# gprofiler
## Project description
The official Python 3 interface to the [g:Profiler](https://biit.cs.ut.ee/gprofiler/)
toolkit for enrichment analysis of functional (GO and other) terms,
conversion between identifier namespaces and mapping orhologous genes in related organisms.
It has an optional dependency on pandas.
### Installing gprofiler
the recommended way of installing gprofiler is using pip
```bash
pip install gprofiler-official
```
### Legacy version
The `0.3.x` series of gprofiler-official is incompatible with the `1.0.x` series. We changed the major version number to
signify the breaking changes in the API. To install the previous version of `gprofiler-official`, use the command
```bash
pip install gprofiler-official==0.3.5
```
## Tools:
To use any of the tools in the g:Profiler toolkit, first initialize the GProfiler object.
```python
from gprofiler import GProfiler
gp = GProfiler(
user_agent='ExampleTool', #optional user agent
return_dataframe=True, #return pandas dataframe or plain python structures
)
```
### g:GOSt (profile)
```python
from gprofiler import GProfiler
gp = GProfiler(return_dataframe=True)
gp.profile(organism='hsapiens',
query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'])
```
Output:
```
source native name p_value significant description term_size query_size intersection_size effective_domain_size precision recall query parents
GO:BP GO:0048585 negative regulation of response to stimulus 0.004229 True "Any process that stops, prevents, or reduces ... 1610 7 6 17622 0.857143 0.003727 query_1 [GO:0048583, GO:0048519, GO:0050896]
GO:BP GO:0002224 toll-like receptor signaling pathway 0.016351 True "Any series of molecular signals generated as ... 133 7 3 17622 0.428571 0.022556 query_1 [GO:0002221]
GO:BP GO:0048486 parasympathetic nervous system development 0.026199 True "The process whose specific outcome is the pro... 19 7 2 17622 0.285714 0.105263 query_1 [GO:0048483, GO:0048731]
GO:BP GO:0034162 toll-like receptor 9 signaling pathway 0.038733 True "Any series of molecular signals generated as ... 23 7 2 17622 0.285714 0.086957 query_1 [GO:0002224]
GO:BP GO:0002221 pattern recognition receptor signaling pathway 0.039782 True "Any series of molecular signals generated as ... 179 7 3 17622 0.428571 0.016760 query_1 [GO:0002758]
CORUM CORUM:5669 PlexinA3-Nrp1 complex 0.049767 True PlexinA3-Nrp1 complex 2 2 1 3620 0.500000 0.500000 query_1 [CORUM:0000000]
CORUM CORUM:5759 PLXNA3-RANBPM complex 0.049767 True PLXNA3-RANBPM complex 2 2 1 3620 0.500000 0.500000 query_1 [CORUM:0000000]
```
* `source` is the code for the datasource
* `native` is the ID for the enriched term/functional category in its native namespace.
* `name` is the readable name for the enriched term, `description` is the longer description if available.
* `p_value` is the corrected p-value for the
* `term_size`, `query_size`, `intersection_size`, `effective_domain_size` are parameters to the hypergeometric test.
* `query` is the name of the query and is significant if multiple queries were made in one call (e.g `gp.profile(query={'query1':['NR1H4'], 'query2':['NR1H4','TRIP12']})`)
Setting the parameter `no_evidences=False` would add the column `intersections` (a list of genes that are annotated to the term and are present in the query )
and the column `evidences` (a list of lists of GO evidence codes for the intersecting genes)
NB! the parameter `combined` significantly changes the output structure by packing the results of distinct queries together.
