G PROTEIN COUPLED RECEPTOR')\n",
"cmap('lgic [type]','ION CHANNEL')\n",
"cmap('other_ic [type]','ION CHANNEL')\n",
"cmap('nhr [type]','NUCLEAR HORMONE RECEPTOR')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Category matches:\n",
"Total matches: 456 Incomplete matches: 59\n",
"Gene matches:\n",
"Total matches: 907 Incomplete matches: 189\n",
"Total family mappings: 456/573 (80%)\n",
"Total genes mapped at least once: 2105/2712 (78%)\n"
]
}
],
"source": [
"gene_remainder_filtered = remove_mapped(gene_remainder)\n",
"category_remainder_filtered = remove_mapped(category_remainder)\n",
"print_totals()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mapping the types to their respective categories bolstered the at-least-one gene mapping by 8 percentage points!\n",
"\n",
"###Manual Review of Gene to Category Mapping\n",
"Next, evaluation of partial matches by gene membership."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DGIdb category gene mapping, after filtering\n",
"============================================\n",
"Category Matches Family/Type\n",
"------------------------------------------------------------------\n",
"ABC TRANSPORTER 5/ 14 ( 36%) F-type ATPase\n",
"ABC TRANSPORTER 14/ 23 ( 61%) V-type ATPase\n",
"ABC TRANSPORTER 1/ 48 ( 2%) other_ic [type]\n",
"ABC TRANSPORTER 85/ 507 ( 17%) transporter [type]\n",
"B30_2 SPRY DOMAIN 3/ 85 ( 4%) lgic [type]\n",
"CYTOCHROME P450 2/ 10 ( 20%) Prostaglandin synthases\n",
"CYTOCHROME P450 59/1140 ( 5%) enzyme [type]\n",
"EXCHANGER 13/ 507 ( 3%) transporter [type]\n",
"G PROTEIN COUPLED RECEPTOR 1/ 3 ( 33%) 1C. Peroxisome proliferator-activated receptors\n",
"G PROTEIN COUPLED RECEPTOR 1/ 3 ( 33%) 1F. Retinoic acid-related orphans\n",
"G PROTEIN COUPLED RECEPTOR 2/ 6 ( 33%) 5-HT<sub>3</sub> receptors\n",
"G PROTEIN COUPLED RECEPTOR 1/ 13 ( 8%) Inositol polyphosphate phosphatases\n",
"G PROTEIN COUPLED RECEPTOR 1/ 18 ( 6%) Ionotropic glutamate receptors\n",
"G PROTEIN COUPLED RECEPTOR 1/ 22 ( 5%) NOD-like receptor family\n",
"G PROTEIN COUPLED RECEPTOR 2/ 4 ( 50%) Natriuretic peptide receptor family\n",
"G PROTEIN COUPLED RECEPTOR 2/ 10 ( 20%) Prostaglandin synthases\n",
"G PROTEIN COUPLED RECEPTOR 1/ 2 ( 50%) Sigma receptors\n",
"G PROTEIN COUPLED RECEPTOR 1/ 28 ( 4%) Transient Receptor Potential channels\n",
"G PROTEIN COUPLED RECEPTOR 1/ 6 ( 17%) Tubulins\n",
"G PROTEIN COUPLED RECEPTOR 1/ 5 ( 20%) Type III RTKs: PDGFR, CSFR, Kit, FLT3 receptor family\n",
"G PROTEIN COUPLED RECEPTOR 4/ 241 ( 2%) catalytic_receptor [type]\n",
"G PROTEIN COUPLED RECEPTOR 3/1140 ( 0%) enzyme [type]\n",
"G PROTEIN COUPLED RECEPTOR 3/ 85 ( 4%) lgic [type]\n",
"G PROTEIN COUPLED RECEPTOR 2/ 49 ( 4%) nhr [type]\n",
"G PROTEIN COUPLED RECEPTOR 2/ 110 ( 2%) other_protein [type]\n",
"G PROTEIN COUPLED RECEPTOR 1/ 142 ( 1%) vgic [type]\n",
"ION CHANNEL 1/ 16 ( 6%) 1.-.-.- Oxidoreductases\n",
"ION CHANNEL 1/ 13 ( 8%) 5-Hydroxytryptamine receptors\n",
