ShinyGO: a graphical gene-set enrichment tool for animals and plants

4/21/2024: New barchart with GO terms on the bars.

4/12/2024: Max set size is increased to 5000 from 2000. Some meaningful GO terms (RNA biosynthetic proc.) contains 4000+ genes.

You can still use the old version (ShinyGO V0.77). To support this effort, please cite our paper, like over 2000 users did. Just including URL is not enough. Mirror sever http://ge-lab.org/go/ hosted by NSF-funded JetStream2. Email Jenny (gelabinfo@gmail.com) for questions, suggestions or data contributions. Follow Dr Ge on Twitter and LinkedIn for updates.


Feb. 11, 2022: Like ShinyGO but your genome is not covered? Customized ShinyGO is now available. Its database includes several custom genomes requested by users. To request to add a new species/genome, fill in this Form.

GO Enrichment analysis, plus a lot more!

Just paste your gene list to get enriched GO terms and othe pathways for over 14,000 species. based on annotation from Ensembl and STRING-db. Produce KEGG pathway diagrams with your genes highlighted, hierarchical clustering trees and networks summarizing overlapping terms/pathways, protein-protein interaction networks, gene characterristics plots, and enriched promoter motifs.
















Top Pathways shown above Results on all Pathways

Methods

All query genes are converted to ENSEMBL gene IDs or STRING-db protein IDs. Our gene ID mapping and pathway data are mostly derived from these two sources. For the most researched species, we also manually compiled an extensive list of pathways from various public databases.

Enrichment P-value are derived using the hypergeometric test. To correct for multiple testing, False Discovery Rate (FDR) is calculated using the Benjamini-Hochberg method. Fold Enrichment of a pathway is defined as the ratio of the percentage of genes in your list to the corresponding percentage in the background genes.

We recommend that users provide their own list of background genes, which could include all genes that pass a minimal filter in RNA-seq analysis. If no background genes are uploaded, the default setting uses all protein-coding genes. Alternatively, you may select the option 'Use pathway DB for gene counts,' which calculates the background based on the total number of unique genes in the chosen pathway database, with a limit set between 5,000 and 30,000 genes. When this option is selected, any genes in the user's original list that are not in the pathway database are excluded.

For enrichment analysis, only pathways within specified size limits are considered. After the analysis, pathways are initially filtered by a user-defined FDR cutoff. Significant pathways are then ranked based on FDR, Fold Enrichment, or other metrics. When the 'Sort by average ranks (FDR & Fold Enrichment)' option is selected, pathways are sorted by the average of their ranks based on both FDR and Fold Enrichment. By selecting 'Sort by FDR, then by Fold Enrichment,' users first filter pathways by FDR and then sort those pathways by Fold Enrichment. The 'Remove redundancy' option eliminates similar pathways that share 95% of their genes and 50% of the words in their names, representing them with the pathway that has the highest significance.

Interpreting GO Enrichment Results

The Gene Ontology (GO) includes over tens of thousands of terms (functional categories), each tested individually for enrichment. These results are ranked, and only the top ones are displayed. Understanding this process is crucial for interpreting GO enrichment results.

  • P-value: Reflects the statistical significance of the enrichment. Lower values suggest a lower likelihood of the result occurring by chance (null hypothesis). FDR q-values adjust P-values for multiple testing to control the proportion of type I error.
  • Fold Enrichment: Measures the magnitude of enrichment. Higher values indicate stronger enrichment and are an important metric of effect size.
  • Pathway Genes: The total number of genes in a pathway or GO term.
  • nGenes: The number of genes in the pathway that overlap with your gene list.

Exercise caution when encountering FDR values of 0.01 or 0.001 for GO terms, as these levels often represent noise due to the vast number of terms tested. For a gene list of reasonable size, more significant results (FDR < 1E-5) are expected.

Large pathways, such as the cell cycle, often show smaller FDRs due to increased statistical power, while smaller pathways might have higher FDRs despite their biological relevance. Enrichment analysis tends to favor larger pathways.