For example:
```python
gp.profile(query={'query1':['NR1H4'], 'query2':['NR1H4','TRIP12']}, combined=True)
```
Output (truncated):
```
source native name p_values description term_size query_sizes intersection_sizes effective_domain_size parents
GO:MF GO:1902122 chenodeoxycholic acid binding [0.024822026073022193, 0.04964405214614093] "Interacting selectively and non-covalently wi... 1 [1, 2] [1, 1] 17516 [GO:0032052, GO:0005496]
GO:MF GO:0035257 nuclear hormone receptor binding [1.0, 0.033391754400990514] "Interacting selectively and non-covalently wi... 154 [1, 2] [1, 2] 17516 [GO:0051427, GO:0061629]
GO:MF GO:0051427 hormone receptor binding [1.0, 0.04929258983003374] "Interacting selectively and non-covalently wi... 187 [1, 2] [1, 2] 17516 [GO:0005102]
```
### g:Convert (convert)
```python
from gprofiler import GProfiler
gp = GProfiler(return_dataframe=True)
gp.convert(organism='hsapiens',
query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'],
target_namespace='ENTREZGENE_ACC')
```
Output:
```
incoming converted n_incoming n_converted name description namespaces query
NR1H4 9971 1 1 NR1H4 nuclear receptor subfamily 1 group H member 4 ... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1
TRIP12 9320 2 1 TRIP12 thyroid hormone receptor interactor 12 [Source... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1
UBC 7316 3 1 UBC ubiquitin C [Source:HGNC Symbol;Acc:HGNC:12468] ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1
FCRL3 115352 4 1 FCRL3 Fc receptor like 3 [Source:HGNC Symbol;Acc:HGN... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1
PLXNA3 55558 5 1 PLXNA3 plexin A3 [Source:HGNC Symbol;Acc:HGNC:9101] ENTREZGENE,HGNC,WIKIGENE query_1
GDNF 2668 6 1 GDNF glial cell derived neurotrophic factor [Source... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1
VPS11 55823 7 1 VPS11 VPS11, CORVET/HOPS core subunit [Source:HGNC S... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1
PLXNA3 55558 5 1 PLXNA3 plexin A3 [Source:HGNC Symbol;Acc:HGNC:9101] ENTREZGENE,HGNC,WIKIGENE query_1
```
`incoming` column lists the input gene, `converted` lists the gene in the target namespace (Entrez Gene accession number in this case).
### g:Orth (orth)
```python
from gprofiler import GProfiler
gp = GProfiler(return_dataframe=True)
gp.orth(organism='hsapiens',
query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'],
target='mmusculus')
```
Output:
```
incoming converted ortholog_ensg n_incoming n_converted n_result name description namespaces
NR1H4 ENSG00000012504 ENSMUSG00000047638 1 1 1 Nr1h4 nuclear receptor subfamily 1, group H, member ... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
TRIP12 ENSG00000153827 ENSMUSG00000026219 2 1 1 Trip12 thyroid hormone receptor interactor 12 [Source... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
UBC ENSG00000150991 ENSMUSG00000008348 3 1 1 Ubc ubiquitin C [Source:MGI Symbol;Acc:MGI:98889] ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
FCRL3 ENSG00000160856 N/A 4 1 1 N/A N/A ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
PLXNA3 ENSG00000130827 ENSMUSG00000031398 5 1 1 Plxna3 plexin A3 [Source:MGI Symbol;Acc:MGI:107683] ENTREZGENE,HGNC,WIKIGENE
GDNF ENSG00000168621 ENSMUSG00000022144 6 1 1 Gdnf glial cell line derived neurotrophic factor [S... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
VPS11 ENSG00000160695 ENSMUSG00000032127 7 1 1 Vps11 VPS11, CORVET/HOPS core subunit [Source:MGI Sy... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
```
`incoming` is the input gene, `converted` is the canonical Ensembl ID for the input gene,
`ortholog_ensg` is the canonical Ensembl ID for the orthologous gene in the target organism.
### g:SNPense (snpense)
```python
from gprofiler import GProfiler
gp = GProfiler(return_dataframe=True)
gp.snpense(query=['rs11734132', 'rs7961894', 'rs4305276', 'rs17396340'])
```
Output:
```
rs_id chromosome strand start end ensgs gene_names variants
rs11734132 -1 -1 [] [] {'intron_variant': 0, 'non_coding_transcript_v...
rs7961894 12 + 121927677 121927677 [ENSG00000158023] [WDR66] {'intron_variant': 3, 'non_coding_transcript_v...
rs4305276 2 + 240555596 240555596 [ENSG00000144504] [ANKMY1] {'intron_variant': 57, 'non_coding_transcript_...
rs17396340 1 + 10226118 10226118 [ENSG00000054523] [KIF1B] {'intron_variant': 8, 'non_coding_transcript_v...
