Commit dd41a3e8 authored by liiskolb's avatar liiskolb
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v.0.1.9

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......@@ -2,3 +2,4 @@
.Rhistory
.RData
.Ruserdata
inst/doc
Package: gprofiler2
Type: Package
Title: Interface to the 'g:Profiler' Toolset
Version: 0.1.8
Version: 0.1.9
Author: Liis Kolberg <liis.kolberg@ut.ee>, Uku Raudvere <uku.raudvere@ut.ee>
Maintainer: Liis Kolberg <liis.kolberg@ut.ee>
Description: A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (<https://biit.cs.ut.ee/gprofiler>).
......@@ -15,8 +15,8 @@ BugReports: https://biit.cs.ut.ee/gprofiler/page/contact
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: jsonlite, RCurl, ggplot2, plotly, tidyr, crosstalk, plyr, grDevices, gridExtra, grid, viridisLite, dplyr
RoxygenNote: 7.1.0
Imports: jsonlite, RCurl, ggplot2, plotly, tidyr, crosstalk, grDevices, gridExtra, grid, viridisLite, dplyr
Depends: R (>= 3.5)
Suggests:
knitr,
......
Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: pdfLaTeX
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......@@ -4,8 +4,14 @@
\alias{gconvert}
\title{Gene ID conversion.}
\usage{
gconvert(query, organism = "hsapiens", target = "ENSG",
numeric_ns = "", mthreshold = Inf, filter_na = TRUE)
gconvert(
query,
organism = "hsapiens",
target = "ENSG",
numeric_ns = "",
mthreshold = Inf,
filter_na = TRUE
)
}
\arguments{
\item{query}{vector that can consist of mixed types of gene IDs (proteins, transcripts, microarray IDs, etc), SNP IDs, chromosomal intervals or term IDs.}
......@@ -16,7 +22,7 @@ concatenating the first letter of the name and the family name. Example: human
\item{target}{target namespace.}
\item{numeric_ns}{namespace to use for fully numeric IDs.}
\item{numeric_ns}{namespace to use for fully numeric IDs (\href{https://biit.cs.ut.ee/gprofiler/page/namespaces-list}{list of available namespaces}).}
\item{mthreshold}{maximum number of results per initial alias to show. Shows all by default.}
......@@ -26,6 +32,8 @@ corresponding target.}
\value{
The output is a data.frame which is a table closely corresponding to the
web interface output.
The result fields are further described in the \href{https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html}{vignette}.
}
\description{
Interface to the g:Profiler tool g:Convert (\url{https://biit.cs.ut.ee/gprofiler/convert}) that uses the information in Ensembl databases to handle hundreds of types of identifiers for genes, proteins, transcripts, microarray probesets, etc, for many species,
......
......@@ -4,9 +4,14 @@
\alias{gorth}
\title{Orthology search.}
\usage{
gorth(query, source_organism = "hsapiens",
target_organism = "mmusculus", numeric_ns = "", mthreshold = Inf,
filter_na = TRUE)
gorth(
query,
source_organism = "hsapiens",
target_organism = "mmusculus",
numeric_ns = "",
mthreshold = Inf,
filter_na = TRUE
)
}
\arguments{
\item{query}{vector of gene IDs to be translated.}
......@@ -19,7 +24,7 @@ mouse - 'mmusculus'.}
the first letter of the name and the family name. Example: human - 'hsapiens',
mouse - 'mmusculus'.}
\item{numeric_ns}{namespace to use for fully numeric IDs.}
\item{numeric_ns}{namespace to use for fully numeric IDs (\href{https://biit.cs.ut.ee/gprofiler/page/namespaces-list}{list of available namespaces}).}
\item{mthreshold}{maximum number of ortholog names per gene to show.}
......@@ -29,6 +34,8 @@ corresponding target name.}
\value{
The output is a data.frame which is a table closely corresponding to the
web interface output.
The result fields are further described in the \href{https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html}{vignette}.
}
\description{
Interface to the g:Profiler tool g:Orth (\url{https://biit.cs.ut.ee/gprofiler/orth}) that, given a target organism, retrieves the genes of the target organism that are similar in sequence to the source organism genes in the input.