"ION CHANNEL 1/ 2 ( 50%) 5.2.-.- Cis-trans-isomerases\n",
"ION CHANNEL 1/ 2 ( 50%) A22: Presenilin\n",
"ION CHANNEL 1/ 2 ( 50%) Alanine/serine/cysteine transporter subfamily\n",
"ION CHANNEL 1/ 9 ( 11%) Ca<sup>2+</sup>-ATPases\n",
"ION CHANNEL 2/ 3 ( 67%) H<sup>+</sup>/K<sup>+</sup>-ATPases\n",
"ION CHANNEL 1/ 27 ( 4%) Integrins\n",
"ION CHANNEL 1/ 8 ( 12%) Metabotropic glutamate receptors\n",
"ION CHANNEL 5/ 8 ( 62%) Na<sup>+</sup>/K<sup>+</sup>-ATPases\n",
"ION CHANNEL 1/ 2 ( 50%) Neuromedin U receptors\n",
"ION CHANNEL 1/ 4 ( 25%) Other SLC26 anion exchangers\n",
"ION CHANNEL 1/ 24 ( 4%) Phosphodiesterases, 3',5'-cyclic nucleotide\n",
"ION CHANNEL 1/ 6 ( 17%) SLC24 family of sodium/potassium/calcium exchangers\n",
"ION CHANNEL 1/ 13 ( 8%) SLC9 family of sodium/hydrogen exchangers\n",
"ION CHANNEL 1/ 6 ( 17%) Sodium-dependent HCO<sub>3</sub><sup>-</sup> transporters\n",
"ION CHANNEL 1/ 4 ( 25%) Type I sodium-phosphate co-transporters\n",
"ION CHANNEL 1/ 241 ( 0%) catalytic_receptor [type]\n",
"ION CHANNEL 6/1140 ( 1%) enzyme [type]\n",
"ION CHANNEL 3/ 398 ( 1%) gpcr [type]\n",
"ION CHANNEL 15/ 507 ( 3%) transporter [type]\n",
"KINASE 1/ 2 ( 50%) 5.2.-.- Cis-trans-isomerases\n",
"KINASE 2/ 10 ( 20%) Adenosine turnover\n",
"KINASE 3/ 15 ( 20%) Lanosterol biosynthesis pathway\n",
"KINASE 3/ 4 ( 75%) Natriuretic peptide receptor family\n",
"KINASE 2/ 24 ( 8%) Non-enzymatic BRD containing proteins\n",
"KINASE 1/ 42 ( 2%) S1: Chymotrypsin\n",
"KINASE 2/ 3 ( 67%) Sphingomyelin synthase\n",
"KINASE 2/ 3 ( 67%) TIF1 family\n",
"KINASE 2/ 28 ( 7%) Transient Receptor Potential channels\n",
"KINASE 6/ 40 ( 15%) Voltage-gated potassium channels\n",
"KINASE 76/ 241 ( 32%) catalytic_receptor [type]\n",
"KINASE 485/1140 ( 43%) enzyme [type]\n",
"KINASE 2/ 110 ( 2%) other_protein [type]\n",
"KINASE 8/ 142 ( 6%) vgic [type]\n",
"LIPASE 2/ 14 ( 14%) Hydrolases\n",
"LIPASE 2/1140 ( 0%) enzyme [type]\n",
"METHYL TRANSFERASE 8/ 11 ( 73%) 2.1.1.- Protein arginine N-methyltransferases\n",
"METHYL TRANSFERASE 18/ 24 ( 75%) 2.1.1.43 Histone methyltransferases (HMTs)\n",
"METHYL TRANSFERASE 26/1140 ( 2%) enzyme [type]\n",
"NEUTRAL ZINC METALLOPEPTIDASE 1/ 14 ( 7%) Hydrolases\n",
"NEUTRAL ZINC METALLOPEPTIDASE 3/ 5 ( 60%) Leukotriene and lipoxin metabolism\n",
"NEUTRAL ZINC METALLOPEPTIDASE 18/ 19 ( 95%) M14: Carboxypeptidase A\n",
"NEUTRAL ZINC METALLOPEPTIDASE 10/ 12 ( 83%) M1: Aminopeptidase N\n",
"NEUTRAL ZINC METALLOPEPTIDASE 4/ 5 ( 80%) M28: Aminopeptidase Y\n",
"NEUTRAL ZINC METALLOPEPTIDASE 1/ 2 ( 50%) M2: Angiotensin-converting (ACE and ACE2)\n",
"NEUTRAL ZINC METALLOPEPTIDASE 1/ 6 ( 17%) S9: Prolyl oligopeptidase\n",
"NEUTRAL ZINC METALLOPEPTIDASE 121/1140 ( 11%) enzyme [type]\n",
"NUCLEAR HORMONE RECEPTOR 1/ 7 ( 14%) Bromodomain kinase (BRDK) family\n",
"NUCLEAR HORMONE RECEPTOR 1/ 9 ( 11%) Prostanoid receptors\n",
"NUCLEAR HORMONE RECEPTOR 1/1140 ( 0%) enzyme [type]\n",