With a default cutoff of FDR < 0.05, we often detect thousands of significant GO terms, though only a subset is shown. The method of filtering and ranking these terms is therefore crucial.

With large sample sizes, small differences can appear extremely significant. Therefore, ranking by FDR alone rarely makes biological sense. Fold enrichment should also be considered when prioritizing pathways as it reflects the strength of the enrichment. We offer several methods that take into account both FDR q-value and fold enrichment.

Many GO terms are closely related (e.g., Cell Cycle, Regulation of Cell Cycle) and can dominate the top 20, obscuring other pathways. To avoid this, consider examining the top 50 terms. Additionally, use tree plots and network plots to identify clusters of related GO terms and uncover overarching themes.

Discuss the most significant pathways first, even if they do not fit your narrative.

A hierarchical clustering tree summarizes the correlation among significant pathways listed in the Enrichment tab. Pathways with many shared genes are clustered together. Bigger dots indicate more significant P-values. The width of the plot can be changed by adjusting the width of your browser window.
Edge cutoff:
Download HTML Edges Nodes
Similar to the Tree tab, this interactive plot also shows the relationship between enriched pathways. Two pathways (nodes) are connected if they share 20% (default) or more genes. You can move the nodes by dragging them, zoom in and out by scrolling, and shift the entire network by click on an empty point and drag. Darker nodes are more significantly enriched gene sets. Bigger nodes represent larger gene sets. Thicker edges represent more overlapped genes.


Please select KEGG from the pathway databases to conduct enrichment analysis first. Then you can visualize your genes on any of the significant pathways. Only for some species.


Your genes are highlighted in red. Downloading pathway diagram from KEGG can take 3 minutes.
More info
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Your genes are grouped by functional categories defined by high-level GO terms.
The characteristics of your genes are compared with the rest in the genome. Chi-squared and Student's t-tests are run to see if your genes have special characteristics when compared with all the other genes or, if uploaded, a customized background.

The genes are represented by red dots. The purple lines indicate regions where these genes are statistically enriched, compared to the density of genes in the background. We scanned the genome with a sliding window. Each window is further divided into several equal-sized steps for sliding. Within each window we used the hypergeometric test to determine if your genes are significantly overrepresented. Essentially, the genes in each window define a gene set/pathway, and we carried out enrichment analysis. The chromosomes may be only partly shown as we use the last gene's location to draw the line. Mouse over to see gene symbols. Zoom in regions of interest.



Download


To validate your results independent of our algorithm and database, your genes are sent to STRING-db website for enrichment analysis. This also enables the retrieval of a protein-protein network. If it is running, please wait until it finishes. The second time it is faster.

ShinyGO is developed and maintained by a small team at South Dakota State University (SDSU). Our team consists of Xijin Ge (PI), Jianli Qi (research associate), and two talented graduate students (Emma Spors and Ben Derenge). None of us are trained as software engineers. But we share the passion about developing an user-friendly tool for all biologists, especially those who do not have access to bioinformaticians.

For feedbacks, email us, or file bug report or feature request on our GitHub repository, where you can also find the source code. For details, please see our paper and a detailed demo. ShinyGO shares many functionalities and databases with iDEP.

Citation (Just including URL is not enough!):
Ge SX, Jung D & Yao R, Bioinformatics 36:2628–2629, 2020. If you use the KEGG diagram, please also cite the papers for pathview, and KEGG.

Previous versions are still functional:
ShinyGO V0.77, based on Ensembl Release 104, archived on Jan. 5, 2024
ShinyGO V0.76, based on Ensembl Release 104 with revision, archived on September 2, 2022
ShinyGO V0.75, based on Ensembl Release 104 with revision, archived on April 4, 2022
ShinyGO V0.74, based on Ensembl Release 104, archived on Feb. 8, 2022
ShinyGO V0.65, based on Ensembl Release 103, archived on Oct. 15, 2021
ShinyGO V0.61, based on Ensembl Release 96, archived on May 23, 2020
ShinyGO V0.60, based on Ensembl Release version 96, archived on Nov 6, 2019
ShinyGO V0.51, based on Ensembl Release version 95, archived on May 20, 2019
ShinyGO V0.50, based on Ensembl Release version 92, archived on March 29, 2019
ShinyGO V0.41, based on Ensembl Release version 91, archived on July 11, 2018
Genomes based on STRING-db is marked as STRING-db. If the same genome is included in both Ensembl and STRING-db, users should use Ensembl annotation, as it is more updated and is supported in more functional modules.