......
......@@ -4,14 +4,24 @@
\alias{gost}
\title{Gene list functional enrichment.}
\usage{
gost(query, organism = "hsapiens", ordered_query = FALSE,
multi_query = FALSE, significant = TRUE, exclude_iea = FALSE,
measure_underrepresentation = FALSE, evcodes = FALSE,
user_threshold = 0.05, correction_method = c("g_SCS", "bonferroni",
"fdr", "false_discovery_rate", "gSCS", "analytical"),
gost(
query,
organism = "hsapiens",
ordered_query = FALSE,
multi_query = FALSE,
significant = TRUE,
exclude_iea = FALSE,
measure_underrepresentation = FALSE,
evcodes = FALSE,
user_threshold = 0.05,
correction_method = c("g_SCS", "bonferroni", "fdr", "false_discovery_rate", "gSCS",
"analytical"),
domain_scope = c("annotated", "known", "custom", "custom_annotated"),
custom_bg = NULL, numeric_ns = "", sources = NULL,
as_short_link = FALSE)
custom_bg = NULL,
numeric_ns = "",
sources = NULL,
as_short_link = FALSE
)
}
\arguments{
\item{query}{vector, or a (named) list of vectors for multiple queries, that can consist of mixed types of gene IDs (proteins, transcripts, microarray IDs, etc), SNP IDs, chromosomal intervals or term IDs.}
......@@ -24,7 +34,7 @@ used to get GSEA style p-values.}
\item{multi_query}{in case of multiple gene lists, returns comparison table of these lists.
If enabled, the result data frame has columns named 'p_values', 'query_sizes', 'intersection_sizes' with vectors showing values in the order of input queries.
To get the results in a long format set 'multi_query' to FALSE and just input query list of multiple gene vectors.}
Set 'multi_query' to FALSE and simply input query as list of multiple gene vectors to get the results in a long format.}
\item{significant}{whether all or only statistically significant results should
be returned.}
......@@ -46,7 +56,7 @@ This parameter does not work if 'multi_query' is set to TRUE.}
\item{custom_bg}{vector of gene names to use as a statistical background. If given, the domain_scope is by default set to "custom", if domain_scope is set to "custom_annotated", then this is used instead.}
\item{numeric_ns}{namespace to use for fully numeric IDs.}
\item{numeric_ns}{namespace to use for fully numeric IDs (\href{https://biit.cs.ut.ee/gprofiler/page/namespaces-list}{list of available namespaces}).}
\item{sources}{a vector of data sources to use. Currently, these include
GO (GO:BP, GO:MF, GO:CC to select a particular GO branch), KEGG, REAC, TF,
......@@ -63,7 +73,7 @@ A named list where 'result' contains data.frame with the enrichment analysis res
If 'evcodes' is set, the return value includes columns 'evidence_codes' and 'intersection'.
The latter conveys info about the intersecting genes between the corresponding query and term.
The result fields are further described in \url{https://biit.cs.ut.ee/gprofiler_beta/page/apis#gost_query_results}
The result fields are further described in the \href{https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html}{vignette}.
If 'as_short_link' is set to TRUE, then the result is a character short-link to see and share corresponding results via the g:Profiler web tool.
}
......
......@@ -4,10 +4,14 @@
\alias{gostplot}
\title{Manhattan plot of functional enrichment results.}
\usage{
gostplot(gostres, capped = TRUE, interactive = TRUE, pal = c(`GO:MF`
= "#dc3912", `GO:BP` = "#ff9900", `GO:CC` = "#109618", KEGG = "#dd4477",
REAC = "#3366cc", WP = "#0099c6", TF = "#5574a6", MIRNA = "#22aa99", HPA
= "#6633cc", CORUM = "#66aa00", HP = "#990099"))
gostplot(
gostres,
capped = TRUE,
interactive = TRUE,
pal = c(`GO:MF` = "#dc3912", `GO:BP` = "#ff9900", `GO:CC` = "#109618", KEGG =
"#dd4477", REAC = "#3366cc", WP = "#0099c6", TF = "#5574a6", MIRNA = "#22aa99", HPA =
"#6633cc", CORUM = "#66aa00", HP = "#990099")
)
}
\arguments{
\item{gostres}{named list from gost() function (with names 'result' and 'meta')}
......