"NUCLEAR HORMONE RECEPTOR 1/ 398 ( 0%) gpcr [type]\n",
"PHOSPHATIDYLINOSITOL 3 KINASE 35/1140 ( 3%) enzyme [type]\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) 3.1.1.- Carboxylic Ester Hydrolases\n",
"PHOSPHOLIPASE 3/ 13 ( 23%) 5-Hydroxytryptamine receptors\n",
"PHOSPHOLIPASE 3/ 5 ( 60%) Acetylcholine receptors (muscarinic)\n",
"PHOSPHOLIPASE 1/ 4 ( 25%) Adenosine receptors\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) Bradykinin receptors\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) Calcium-sensing receptors\n",
"PHOSPHOLIPASE 2/ 23 ( 9%) Chemokine receptors\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) Cholecystokinin receptors\n",
"PHOSPHOLIPASE 1/ 3 ( 33%) Complement peptide receptors\n",
"PHOSPHOLIPASE 2/ 7 ( 29%) Endocannabinoid turnover\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) Endothelin receptors\n",
"PHOSPHOLIPASE 1/ 2 ( 50%) Haem oxygenase\n",
"PHOSPHOLIPASE 5/ 14 ( 36%) Hydrolases\n",
"PHOSPHOLIPASE 1/ 4 ( 25%) Proteinase-activated receptors\n",
"PHOSPHOLIPASE 47/1140 ( 4%) enzyme [type]\n",
"PHOSPHOLIPASE 15/ 398 ( 4%) gpcr [type]\n",
"PROTEASE 1/ 11 ( 9%) Aquaporins\n",
"PROTEASE 7/ 9 ( 78%) C1: Papain\n",
"PROTEASE 1/ 19 ( 5%) Fatty acid-binding proteins\n",
"PROTEASE 1/ 14 ( 7%) Hydrolases\n",
"PROTEASE 3/ 5 ( 60%) Leukotriene and lipoxin metabolism\n",
"PROTEASE 10/ 12 ( 83%) M1: Aminopeptidase N\n",
"PROTEASE 236/1140 ( 21%) enzyme [type]\n",
"PROTEASE 1/ 48 ( 2%) other_ic [type]\n",
"PROTEASE 1/ 110 ( 1%) other_protein [type]\n",
"PROTEASE INHIBITOR 1/ 3 ( 33%) Blood coagulation components\n",
"PROTEASE INHIBITOR 1/ 4 ( 25%) C19: Ubiquitin-specific protease\n",
"PROTEASE INHIBITOR 5/ 8 ( 62%) Inhibitors of apoptosis (IAP) protein family\n",
"PROTEASE INHIBITOR 1/ 5 ( 20%) Leukotriene and lipoxin metabolism\n",
"PROTEASE INHIBITOR 1/ 22 ( 5%) NOD-like receptor family\n",
"PROTEASE INHIBITOR 2/ 4 ( 50%) RSK subfamily\n",
"PROTEASE INHIBITOR 1/ 10 ( 10%) S8: Subtilisin\n",
"PROTEASE INHIBITOR 1/ 30 ( 3%) Tumour necrosis factor (TNF) receptor family\n",
"PROTEASE INHIBITOR 2/ 241 ( 1%) catalytic_receptor [type]\n",
"PROTEASE INHIBITOR 6/1140 ( 1%) enzyme [type]\n",
"PROTEASE INHIBITOR 6/ 110 ( 5%) other_protein [type]\n",
"PROTEIN PHOSPHATASE 1/ 5 ( 20%) CAMK2 family\n",
"PROTEIN PHOSPHATASE 3/ 7 ( 43%) Lipid phosphate phosphatases\n",
"PROTEIN PHOSPHATASE 1/ 7 ( 14%) STE7 family\n",
"PROTEIN PHOSPHATASE 1/ 11 ( 9%) Src family\n",
"PROTEIN PHOSPHATASE 20/ 241 ( 8%) catalytic_receptor [type]\n",
"PROTEIN PHOSPHATASE 6/1140 ( 1%) enzyme [type]\n",
"PTEN FAMILY 1/ 7 ( 14%) Lipid phosphate phosphatases\n",
"PTEN FAMILY 1/1140 ( 0%) enzyme [type]\n",
"SERINE THREONINE KINASE 1/ 2 ( 50%) 5.2.-.- Cis-trans-isomerases\n",
"SERINE THREONINE KINASE 7/ 8 ( 88%) AMPK subfamily\n",
"SERINE THREONINE KINASE 1/ 2 ( 50%) Ack family\n",
"SERINE THREONINE KINASE 3/ 7 ( 43%) Bromodomain kinase (BRDK) family\n",