Input:

A list of gene ids, separated by tab, space, comma or the newline characters. Ensembl gene IDs are used internally to identify genes. Other types of IDs will be mapped to Ensembl gene IDs using ID mapping information available in Ensembl BioMart.

Output:

Enriched GO terms and pathways:


In addition to the enrichment table, a set of plots are produced. If KEGG database is choosen, then enriched pathway diagrams are shown, with user's genes highlighted, like this one below:


Many GO terms are related. Some are even redundant, like "cell cycle" and "cell cycle process". To visualize such relatedness in enrichment results, we use a hierarchical clustering tree and network. In this hierarchical clustering tree, related GO terms are grouped together based on how many genes they share. The size of the solid circle corresponds to the enrichment FDR.


In this network below, each node represents an enriched GO term. Related GO terms are connected by a line, whose thickness reflects percent of overlapping genes. The size of the node corresponds to number of genes.


Through API access to STRING-db, we also retrieve a protein-protein interaction (PPI) network. In addition to a static network image, users can also get access to interactive graphics at the www.string-db.org web server.


ShinyGO also detects transcription factor (TF) binding motifs enriched in the promoters of user's genes.

Sources for human pathway databases:

Type

Subtype/Database name

#GeneSets

Source

Gene Ontology

Biological Process (BP)

15796

Ensembl 92

 

Cellular Component (CC)

1916

Ensembl 92

 

Molecular Function (MF)

4605

Ensembl 92

KEGG

KEGG

327

Release 86.1

Curated

Biocarta

249

Whichgenes 1.5

 

GeneSetDB.EHMN

55

GeneSetDB

 

Panther

168

1.0.4

 

HumanCyc

240

pathway Commons V9

 

INOH

576

pathway Commons V9

 

NetPath

27

pathway Commons V9

 

PID

223

pathway Commons V9

 

PSP

327

pathway Commons V9

 

Recon X

2339

pathway Commons V9

 

Reactome

2010

V64

 

Wiki

457

20180610

TF.Target

CircuitsDB.TF

829

V2012

 

ENCODE

181

V70.0

 

Marbach2016

628

regulatorycircuits Release 1.0

 

RegNetwork.TF

1400

7/1/2017

 

TFacts

428

Feb. 2012

 

tftargets.ITFP

1926

tftargets May,2017

 

tftargets.Neph2012

16476

tftargets May,2017

 

tftargets.TRED

131

tftargets May,2017

 

TRRUST

793

V2

miRNA.Targets

CircuitsDB.miRNA

140

V. 2012

 

GeneSetDB.MicroCosm

44

GeneSetDB

 

miRDB

2588

V 5.0

 

miRTarBase

2599

V 7.0

 

RegNetwork.miRNA

618

V. 2015

 

TargetScan

219

V7.2

MSigDB.Computational

Computational gene sets 

858

MSigDB 6.1

MSigDB.Curated

Literature

3465

MSigDB 6.1

MSigDB.Hallmark

hallmark

50

MSigDB 6.1

MSigDB.Immune

Immune system

4872

MSigDB 6.1

MSigDB.Location

Cytogenetic band

326

MSigDB 6.1

MSigDB.Motif

TF and miRNA Motifs

836

MSigDB 6.1

MSigDB.Oncogenic

Oncogenic signatures

189

MSigDB 6.1

PPI

BioGRID

15542

3.4.160

 

CORUM

2178

02.07.2017

 

BIND

3807

pathway Commons V9

 

DIP

2630

pathway Commons V9

 

HPRD

7141

pathway Commons V9

 

IntAct

11991

pathway Commons V9

Drug

GeneSetDB.MATADOR

266

GeneSetDB

 