......@@ -2,7 +2,7 @@
% Please edit documentation in R/gprofiler2.R
\name{gsnpense}
\alias{gsnpense}
\title{Convert SNP rs numbers to genes.}
\title{Convert SNP rs identifiers to genes.}
\usage{
gsnpense(query, filter_na = TRUE)
}
......@@ -15,10 +15,12 @@ corresponding target name.}
\value{
The output is a data.frame which is a table closely corresponding to the
web interface output. Columns 'ensgs' and 'gene_names' can contain list of multiple values.
The result fields are further described in the \href{https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html}{vignette}.
}
\description{
Interface to the g:Profiler tool g:SNPense (\url{https://biit.cs.ut.ee/gprofiler/snpense}) that maps SNP rs identifiers to chromosome positions, genes and variant effects.
Available only for human SNPs.
Available only for human variants.
}
\examples{
gsnpense(c("rs11734132", "rs7961894", "rs4305276", "rs17396340", "rs3184504"))
......
......@@ -4,8 +4,13 @@
\alias{publish_gostplot}
\title{Create and save an annotated Manhattan plot of enrichment results.}
\usage{
publish_gostplot(p, highlight_terms = NULL, filename = NULL,
width = NA, height = NA)
publish_gostplot(
p,
highlight_terms = NULL,
filename = NULL,
width = NA,
height = NA
)
}
\arguments{
\item{p}{ggplot object from gostplot(gostres, interactive = FALSE) function}
......@@ -29,7 +34,7 @@ The plot is very similar to the one shown in the g:GOSt web tool after clicking
\examples{
gostres <- gost(c("Klf4", "Pax5", "Sox2", "Nanog"), organism = "mmusculus")
p <- gostplot(gostres, interactive = FALSE)
publish_gostplot(p, highlight_terms = c("GO:0001010", "WP:WP1763"))
publish_gostplot(p, highlight_terms = c("GO:0001010", "REAC:R-MMU-8939245"))
}
\author{
Liis Kolberg <liis.kolberg@ut.ee>
......
......@@ -4,9 +4,14 @@
\alias{publish_gosttable}
\title{Create and save a table with the functional enrichment analysis results.}
\usage{
publish_gosttable(gostres, highlight_terms = NULL, use_colors = TRUE,
show_columns = c("source", "term_name", "term_size",
"intersection_size"), filename = NULL)
publish_gosttable(
gostres,
highlight_terms = NULL,
use_colors = TRUE,
show_columns = c("source", "term_name", "term_size", "intersection_size"),
filename = NULL,
ggplot = TRUE
)
}
\arguments{
\item{gostres}{named list from gost() function (with names 'result' and 'meta') or a data frame that has columns named "term_id" and "p_value(s)".}
......@@ -18,6 +23,8 @@ publish_gosttable(gostres, highlight_terms = NULL, use_colors = TRUE,
\item{show_columns}{names of additional columns to show besides term_id and p_value. By default the output table shows additional columns named "source", "term_name", "term_size", "intersection_size"}
\item{filename}{file name to create on disk and save the annotated plot. Filename extension should be from c("png", "pdf", "jpeg", "tiff", "bmp").}
\item{ggplot}{if FALSE, then the function returns a gtable object.}
}
\value{
The output is a ggplot object.
......@@ -31,7 +38,7 @@ The output table is very similar to the one shown under the Manhattan plot.
}
\examples{
gostres <- gost(c("Klf4", "Pax5", "Sox2", "Nanog"), organism = "mmusculus")
publish_gosttable(gostres, highlight_terms = c("GO:0001010", "WP:WP1763"))
publish_gosttable(gostres, highlight_terms = c("GO:0001010", "REAC:R-MMU-8939245"))
}
\author{
Liis Kolberg <liis.kolberg@ut.ee>
......
......@@ -2,7 +2,7 @@
% Please edit documentation in R/gprofiler2.R
\name{random_query}
\alias{random_query}
\title{Generate a random gene list.}
\title{Generate a random gene list for testing.}
\usage{
random_query(organism = "hsapiens")
}
......