```
* `rs_id` is the input rs-number
* `chromosome`, `strand`, `start` and `end` encode the position of the variation
* `ensgs` and `gene_names` are lists of protein-encoding genes associated with the rs-number.
* `variants` are predicted variant effects.
Raw data
{
"_id": null,
"home_page": "https://biit.cs.ut.ee/gprofiler/",
"name": "gprofiler-official",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "",
"author": "Uku Raudvere",
"author_email": "biit.support@ut.ee",
"download_url": "https://files.pythonhosted.org/packages/ec/c1/d9252620d09a064247d1623ebc4732d624921a2ed80a677f8b9ce61810dd/gprofiler-official-1.0.0.tar.gz",
"platform": "",
"description": "# gprofiler\n\n## Project description\n\nThe official Python 3 interface to the [g:Profiler](https://biit.cs.ut.ee/gprofiler/) \ntoolkit for enrichment analysis of functional (GO and other) terms, \nconversion between identifier namespaces and mapping orhologous genes in related organisms. \n\nIt has an optional dependency on pandas.\n\n### Installing gprofiler\n\nthe recommended way of installing gprofiler is using pip\n```bash\npip install gprofiler-official\n```\n\n### Legacy version \n\nThe `0.3.x` series of gprofiler-official is incompatible with the `1.0.x` series. We changed the major version number to \nsignify the breaking changes in the API. To install the previous version of `gprofiler-official`, use the command\n```bash\npip install gprofiler-official==0.3.5\n```\n\n## Tools:\n\nTo use any of the tools in the g:Profiler toolkit, first initialize the GProfiler object.\n\n```python\nfrom gprofiler import GProfiler\ngp = GProfiler(\n user_agent='ExampleTool', #optional user agent\n return_dataframe=True, #return pandas dataframe or plain python structures \n)\n```\n\n\n### g:GOSt (profile)\n\n```python\nfrom gprofiler import GProfiler\n\ngp = GProfiler(return_dataframe=True)\ngp.profile(organism='hsapiens',\n query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'])\n```\n\nOutput:\n```\nsource native name p_value significant description term_size query_size intersection_size effective_domain_size precision recall query parents\nGO:BP GO:0048585 negative regulation of response to stimulus 0.004229 True \"Any process that stops, prevents, or reduces ... 1610 7 6 17622 0.857143 0.003727 query_1 [GO:0048583, GO:0048519, GO:0050896]\nGO:BP GO:0002224 toll-like receptor signaling pathway 0.016351 True \"Any series of molecular signals generated as ... 133 7 3 17622 0.428571 0.022556 query_1 [GO:0002221]\nGO:BP GO:0048486 parasympathetic nervous system development 0.026199 True \"The process whose specific outcome is the pro... 19 7 2 17622 0.285714 0.105263 query_1 [GO:0048483, GO:0048731]\nGO:BP GO:0034162 toll-like receptor 9 signaling pathway 0.038733 True \"Any series of molecular signals generated as ... 23 7 2 17622 0.285714 0.086957 query_1 [GO:0002224]\nGO:BP GO:0002221 pattern recognition receptor signaling pathway 0.039782 True \"Any series of molecular signals generated as ... 179 7 3 17622 0.428571 0.016760 query_1 [GO:0002758]\nCORUM CORUM:5669 PlexinA3-Nrp1 complex 0.049767 True PlexinA3-Nrp1 complex 2 2 1 3620 0.500000 0.500000 query_1 [CORUM:0000000]\nCORUM CORUM:5759 PLXNA3-RANBPM complex 0.049767 True PLXNA3-RANBPM complex 2 2 1 3620 0.500000 0.500000 query_1 [CORUM:0000000]\n```\n\n* `source` is the code for the datasource\n* `native` is the ID for the enriched term/functional category in its native namespace.\n* `name` is the readable name for the enriched term, `description` is the longer description if available.