"SERINE THREONINE KINASE 4/ 5 ( 80%) CAMK2 family\n",
"SERINE THREONINE KINASE 3/ 4 ( 75%) Casein kinase 2 (CK2) family\n",
"SERINE THREONINE KINASE 1/ 3 ( 33%) Inositol 1,4,5-trisphosphate 3-kinases\n",
"SERINE THREONINE KINASE 1/ 24 ( 4%) Non-enzymatic BRD containing proteins\n",
"SERINE THREONINE KINASE 3/ 4 ( 75%) Other-unique family\n",
"SERINE THREONINE KINASE 3/ 18 ( 17%) Phosphatidylinositol kinases\n",
"SERINE THREONINE KINASE 2/ 4 ( 50%) Phosphatidylinositol-4,5-bisphosphate 3-kinase family\n",
"SERINE THREONINE KINASE 5/ 10 ( 50%) Protein kinase A\n",
"SERINE THREONINE KINASE 1/ 11 ( 9%) Src family\n",
"SERINE THREONINE KINASE 2/ 28 ( 7%) Transient Receptor Potential channels\n",
"SERINE THREONINE KINASE 1/ 4 ( 25%) Type I RTKs: ErbB (epidermal growth factor) receptor family\n",
"SERINE THREONINE KINASE 1/ 2 ( 50%) Type XIX RTKs: Leukocyte tyrosine kinase (LTK) receptor family\n",
"SERINE THREONINE KINASE 18/ 241 ( 7%) catalytic_receptor [type]\n",
"SERINE THREONINE KINASE 407/1140 ( 36%) enzyme [type]\n",
"SERINE THREONINE KINASE 1/ 110 ( 1%) other_protein [type]\n",
"SERINE THREONINE KINASE 2/ 142 ( 1%) vgic [type]\n",
"SHORT CHAIN DEHYDROGENASE REDUCTASE 1/ 16 ( 6%) 1.-.-.- Oxidoreductases\n",
"SHORT CHAIN DEHYDROGENASE REDUCTASE 1/ 2 ( 50%) 2.3.-.- Acyltransferases\n",
"SHORT CHAIN DEHYDROGENASE REDUCTASE 2/ 10 ( 20%) Prostaglandin synthases\n",
"SHORT CHAIN DEHYDROGENASE REDUCTASE 5/1140 ( 0%) enzyme [type]\n",
"TRANSPORTER 1/ 16 ( 6%) 1.-.-.- Oxidoreductases\n",
"TRANSPORTER 5/ 6 ( 83%) 5-HT<sub>3</sub> receptors\n",
"TRANSPORTER 1/ 13 ( 8%) 5-Hydroxytryptamine receptors\n",
"TRANSPORTER 1/ 2 ( 50%) 5.2.-.- Cis-trans-isomerases\n",
"TRANSPORTER 1/ 2 ( 50%) A22: Presenilin\n",
"TRANSPORTER 2/ 3 ( 67%) Calcitonin receptors\n",
"TRANSPORTER 16/ 24 ( 67%) Connexins and Pannexins\n",
"TRANSPORTER 4/ 5 ( 80%) Epithelial sodium channels (ENaC)\n",
"TRANSPORTER 18/ 19 ( 95%) Fatty acid-binding proteins\n",
"TRANSPORTER 5/ 6 ( 83%) Glycine receptors\n",
"TRANSPORTER 1/ 27 ( 4%) Integrins\n",
"TRANSPORTER 1/ 40 ( 2%) M12: Astacin/Adamalysin\n",
"TRANSPORTER 1/ 8 ( 12%) Metabotropic glutamate receptors\n",
"TRANSPORTER 1/ 2 ( 50%) Neuromedin U receptors\n",
"TRANSPORTER 16/ 17 ( 94%) Nicotinic acetylcholine receptors\n",
"TRANSPORTER 1/ 24 ( 4%) Phosphodiesterases, 3',5'-cyclic nucleotide\n",
"TRANSPORTER 1/ 10 ( 10%) Prostaglandin synthases\n",
"TRANSPORTER 27/ 28 ( 96%) Transient Receptor Potential channels\n",
"TRANSPORTER 1/ 241 ( 0%) catalytic_receptor [type]\n",
"TRANSPORTER 8/1140 ( 1%) enzyme [type]\n",
"TRANSPORTER 5/ 398 ( 1%) gpcr [type]\n",
"TRANSPORTER 84/ 85 ( 99%) lgic [type]\n",
"TRANSPORTER 39/ 48 ( 81%) other_ic [type]\n",
"TRANSPORTER 18/ 110 ( 16%) other_protein [type]\n",
"TRANSPORTER 141/ 142 ( 99%) vgic [type]\n",
"TYROSINE KINASE 1/ 3 ( 33%) Delta subfamily\n",
"TYROSINE KINASE 2/ 4 ( 50%) MLK subfamily\n",
"TYROSINE KINASE 1/ 2 ( 50%) Meta subfamily\n",