<?xml version="1.0" encoding="utf-8"?>
<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only" default-locale="en-US" page-range-format="minimal">
<info>
<title>BioMed Central</title>
<id>http://www.zotero.org/styles/biomed-central</id>
<link href="http://www.zotero.org/styles/biomed-central" rel="self"/>
<link href="http://bmcbioinformatics.biomedcentral.com/submission-guidelines/preparing-your-manuscript/research-article" rel="documentation"/>
<author>
<name>Robert M Flight</name>
<email>rflight79@gmail.com</email>
</author>
<contributor>
<name>Sebastian Karcher</name>
</contributor>
<category citation-format="numeric"/>
<category field="medicine"/>
<category field="biology"/>
<updated>2017-03-18T03:58:52+00:00</updated>
<rights license="http://creativecommons.org/licenses/by-sa/3.0/">This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License</rights>
</info>
<locale xml:lang="en">
<date form="text" delimiter=" ">
<date-part name="day"/>
<date-part name="month" form="short" strip-periods="true"/>
<date-part name="year"/>
</date>
<terms>
<term name="collection-editor" form="long">
<single>Series editor</single>
<multiple>Series editors</multiple>
</term>
</terms>
</locale>
<macro name="author">
<names variable="author">
<name sort-separator=" " initialize-with="" name-as-sort-order="all" delimiter=", " delimiter-precedes-last="always"/>
<label prefix=", "/>
<substitute>
<names variable="editor"/>
</substitute>
</names>
</macro>
<macro name="editor">
<names variable="editor" suffix=".">
<name sort-separator=" " initialize-with="" name-as-sort-order="all" delimiter=", " delimiter-precedes-last="always"/>
<label prefix=", "/>
</names>
</macro>
<macro name="publisher">
<group delimiter="; ">
<group delimiter=": ">
<choose>
<if type="thesis" match="none">
<text variable="publisher-place"/>
</if>
</choose>
<text variable="publisher"/>
</group>
<text macro="year-date"/>
</group>
</macro>
<macro name="container-title">
<choose>
<if type="article-journal" match="any">
<text variable="container-title" form="short" strip-periods="true"/>
</if>
<else>
<text variable="container-title"/>
</else>
</choose>
</macro>
<macro name="edition">
<choose>
<if is-numeric="edition">
<group delimiter=" ">
<number variable="edition" form="ordinal"/>
<text term="edition" form="long" suffix="."/>
</group>
</if>
<else>
<text variable="edition" suffix="."/>
</else>
</choose>
</macro>
<macro name="year-date">
<date variable="issued" form="numeric" date-parts="year"/>
</macro>
<macro name="access">
<choose>
<if variable="URL">
<choose>
<if variable="DOI">
<text variable="DOI" prefix="doi:"/>
</if>
<else>
<group delimiter=". ">
<text variable="URL"/>
<group delimiter=" ">
<text term="accessed" text-case="capitalize-first"/>
<date variable="accessed" form="text"/>
</group>
</group>
</else>
</choose>
</if>
</choose>
</macro>
<citation collapse="citation-number">
<sort>
<key variable="citation-number"/>
</sort>
<layout prefix="[" suffix="]" delimiter=", ">
<text variable="citation-number"/>
</layout>
</citation>
<bibliography et-al-min="7" et-al-use-first="6">
<layout suffix=".">
<text variable="citation-number" suffix=". "/>
<group>
<group delimiter=". ">
<text macro="author"/>
<text variable="title"/>
<choose>
<if type="bill book graphic legal_case legislation motion_picture report song thesis" match="any">
<group delimiter=". " prefix=" ">
<text macro="edition"/>
<text variable="genre"/>
<text macro="publisher"/>
</group>
</if>
<else-if type="chapter paper-conference" match="any">
<group delimiter=". ">
<group delimiter=": ">
<text term="in" text-case="capitalize-first"/>
<group delimiter=". ">
<text macro="editor"/>
<text macro="container-title"/>
</group>
</group>
<text macro="edition"/>
<text macro="publisher"/>
<group delimiter=" ">