GeneSetDB.SIDER

473

GeneSetDB

 

GeneSetDB.STITCH

4616

GeneSetDB

 

GeneSetDB.T3DB

846

GeneSetDB

 

SMPDB

699

pathway Commons V9

 

CTD

8758

pathway Commons V9

 

Drugbank

2563

pathway Commons V9

Other

GeneSetDB.CancerGenes

23

GeneSetDB

 

GeneSetDB.MethCancerDB

21

GeneSetDB

 

GeneSetDB.MethyCancer

54

GeneSetDB

 

GeneSetDB.MPO

3134

GeneSetDB

 

HPO

6785

May,2018

Total:

 

140,438

 

Sources for mouse pathway databases:

 

 

 

 

Type

Source

#Sets

Note

Co-expression

Literature

8,742

Differentially expressed genes from 2526 studies

 

MSigDB

3,964

Molecular Signature Database, v.6.0

 

L2L

248

List of lists,  v.2006.2

 

CancerGenes*

23

Cancer gene lists

 

GeneSigDB

494

Gene Signature Database,  R.4

Gene

GO_BP

11,943

V2017.5

Ontology

GO_MF

2,932

 

 

GO_CC

1,475

 

Curated

Biocarta*

176

Metabolic and signaling pathways

pathways

PANTHER

151

Ontology-based pathway database,  v3.4.1

 

WikiPathways*

146

Open platform for pathway curation

 

INOH*

73

Integrating network objects with hierarchies

 

NetPath*

25

Signal transduction pathways

Metabolic

KEGG

314

Metabolic pathways, R.82.0

pathways

EHMN*

53

Edinburgh human metabolic network

 

MouseCyc

321

Mouse Biochemical Pathways , v2013.7

Drug

CTD*

910

The Comparative Toxicogenomics Database

related

SIDER*

460

Side Effect Resource

 

MATADOR*

248

Manually Annotated Targets and Drugs Online Resource

 

DrugBank*

136

Open data drug and target database

 

SMPDB*

74

Small Molecule Pathway Database

miRNA

miRDB

1,912

miRNA target prediction and annotations, v 5.0

Target

microRNA.org

314

Predicted miRNA targets, v.R2010

Genes

Grimson et al.

179

Predicted miRNA targets. v.6.2

 

TarBase

84

Experimentally validated miRNA targets, v.6.0

 

miRTarBase

775

Experimentally validated miRNA targets, V6.1

 

MicroCosm

464

Predicted targets

 

PicTar

35

Predicted miRNA sites, v. 2007.3

TF Target

TFactS*

101

Predicted TF targets

Genes

TRED

99

Confirmed TF target genes, v.2013.7

 

CircuitsDB

94

Mixed miRNA/TF regulation, v. 2012

 

TRANSFAC

78

Confirmed TF binding sites, v7.0

Others

Location

341

Genomic location on chromosomes, v.2017

 

HPO*

1,518

The human phenotype ontology

 

STITCH*

3,929

Interaction networks of chemicals and proteins

 

MPO*

2,943

Mammalian Phenotype Ontology

 

T3DB*

722

Database of common toxins and their targets

 

PID*

193

Pathway Interaction Database

 

MethyCancer*

50

Human DNA methylation and cancer

 

MethCancerDB*

19

Aberrant DNA methylation in human cancer

 

Total

46,758

*Secondary data from GeneSetDB


Changes:

4/21/2024: New barchart with GO terms on the bars.

4/20/2024: UI adjustment

4/12/2024: Max set size is increased to 5000 from 2000. Some meaningful GO terms (RNA biosynthetic proc.) contains 4000+ genes.

1/5/2024: ShinyGO 0.80 becomes default. You have to select your species first. Database is updated to Ensembl release 107 which includes 620 species: 215 main, 177 metazoa, 124 plants, 33 protists and 1 bacteria. We also included 14,094 species from STRING-DB 11.5.