"TYROSINE KINASE 1/ 5 ( 20%) Myosin Light Chain Kinase (MLCK) family\n",
"TYROSINE KINASE 1/ 2 ( 50%) NAK family\n",
"TYROSINE KINASE 2/ 11 ( 18%) NIMA (never in mitosis gene a)- related kinase (NEK) family\n",
"TYROSINE KINASE 1/ 24 ( 4%) Non-enzymatic BRD containing proteins\n",
"TYROSINE KINASE 1/ 2 ( 50%) Other PEK family kinases\n",
"TYROSINE KINASE 1/ 4 ( 25%) Other-unique family\n",
"TYROSINE KINASE 1/ 4 ( 25%) Polo-like kinase (PLK) family\n",
"TYROSINE KINASE 1/ 5 ( 20%) Receptor interacting protein kinase (RIPK) family\n",
"TYROSINE KINASE 1/ 2 ( 50%) SCY1 family\n",
"TYROSINE KINASE 1/ 4 ( 25%) Trio family\n",
"TYROSINE KINASE 2/ 3 ( 67%) WEE family\n",
"TYROSINE KINASE 58/ 241 ( 24%) catalytic_receptor [type]\n",
"TYROSINE KINASE 71/1140 ( 6%) enzyme [type]\n",
"TYROSINE KINASE 1/ 110 ( 1%) other_protein [type]\n"
]
}
],
"source": [
"fstring_row = print_cat_header(gene_remainder_filtered, \n",
" 'DGIdb category gene mapping, after filtering')\n",
"print_match(fstring_row, gene_remainder_filtered)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Manual Review Mappings for Gene to Category Mapping\n",
"* 2.1.1.- Protein arginine N-methyltransferases to METHYL TRANSFERASE\n",
"* 2.1.1.43 Histone methyltransferases (HMTs) to METHYL TRANSFERASE\n",
"* [M14](http://en.wikipedia.org/wiki/Carboxypeptidase_A), [M1](http://en.wikipedia.org/wiki/Alanine_aminopeptidase), [M28](http://www.ncbi.nlm.nih.gov/pubmed/8175799), and [M2](http://www.ncbi.nlm.nih.gov/pubmed/16054014) to NEUTRAL ZINC METALLOPEPTIDASE\n",
"* [C1: Papain](http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=728) to PROTEASE\n",
"* [M1: Alanine aminopeptidase](http://en.wikipedia.org/wiki/Alanine_aminopeptidase) to PROTEASE\n",
"* [AMPK subfamily](http://www.ncbi.nlm.nih.gov/pubmed/9136115) to SERINE THREONINE KINASE\n",
"* [CAMK2 family](http://en.wikipedia.org/wiki/Ca2%2B/calmodulin-dependent_protein_kinase_II) to SERINE THREONINE KINASE\n",
"* [Casein kinase 2 (CK2) family](http://en.wikipedia.org/wiki/Casein_kinase_2) to SERINE THREONINE KINASE\n",
"* [Protein kinase A](http://en.wikipedia.org/wiki/Protein_kinase_A) to SERINE THREONINE KINASE\n",
"* [5-HT<sub>3</sub> receptors](http://www.guidetopharmacology.org/GRAC/FamilyIntroductionForward?familyId=68) to TRANSPORTER\n",
"* [Transient Receptor Potential channels](http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=78) to TRANSPORTER\n",
"* [Fatty acid-binding proteins](http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=783) to TRANSPORTER\n",
"* lgic [type], vgic [type], and other_ic[type] to TRANSPORTER\n",
"\n",
"*Notably*, the M\\d+: identifier shows various metallopeptidase families, most of which are zinc based. There is no \"Neutral Zinc Metallopeptidase\" annotation in GO, nor in the Hopkins/Groom resource from DGIdb (the two resources with genes in this category). However, there is documentation of such a group at [prosite](http://prosite.expasy.org/PDOC00129), so I'm assuming there was good reason for naming it this."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cmap('2.1.1.- Protein arginine N-methyltransferases','METHYL TRANSFERASE')\n",
