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Merge pull request #253 from tanghaibao/dvk
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Added GPAD annotation notebook
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@dvklopfenstein
dvklopfenstein authored Nov 30, 2022
2 parents 9a15121 + 7c98c33 commit 341d1c7
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading a _Gene Product Association Data_ (GPAD) file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Download a GPAD file\n",
"2. Load the GPAD file into the GpadReader\n",
"3. Get Annotations\n",
"\n",
"**Bonus: Each line in the GPAD file is stored in a namedtuple**:\n",
" * Namedtuple fields\n",
" * Print a subset of the namedtuple fields"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1) Download a GPAD file and GO Dag file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from os.path import exists\n",
"if not exists('goa_human.gpad'):\n",
" !rm -f goa_human.gpad.gz\n",
" !wget http://current.geneontology.org/annotations/goa_human.gpad.gz\n",
" !gunzip goa_human.gpad.gz"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"if not exists('go-basic.obo'):\n",
" !wget http://geneontology.org/ontology/go-basic.obo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2) Load the GPAD file into the GpadReader"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" EXISTS: go-basic.obo\n",
"go-basic.obo: fmt(1.2) rel(2022-11-03) 46,800 Terms\n",
"HMS:0:00:16.880690 635,765 annotations READ: goa_human.gpad \n"
]
}
],
"source": [
"from goatools.base import get_godag\n",
"from goatools.anno.gpad_reader import GpadReader\n",
"\n",
"godag = get_godag(\"go-basic.obo\")\n",
"anno = GpadReader(\"goa_human.gpad\", godag=godag)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3) Get Annotations\n",
"The annotations will be stored in three dicts, one for each GODAG branch, where:\n",
" * the key is the protein ID and \n",
" * the value is a list of GO IDs associated with the protein."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ns2assc = anno.get_ns2assc()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MF A0A024RBG1 : GO:0000298 GO:0008486 GO:0034431 GO:0034432 GO:0046872 GO:0050072\n",
"MF A0A075B6Q5 : GO:0003823 GO:0034987\n",
"MF A0A075B6R2 : GO:0003823 GO:0034987\n",
"CC A0A024RBG1 : GO:0005634 GO:0005737 GO:0005829\n",
"CC A0A075B6H5 : GO:0005886\n",
"CC A0A075B6H7 : GO:0005615 GO:0005886 GO:0019814\n",
"BP A0A024RBG1 : GO:0071543 GO:1901907 GO:1901909 GO:1901911\n",
"BP A0A075B6H5 : GO:0007166\n",
"BP A0A075B6H7 : GO:0002250 GO:0002377 GO:0006955\n"
]
}
],
"source": [
"for namespace, associations in ns2assc.items():\n",
" for protein_id, go_ids in sorted(associations.items())[:3]:\n",
" print(\"{NS} {PROT:7} : {GOs}\".format(\n",
" NS=namespace,\n",
" PROT=protein_id,\n",
" GOs=' '.join(sorted(go_ids))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bonus: The GPAD is stored as a list of named tuples\n",
"The list of namedtuples is stored in the **GpadReader** data member named **_associations_**.\n",
"\n",
"Each namedtuple stores data for one line in the GPAD file."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ntgpadobj(DB='UniProtKB', DB_ID='A0A024RBG1', Qualifier={'enables'}, GO_ID='GO:0046872', DB_Reference={'GO_REF:0000043'}, ECO='ECO:0000501', Evidence_Code='IEA', With_From={'UniProtKB-KW:KW-0479'}, Taxon=None, Date=datetime.date(2022, 9, 7), Assigned_By='UniProt', Extension=None, Properties={}, NS='MF')\n"
]
}
],
"source": [
"# Sort the list of GPAD namedtuples by ID\n",
"nts = sorted(anno.associations, key=lambda nt:nt.DB_ID)\n",
"\n",
"# Print one namedtuple\n",
"print(nts[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Namedtuple fields\n",
"```\n",
"DB # 0 required 1 UniProtKB\n",
"DB_ID # 1 required 1 P12345\n",
"DB_Symbol # 2 required 1 PHO3\n",
"Qualifier # 3 optional 0 or greater NOT\n",
"GO_ID # 4 required 1 GO:0003993\n",
"DB_Reference # 5 required 1 or greater PMID:2676709\n",
"Evidence_Code # 6 required 1 IMP\n",
"With_From # 7 optional 0 or greater GO:0000346\n",
"Aspect # 8 required 1 F\n",
"DB_Name # 9 optional 0 or 1 Toll-like receptor 4\n",
"DB_Synonym # 10 optional 0 or greater hToll|Tollbooth\n",
"DB_Type # 11 required 1 protein\n",
"Taxon # 12 required 1 or 2 taxon:9606\n",
"Date # 13 required 1 20090118\n",
"Assigned_By # 14 required 1 SGD\n",
"Annotation_Extension # 15 optional 0 or greater part_of(CL:0000576)\n",
"Gene_Product_Form_ID # 16 optional 0 or 1 UniProtKB:P12345-2\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Print a subset of the namedtuple fields"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MF A0A024RBG1 GO:0046872 IEA 2022-09-07 UniProt\n",
"CC A0A024RBG1 GO:0005829 IDA 2016-12-04 HPA\n",
"BP A0A024RBG1 GO:1901907 IBA 2017-02-28 GO_Central\n",
"MF A0A024RBG1 GO:0000298 IBA 2017-02-28 GO_Central\n",
"MF A0A024RBG1 GO:0008486 IBA 2021-05-30 GO_Central\n",
"CC A0A024RBG1 GO:0005634 IBA 2017-02-28 GO_Central\n",
"BP A0A024RBG1 GO:1901911 IBA 2017-02-28 GO_Central\n",
"BP A0A024RBG1 GO:1901909 IBA 2017-02-28 GO_Central\n",
"MF A0A024RBG1 GO:0034432 IBA 2017-02-28 GO_Central\n",
"CC A0A024RBG1 GO:0005737 IBA 2017-02-28 GO_Central\n"
]
}
],
"source": [
"fmtpat = '{NS} {DB_ID} {GO_ID} {Evidence_Code} {Date} {Assigned_By}'\n",
"for nt_line in nts[:10]:\n",
" print(fmtpat.format(**nt_line._asdict()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (C) 2010-present, DV Klopfenstein PhD, Haibao Tang. All rights reserved."
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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