\n* `p_value` is the corrected p-value for the \n* `term_size`, `query_size`, `intersection_size`, `effective_domain_size` are parameters to the hypergeometric test.\n* `query` is the name of the query and is significant if multiple queries were made in one call (e.g `gp.profile(query={'query1':['NR1H4'], 'query2':['NR1H4','TRIP12']})`)\n\nSetting the parameter `no_evidences=False` would add the column `intersections` (a list of genes that are annotated to the term and are present in the query )\nand the column `evidences` (a list of lists of GO evidence codes for the intersecting genes)\n\n\n\n\nNB! the parameter `combined` significantly changes the output structure by packing the results of distinct queries together.\nFor example:\n\n```python\ngp.profile(query={'query1':['NR1H4'], 'query2':['NR1H4','TRIP12']}, combined=True)\n```\nOutput (truncated):\n```\nsource native name p_values description term_size query_sizes intersection_sizes effective_domain_size parents\nGO:MF GO:1902122 chenodeoxycholic acid binding [0.024822026073022193, 0.04964405214614093] \"Interacting selectively and non-covalently wi... 1 [1, 2] [1, 1] 17516 [GO:0032052, GO:0005496]\nGO:MF GO:0035257 nuclear hormone receptor binding [1.0, 0.033391754400990514] \"Interacting selectively and non-covalently wi... 154 [1, 2] [1, 2] 17516 [GO:0051427, GO:0061629]\nGO:MF GO:0051427 hormone receptor binding [1.0, 0.04929258983003374] \"Interacting selectively and non-covalently wi... 187 [1, 2] [1, 2] 17516 [GO:0005102]\n```\n\n\n### g:Convert (convert)\n\n```python\nfrom gprofiler import GProfiler\n\ngp = GProfiler(return_dataframe=True)\ngp.convert(organism='hsapiens',\n query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'],\n target_namespace='ENTREZGENE_ACC')\n\n```\n\nOutput:\n```\nincoming converted n_incoming n_converted name description namespaces query\n NR1H4 9971 1 1 NR1H4 nuclear receptor subfamily 1 group H member 4 ... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1\n TRIP12 9320 2 1 TRIP12 thyroid hormone receptor interactor 12 [Source... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1\n UBC 7316 3 1 UBC ubiquitin C [Source:HGNC Symbol;Acc:HGNC:12468] ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1\n FCRL3 115352 4 1 FCRL3 Fc receptor like 3 [Source:HGNC Symbol;Acc:HGN... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1\n PLXNA3 55558 5 1 PLXNA3 plexin A3 [Source:HGNC Symbol;Acc:HGNC:9101] ENTREZGENE,HGNC,WIKIGENE query_1\n GDNF 2668 6 1 GDNF glial cell derived neurotrophic factor [Source... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1\n VPS11 55823 7 1 VPS11 VPS11, CORVET/HOPS core subunit [Source:HGNC S... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE query_1\n PLXNA3 55558 5 1 PLXNA3 plexin A3 [Source:HGNC Symbol;Acc:HGNC:9101] ENTREZGENE,HGNC,WIKIGENE query_1\n```\n\n`incoming` column lists the input gene, `converted` lists the gene in the target namespace (Entrez Gene accession number in this case). \n\n\n\n### g:Orth (orth)\n\n```python\nfrom gprofiler import GProfiler\n\ngp = GProfiler(return_dataframe=True)\ngp.orth(organism='hsapiens',\n query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'],\n target='mmusculus')\n\n```\nOutput:\n```\nincoming converted ortholog_ensg n_incoming n_converted n_result name description namespaces\n NR1H4 ENSG00000012504 ENSMUSG00000047638 1 1 1 Nr1h4 nuclear receptor subfamily 1, group H, member ... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE\n TRIP12 ENSG00000153827 ENSMUSG00000026219 2 1 1 Trip12 thyroid hormone receptor interactor 12 [Source... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE\n UBC ENSG00000150991 ENSMUSG00000008348 3 1 1 Ubc ubiquitin C [Source:MGI Symbol;Acc:MGI:98889] ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE\n FCRL3 ENSG00000160856 N/A 4 1 1 N/A N/A ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE\n PLXNA3 ENSG00000130827 ENSMUSG00000031398 5 1 1 Plxna3 plexin A3 [Source:MGI Symbol;Acc:MGI:107683] ENTREZGENE,HGNC,WIKIGENE\n GDNF ENSG00000168621 ENSMUSG00000022144 6 1 1 Gdnf glial cell line derived neurotrophic factor [S... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE\n VPS11 ENSG00000160695 ENSMUSG00000032127 7 1 1 Vps11 VPS11, CORVET/HOPS core subunit [Source:MGI Sy... ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE\n\n```\n\n`incoming` is the input gene, `converted` is the canonical Ensembl ID for the input gene, \n`ortholog_ensg` is the canonical Ensembl ID for the orthologous gene in the target organism.\n\n### g:SNPense (snpense)\n\n```python\nfrom gprofiler import GProfiler\n\ngp = GProfiler(return_dataframe=True)\ngp.snpense(query=['rs11734132', 'rs7961894', 'rs4305276', 'rs17396340'])\n```\nOutput:\n\n```\nrs_id chromosome strand start end ensgs gene_names variants\nrs11734132 -1 -1 [] [] {'intron_variant': 0, 'non_coding_transcript_v...\n rs7961894 12 + 121927677 121927677 [ENSG00000158023] [WDR66] {'intron_variant': 3, 'non_coding_transcript_v...\n rs4305276 2 + 240555596 240555596 [ENSG00000144504] [ANKMY1] {'intron_variant': 57, 'non_coding_transcript_...\nrs17396340 1 + 10226118 10226118 [ENSG00000054523] [KIF1B] {'intron_variant': 8, 'non_coding_transcript_v...\n\n```\n* `rs_id` is the input rs-number\n* `chromosome`, `strand`, `start` and `end` encode the position of the variation\n* `ensgs` and `gene_names` are lists of protein-encoding genes associated with the rs-number.\n* `variants` are predicted variant effects.\n\n",
"bugtrack_url": null,
"license": "",
"summary": "Functional enrichment analysis and more via the g:Profiler toolkit",
"version": "1.0.0",
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "df1b5a87c1a1da8f601c00a0ce4dedb5aab8a5cad6a0f4a5062c4da22a045072",
"md5": "a31adb48d09059958b1f48cf0d356879",
"sha256": "c582baf728e5a6cddac964e4085ca385e082c4ef0279e3af1a16a9af07ab5395"
},
"downloads": -1,
"filename": "gprofiler_official-1.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "a31adb48d09059958b1f48cf0d356879",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 9277,
"upload_time": "2019-04-02T10:52:17",
"upload_time_iso_8601": "2019-04-02T10:52:17.769447Z",
"url": "https://files.pythonhosted.org/packages/df/1b/5a87c1a1da8f601c00a0ce4dedb5aab8a5cad6a0f4a5062c4da22a045072/gprofiler_official-1.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ecc1d9252620d09a064247d1623ebc4732d624921a2ed80a677f8b9ce61810dd",
"md5": "27af90e2bdce5603262f6b23f97679b0",
"sha256": "5015b47f10fbdcb59c57e342e815c9c07afbe57cd3984154f75b845ddef2445d"
},
"downloads": -1,
"filename": "gprofiler-official-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "27af90e2bdce5603262f6b23f97679b0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 9584,
"upload_time": "2019-04-02T10:52:19",
"upload_time_iso_8601": "2019-04-02T10:52:19.527145Z",
"url": "https://files.pythonhosted.org/packages/ec/c1/d9252620d09a064247d1623ebc4732d624921a2ed80a677f8b9ce61810dd/gprofiler-official-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2019-04-02 10:52:19",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "gprofiler-official"
}
JSON