"cmap('2.1.1.43 Histone methyltransferases (HMTs)','METHYL TRANSFERASE')\n",
"cmap('M14: Carboxypeptidase A','NEUTRAL ZINC METALLOPEPTIDASE')\n",
"cmap('M1: Aminopeptidase N','NEUTRAL ZINC METALLOPEPTIDASE')\n",
"cmap('M28: Aminopeptidase Y','NEUTRAL ZINC METALLOPEPTIDASE')\n",
"cmap('M2: Angiotensin-converting (ACE and ACE2)','NEUTRAL ZINC METALLOPEPTIDASE')\n",
"cmap('C1: Papain','PROTEASE')\n",
"cmap('M1: Aminopeptidase N','PROTEASE')\n",
"cmap('AMPK subfamily','SERINE THREONINE KINASE')\n",
"cmap('CAMK2 family','SERINE THREONINE KINASE')\n",
"cmap('Casein kinase 2 (CK2) family','SERINE THREONINE KINASE')\n",
"cmap('Protein kinase A','SERINE THREONINE KINASE')\n",
"cmap('5-HT<sub>3</sub> receptors','TRANSPORTER')\n",
"cmap('Transient Receptor Potential channels','TRANSPORTER')\n",
"cmap('Fatty acid-binding proteins','TRANSPORTER')\n",
"cmap('lgic [type]','TRANSPORTER')\n",
"cmap('vgic [type]','TRANSPORTER')\n",
"cmap('other_ic [type]', 'TRANSPORTER')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Category matches:\n",
"Total matches: 456 Incomplete matches: 56\n",
"Gene matches:\n",
"Total matches: 907 Incomplete matches: 167\n",
"Total family mappings: 462/573 (81%)\n",
"Total genes mapped at least once: 2179/2712 (80%)\n"
]
}
],
"source": [
"gene_remainder_filtered = remove_mapped(gene_remainder)\n",
"category_remainder_filtered = remove_mapped(category_remainder)\n",
"print_totals()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"New category evaluation\n",
"===\n",
"Evaluate new categories for inclusion in the DGIdb.\n",
"\n",
"Gene Counts in Current Categories\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dgidb_reverse_map = { category: set() for category in categories }\n",
"for family in dgidb_category_map:\n",
" for category in dgidb_category_map[family]:\n",
" dgidb_reverse_map[category].add(family)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene Counts by Category:\n",
"Category GtP DGIdb Totals\n",
"================================================================\n",
"ABC TRANSPORTER 79 102 114\n",
"B30_2 SPRY DOMAIN 3 89 89\n",
"CYTOCHROME P450 57 57 57\n",
"EXCHANGER 45 14 46\n",
"G PROTEIN COUPLED RECEPTOR 398 899 913\n",
"ION CHANNEL 276 402 432\n",
"KINASE 546 821 838\n",
"LIPASE 35 10 45\n",
"METHYL TRANSFERASE 35 56 65\n",
"NEUTRAL ZINC METALLOPEPTIDASE 121 181 186\n",
"NUCLEAR HORMONE RECEPTOR 49 57 58\n",
"PHOSPHATIDYLINOSITOL 3 KINASE 24 24 24\n",
"PHOSPHOLIPASE 39 96 97\n",
"PROTEASE 240 634 642\n",
"PROTEASE INHIBITOR 1 181 181\n",
"PROTEIN PHOSPHATASE 20 174 174\n",
"SERINE THREONINE KINASE 412 464 469\n",
"SHORT CHAIN DEHYDROGENASE REDUCTASE 1 53 53\n",
"TRANSPORTER 800 1195 1290\n",
"TYROSINE KINASE 116 148 151\n"
