The official Python 3 interface to the g:Profiler 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.
the recommended way of installing gprofiler is using pip
pip install gprofiler-official
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
pip install gprofiler-official==0.3.5
To use any of the tools in the g:Profiler toolkit, first initialize the GProfiler object.
from gprofiler import GProfiler
gp = GProfiler(
user_agent='ExampleTool', #optional user agent
return_dataframe=True, #return pandas dataframe or plain python structures
)
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 datasourcenative 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 theterm_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:
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]
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).
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.
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-numberchromosome, strand, start and end encode the position of the variationensgs and gene_names are lists of protein-encoding genes associated with the rs-number.variants are predicted variant effects.FAQs
Functional enrichment analysis and more via the g:Profiler toolkit
We found that gprofiler-official demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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