<label variable="page" form="short" plural="never"/>
<text variable="page"/>
</group>
</group>
</else-if>
<else>
<text macro="container-title"/>
<group delimiter=";">
<text macro="year-date"/>
<group>
<text variable="volume"/>
<!-- This will hopefully deal with supplements at least reasonably well -->
<choose>
<if is-numeric="issue"/>
<else>
<text variable="issue" prefix=" "/>
</else>
</choose>
<text variable="page" prefix=":"/>
</group>
</group>
</else>
</choose>
<text macro="access"/>
</group>
</group>
</layout>
</bibliography>
</style>
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@Article{gp,
title={g: Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)},
author={Raudvere, Uku and Kolberg, Liis and Kuzmin, Ivan and Arak, Tambet and Adler, Priit and Peterson, Hedi and Vilo, Jaak},
journal={Nucleic acids research},
volume={47},
number={W1},
pages={W191--W198},
year={2019},
publisher={Oxford University Press}
}
---
title: "Gene list functional enrichment analysis and namespace conversion with gprofiler2"
#rmarkdown::html_vignette
date: "`r Sys.Date()`"
output:
prettydoc::html_pretty:
theme: cayman
highlight: github
toc: true
bibliography: extdata/references.bib
link-citations: yes
csl: extdata/biomed-central.csl
vignette: >
%\VignetteIndexEntry{gprofiler2}
%\VignetteEngine{knitr::rmarkdown}
......@@ -37,7 +41,7 @@ knitr::opts_chunk$set(
## Overview
[gprofiler2](https://CRAN.R-project.org/package=gprofiler2) provides an R interface to the widely used web toolset g:Profiler ([https://biit.cs.ut.ee/gprofiler](https://biit.cs.ut.ee/gprofiler)).
[gprofiler2](https://CRAN.R-project.org/package=gprofiler2) provides an R interface to the widely used web toolset g:Profiler ([https://biit.cs.ut.ee/gprofiler](https://biit.cs.ut.ee/gprofiler)) @gp.
The toolset performs functional enrichment analysis and visualization of gene lists, converts gene/protein/SNP identifiers to numerous namespaces, and maps orthologous genes across species.
[g:Profiler](https://biit.cs.ut.ee/gprofiler) relies on [Ensembl databases](https://www.ensembl.org/index.html) as the primary data source and follows their release cycle for updates.
......@@ -269,7 +273,7 @@ The `gost` results can also be visualized with a table. The `publish_gosttable`
The `highlight_terms` can be a vector of term IDs or a subset of the results.
```{r fig.width = 9.5, fig.height = 3}
pt <- publish_gosttable(gostres, highlight_terms = gostres$result[c(1:2,10,100:102,120,124,125),],
publish_gosttable(gostres, highlight_terms = gostres$result[c(1:2,10,100:102,120,124,125),],
use_colors = TRUE,
show_columns = c("source", "term_name", "term_size", "intersection_size"),
filename = NULL)
......@@ -286,7 +290,7 @@ gostplot(multi_gostres2, capped = TRUE, interactive = TRUE)
Note that if a term is clicked on one of the Manhattan plots, it is also highlighted in the others (if it is present) enabling to compare the multiple queries. The insignificant terms are shown with lighter color.
```{r fig.width = 10, fig.height = 2, warning = F}
pt2 <- publish_gosttable(multi_gostres1,
publish_gosttable(multi_gostres1,
highlight_terms = multi_gostres1$result[c(1, 24, 82, 176, 204, 234),],
use_colors = TRUE,
show_columns = c("source", "term_name", "term_size"),
......@@ -340,6 +344,72 @@ For example, the same query in the web tool is available from [https://biit.cs.u
----
## Creating a Generic Enrichment Map (GEM) file for EnrichmentMap
Generic Enrichment Map (GEM) is a file format that can be used as an input for [Cytoscape EnrichmentMap application](http://apps.cytoscape.org/apps/enrichmentmap). In EnrichmentMap you can set the Analysis Type parameter as **Generic/gProfiler** and upload the required files: GEM file with enrichment results and GMT file that defines the annotations.