]
}
],
"source": [
"category_length = max([len(x) for x in dgidb_reverse_map])\n",
"fstring = '{0:{length}} {1:>7} {2:>7} {3:>7}'\n",
"header = fstring.format('Category','GtP','DGIdb','Totals',length=category_length)\n",
"print(\"Gene Counts by Category:\\n\" + header)\n",
"print('=' * len(header))\n",
"gtp_gene_counts = []\n",
"dgi_gene_counts = []\n",
"total_gene_counts = []\n",
"final_categories = []\n",
"for cat in sorted(categories):\n",
" s = set()\n",
" for family in dgidb_reverse_map[cat]:\n",
" for gene in family_members[family]:\n",
" s.add(gene)\n",
" count = len(s)\n",
" if count == 0: # Ignore categories with no GtP matches\n",
" continue\n",
" for gene in genes[cat]:\n",
" s.add(gene)\n",
" print(fstring.format(cat,count,len(genes[cat]),\n",
" len(s),length=category_length))\n",
" gtp_gene_counts.append(count)\n",
" dgi_gene_counts.append(len(genes[cat]))\n",
" total_gene_counts.append(len(s))\n",
" final_categories.append(cat)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10c212a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def intersect_values(a,b,c):\n",
" return c-b,c-a,abs(c-a-b)\n",
"figure, axes = plt.subplots(5,4)\n",
"figure.set_size_inches(15,15)\n",
"figure.set_dpi(300)\n",
"for i, category in enumerate(final_categories):\n",
" x = intersect_values(gtp_gene_counts[i],\n",
" dgi_gene_counts[i],total_gene_counts[i])\n",
" venn2(x, (' GtP ',' DGIdb '), ax=axes[i%5][i//5])\n",
" axes[i%5][i//5].set_title(category)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It may be worth double-checking the lipase group before going any further."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phosphatidylcholine-specific phospholipase D\n",
"3.1.1.- Carboxylic Ester Hydrolases\n",
"Phospholipase A<sub>2</sub>\n",
"Phosphoinositide-specific phospholipase C\n"
]
}
],
"source": [
"for family in dgidb_reverse_map['LIPASE']:\n",
" print(family)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like these are obvious lipase families, except for 3.1.1. Let's review the genes in that family."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASPG\n",
"LIPF\n"
]
}
],
"source": [
"for gene in family_members['3.1.1.- Carboxylic Ester Hydrolases']:\n",
" print(gene)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It checks out. [ASPG](http://www.genecards.org/cgi-bin/carddisp.pl?gene=ASPG) and [LIPF](http://www.genecards.org/cgi-bin/carddisp.pl?gene=LIPF) both exhibit lipase activity. Let's move on and investigate the overall gene category size distribution, to use for identifying new gene category candidates."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"YII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x10c22d6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure = plt.figure(figsize=(8,8),dpi=300)\n",
"plt.boxplot([gtp_gene_counts,dgi_gene_counts,total_gene_counts], \n",
" labels=['GtP','DGIdb','Combined'])\n",