5/1/2023: ShinyGO 0.80 release in testing mode. Thanks to Jenny's hardwork, we update to Ensembl release 107 which includes 620 species: 215 main, 177 metazoa, 124 plants, 33 protists and 1 bacteria. We also included 14,094 species from STRING-DB 11.5.

Jan. 19, 2023: Thanks to a user's feedback, we found a serious bug in ShinyGO 0.76. As some genes are represented by multiple gene IDs in Ensebml, they are counted more than once in calculating enrichment. We believe this is fixed. If you pasted Ensembl gene IDs to ShinyGO 0.76 between April 4, 2022 and Jan. 19, 2023, please rerun your analysis. ShinyGO has not been throughly tested. Please always double check your results with other tools such as G:profiler, Enrichr, STRING-db, and DAVID.

Oct 26, 2022: V. 0.76.3 Add hover text. Change plot styles. When users select "Sort by Fold Enrichment", the minimum pathway size is raised to 10 to filter out noise from tiny gene sets.

Sept 28, 2022: In ShinyGO 0.76.2, KEGG is now the default pathway database. More importantly, we reverted to 0.76 for default gene counting method, namely all protein-coding genes are used as the background by default. The new feature introduced in 0.76.1, which uses the pathway database to determine total number of genes in the background, can be turned on as an option ('Use pathway database for gene counts'). This is based on feedback from some users that when using smaller pathway databases, such as KEGG, the new method changes the P values substantially.

Sept 3, 2022: ShinyGO 0.76.1. In this small improvement, we improved how we count the number of genes for calculating P value. A gene must match at least one pathway in the selected pathway database. Otherwise this gene is ignored in the calculation of P values based on hypergeometric distribution. This applies to both query and background genes.

April 19, 2022: ShinyGO 0.76 released. Improved pathway filtering, pathway sorting, figure downloading. Version 0.75 is available here.

April 17, 2022: Add more flexiblity for download figures in PDF, SVG and high-res PNG.

April 8, 2022: Add features to remove redundant pathways. Add filter to remove extrmely large or small pathways. Changed interface to always show KEGG tab.

Mar. 7, 2022: Fixed an R library issue affected KEGG diagrams for some organisms.

Feb. 26, 2022: Fixed a bug regarding the Plot tab when background genes are used. Background genes were not correctly used to calculate the distributions of various gene characteristics. If these plots are important in your study, please re-analyze your genes.

Feb. 19, 2022: R upgraded from 4.05 to 4.1.2. This solved the STRING API issues. Some Bioconductor packages are also upgraded.

Feb. 8, 2022: ShinyGO v0.75 officially released. Old versions are still available. See the last tab.

Nov. 15, 2021: Database update. ShinyGO v0.75 available in testing mode. It includes Ensembl database update, new species from Ensembl Fungi and Ensembl Protists, and STRINGdb (5090 species) update to 11.5.

Oct25, 2021: Interactive genome plot. Identificantion of genomic regions signficantly enriched with user genes.

Oct.23, 2021: Version 0.741 A fully customizable enrichment chart! Switch between bar, dot or lollipop plots. Detailed gene informations with links on the Genes tab.

Oct. 15, 2021: Version 0.74. Database updated to Ensembl Release 104 and STRING v11. We now recommends the use of background genes in enrichment analysis. V.0.74 is much faster with even large set of background genes.

6/6/2021: V0.66 Adjusted interface.
6/2/2021: V0.66 add customized background genes.
5/23/2021: V0.65 Database update to Ensembl 103 and STRING-db v11.
11/3/2019: V 0.61 Improved visualization based on suggestions from reviewers. Interactive networks.
5/20/2019: V0.60 Upgraded to Ensembl Biomart 96. Add annotation from STRING-db v10
3/29/2019: V0.51 Update annotation to Ensembl release 95. Interface change. Demo gene lists. Error messages.
9/10/2018: V0.5 Upgraded to Ensembl Biomart 92
4/30/2018: V0.42 changed figure configurations for tree.
4/27/2018: V0.41 Change to ggplot2, add grid and gridExtra packages
4/24/2018: V0.4 Add STRING API, KEGG diagram, tree display and network.