For a single query, the GEM file can be generated and saved using the following commands:
```{r}
gostres <- gost(query = c("X:1000:1000000", "rs17396340", "GO:0005005", "ENSG00000156103", "NLRP1"),
evcodes = TRUE, multi_query = FALSE,
sources = c("GO", "REAC", "MIRNA", "CORUM", "HP", "HPA", "WP"))
gem <- gostres$result[,c("term_id", "term_name", "p_value", "intersection")]
colnames(gem) <- c("GO.ID", "Description", "p.Val", "Genes")
gem$FDR <- gem$p.Val
gem$Phenotype = "+1"
gem <- gem[,c("GO.ID", "Description", "p.Val", "FDR", "Phenotype", "Genes")]
gem <- gem[order(gem$p.Val),]
head(gem)
```
The parameter `evcodes = TRUE` is necessary as it returns the column **intersection** with corresponding gene IDs that are annotated to the term.
Saving the file before uploading to Cytoscape:
```{r, eval=F}
write.table(gem, file = "extdata/gProfiler.gem.txt", sep = "\t", quote = F, row.names = F)
```
Here the parameter `file` should be the character string naming the file together with the path you want to save it to.
In addition to the GEM file, EnrichmentMap requires also the data source description GMT file as an input. For example, if you are using g:Profiler default data sources and your input query consists of human ENSG identifiers, then the required GMT file is available from [https://biit.cs.ut.ee/gprofiler/static/gprofiler_full_hsapiens.ENSG.gmt](https://biit.cs.ut.ee/gprofiler/static/gprofiler_full_hsapiens.ENSG.gmt). Note that this file does not include annotations from KEGG and Transfac as we are restricted by data source licenses that do not allow us to share these two data sources with our users. This means that the enrichment results in the GEM file cannot include results from these resources, otherwise you will get an error from the Cytoscape application. This can be assured by setting appropriate values to the `sources` parameter in the `gost()` function.
For other organisms, the GMT files are downloadable from the [g:Profiler web page](https://biit.cs.ut.ee/gprofiler) under the *Data sources* section, after setting a suitable value for the organism. If you are using a custom GMT file for you analysis, then this should be uploaded to EnrichmentMap.
In case you want to compare **multiple queries** in EnrichmentMap you could generate individual GEM files for each of the queries and upload these as separate Data sets. This EnrichmentMap option enables you to browse, edit and compare multiple networks simultaneously by color-coding different uploaded Data sets.
These files can be generated with the following commands (note that the parameter is still set to `multi_query = FALSE`):
```{r, eval=F}
# enrichment for two input gene lists
multi_gostres <- gost(query = list("chromX" = c("X:1000:1000000", "rs17396340",
"GO:0005005", "ENSG00000156103", "NLRP1"),
"chromY" = c("Y:1:10000000", "rs17396340",
"GO:0005005", "ENSG00000156103", "NLRP1")),
evcodes = TRUE, multi_query = FALSE,
sources = c("GO", "REAC", "MIRNA", "CORUM", "HP", "HPA", "WP"))
# format to GEM
gem <- multi_gostres$result[,c("query", "term_id", "term_name", "p_value", "intersection")]
colnames(gem) <- c("query", "GO.ID", "Description", "p.Val", "Genes")
gem$FDR <- gem$p.Val
gem$Phenotype = "+1"
# write separate files for queries
library(dplyr)
gem %>% group_by(query) %>%
group_walk(~
write.table(data.frame(.x[,c("GO.ID", "Description", "p.Val", "FDR", "Phenotype", "Genes")]),
file = file.path("extdata", paste0("gProfiler_", unique(.y$query), ".gem.txt")),
sep = "\t", quote = F, row.names = F)
)
```
----
## Gene identifier conversion with `gconvert`
`gconvert` enables to map between genes, proteins, microarray probes, common names, various database identifiers, etc, from numerous [databases](https://biit.cs.ut.ee/gprofiler/page/namespaces-list) and for many [species](https://biit.cs.ut.ee/gprofiler/page/organism-list).
......@@ -462,3 +532,5 @@ Uku Raudvere, Liis Kolberg, Ivan Kuzmin, Tambet Arak, Priit Adler, Hedi Peterson
If you have questions or issues, please write to biit.support@ut.ee
## References
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