"plt.yscale('log')\n",
"plt.title('Category Gene Counts by Resource')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It appears that the majority of the DGIdb categories have at least 30 genes. While evaluating new categories, we should select those that have at least as many."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Count Family\n",
"============\n",
" 91 Class A Orphans\n",
" 30 Tumour necrosis factor (TNF) receptor family\n",
" 241 catalytic_receptor [type]\n",
" 1140 enzyme [type]\n",
" 110 other_protein [type]\n"
]
}
],
"source": [
"remainder = [ family for family in family_members if family not in dgidb_category_map ]\n",
"fstring = '{0:5} {1}'\n",
"header = fstring.format('Count', 'Family')\n",
"print(header)\n",
"print('=' * len(header))\n",
"for family in sorted(remainder):\n",
" if len(family_members[family]) >= 30:\n",
" print(fstring.format(len(family_members[family]),family))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of these, the [type] values are too broad, and the [Class A Orphans](http://www.guidetopharmacology.org/GRAC/FamilyDisplayForward?familyId=16) are a collection of otherwise unclassified Rhodopsin-like GPCRs. Drugs that promote TNF to TNFR binding may be useful, making the TNFR category worth investigating further as a potential addition.\n",
"\n",
"Reporting\n",
"===\n",
"We can now update the records from our initial file to include the new category mapping, omitting records with no categorical map."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"out_fields = ['Target id', \n",
" 'Target name', \n",
" 'Family name', \n",
" 'Family id',\n",
" 'Type',\n",
" 'HGNC symbol',\n",
" 'HGNC id',\n",
" 'HGNC name',\n",
" 'Human nucleotide RefSeq',\n",
" 'Human protein RefSeq',\n",
" 'Human SwissProt',\n",
" 'Human Entrez Gene',\n",
" 'Gene Category'\n",
" ]\n",
"\n",
"with open('targets_and_families.4_mar.2015.csv') as csvinfile:\n",
" in_csv = csv.DictReader(csvinfile)\n",
" with open('targets_and_families.mapped.4_mar_2015.tsv', 'w') as tsvoutfile:\n",
" out_tsv = csv.DictWriter(tsvoutfile, out_fields, delimiter='\\t')\n",
" out_tsv.writeheader()\n",
" for row in in_csv:\n",
" family = row['Family name']\n",
" if family not in dgidb_category_map:\n",
" continue\n",
" row['Gene Category'] = '|'.join(dgidb_category_map[family])\n",
" out_tsv.writerow({ k: row[k] for k in row if k in out_fields })"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conclusion\n",
"===\n",
"Name and gene member mapping from GtP to DGIdb incorporated approximately 80% of all genes and families at least once. TNF Receptors were identified as a potential novel category for DGIdb by size inclusion, although clinical importance of this group is unknown by me. The mapped targets from GtP successfully imported into a development instance of DGIdb."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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