1- General info

GeneCodis is an API and web tool for the Singular and Modular Enrichment Analysis (SEA and MEA) of lists of genes, proteins, and regulatory elements: miRNAs, transcription factors (TFs), and CpGs. Look CoAnnotation in the next panel for more information on the Modular Enrichment Analysis. The enrichment analysis of regulatory elements, requires a different approach. Due to a gene selection bias (some genes are more targeted than others) in platforms and regulation networks it requires a test based on a non-central hypergeometric distribution, concretely, we use the Wallenius distribution. Our method is based on the GOseq implementation. Read the paper for more information: Functional Enrichment Analysis of Regulatory Elements (Biomedicines 2022).

We have updated the annotations and organisms. Annotations are now divided in four general collections, Functional, Regulatory with miRNAs-based annotations, Drugs and Phenotypes. The results are also displayed differently, we are now using D3.js to create two interactive visualizations, a gene-, mirna-annotation network and a bars chart.

In case that you are missing any functionality, organisms, annotation or even if you want some kind of new feature please do not hesitate in contact us at statsandcompbio@outlook.com since we are continually working on creating a complete updated GeneCodis, feedback is always appreciated. We want GeneCodis to be an open source project and share it with all developers and researchers, so feel free to visit our Github repository and fork your own branch.

2- Analysis options

1º Choose an organism

Select one of the possible organisms listed in the drop down menu. See section 5- Organisms supported.

2º Choose the type of input

Select between, genes/proteins, TFs, CpGs and miRNAS.

If you select miRNAs, below the input box appears the converter between mature and precursos miRNAs ids.

3º Paste your query list

Genes, proteins, transcription factors, CpGs or miRNAs in any of the allowed nomenclatures, as explained in section 4- Input allowed

4º Choose the annotations

The available annotations vary for each organism, check section 5- Organisms supported.

5º Advanced Options

Enrichment stats

You can select the test with the standard hypergeometric or Wallenius' noncentral hypergeometric distribution. The Wallenius test approach is reccommended to overcome bias effects. This approach is widely used and is implemented following R's packages GOseq, methylGSA and missMethyl GOmeth. You can read the articles by clicking in the previous links.

Universe scope

You can choose between using as background set only the genes in the selected database, "Annotated" or all the genes included in our database for the selected organism, "Whole".

CoAnnotation analysis

Discover groups of annotations that share a minimun number of genes to test their over-representation significance.

CoAnnotation algorithm

The groups of annotations or itemesets, CoAnnotations in our context, are discovered by means of the Frequent Pattern (FP) algorithm. Previous versions of GeneCodis are based on the Apriori algorithm but the FP methods are more timely efficient. We offer two variations: FP Growth, to discover closed CoAnnotations, and FP Max, to discover closed maximal CoAnnotations. FP Max is faster. A closed CoAnnotation is a combination of annotations that share a minimun number of genes. A closed CoAnnotation is maximal when it is the superset of other closed CoAnnotations. In the following image, the minimun support can be regarded as the minimun number of genes, being the numbers the genes and the letters the annotations. Source from MLxtend

Minimal number of input elements in CoAnnotation

The annotations in the co-annotation must share this minimum number of miRNAs or genes. The minimun is always 3 or the input size if less than 3 elements are analysed. The minimun support is increased automatically by one if the FP algorithm takes more than 5 minutes to finish, or it will be reduced if no CoAnnotations are returned.

Compare two lists

Select Yes to analyse the common and unique elements of two input lists. If this last option is selected, you will be able to name each list, if not they are called "input1" and "input 2", respectively.

Custom universe

By default, GeneCodis uses all the genes or miRNAs of the organism that can be measured according to our database (see section 4- Input allowed). It will differ in the input type selected. You can paste here all the genes, proteins, TFs, miRNAs and CpGs, in order to fit the analysis to your experiment or to only consider a set of genes of interest in your system under study.

6º Email

Introduce your email so that we can send you the link with results. If you do not want to submit it please be sure to copy the report url to recover your job anytime.

7º Report Name

Personalise your analysis with a name.

3- Results interpretation

Quality Control

The first step in the GeneCodis analysis is to check the validity of the input. We differenciate between a general control and that specific to the annotation whose results are under exploration. There are six possible conditions, one regarding the annotations and the other to the input list. We can explore the elements that did not pass the quality control by clicking the number if there are cases. The six conditions are entitled as follows:

Input not in our database

Those elements in the input that are not in our database. Be sure that these elements are invalid, any kind of strange characters could cause not to identify them in our database. If applicable fix this and repeat the analysis.

Universe not in our database

Those elements in the custom universe that are not in our database.

Annotations not mapped to input

The annotations that have none of the elements introduced in the input.

Annotated input

Those elements in the input that have annotations of the selected collection.

No annotated input

Those elements in the input that do not have annotations of the selected collection.

Annotation universe

Those elements in the universe that have annotations of the selected collection.

Table

GeneCodis report all the terms without applying a 0.05 p-value cut-off. To keep the navigation light, in the web it is limited to the top 100 terms. Download the complete table to obtain the full results and analysis information.

There is one for every selected annotation database, pluse one for the CoAnnotation analysis. The table columns are:

Description

The human readable name of the annotation id.

Annotation Id (in download)

The standarized code that identifies each term, specific to each database.

Genes Found (in download)

Number of genes in the input list that part of the annotation.

Pval (in download) and Pval adj. (FDR)

The hypergeometric or Wallenius test raw p-values and corrected via Benjamini/Hochberg FDR. In the website only the adjusted are shown, download the table to see the raw.

Relative Enrichment

It measures the proportion between the number of genes found of an annotation in the input divided by the size of the input and how many genes are under that annotation divided by all the genes in the database:

^{(Genes Found / Input Size)} ⁄ _{(Term Genes / Genes Universe)}

High numbers imply a better relative enrichment.

Input Size (in download)

Number of genes introduced in GeneCodis. Only in downloaded table.

Term Genes (in download)

Number of genes that are annotated to each annotation. Only in downloaded table.

Genes Count

(Genes Found / Term Genes). Only in online table.

Genes Universe (in download)

The number of genes to use as the background set in the Fisher's test.Only in downloaded table. By default, GeneCodis uses all the measurable elements depending on the annotations and organism selected. Since, TFs and CpGs are transformed to genes the universe is the same used when the input consist in genes/proteins. The same applies to miRNAs, excepting when miRNAs-based annotations are selected the universe covers all the miRNAs known of the organism selected.

• For gene-based annotations, all the genes available in NCBI gene_info.gz
• For miRNAs-based annotations, all the miRNAs available in current miRBase

The universe should be customized to the set of elements that can be measured in your upstream experiment. Go to "Advanced Options > Universe > Custom".

Annotation bias (in download)

Only appears if Wallenius is the stat selected in advanced options. The bias score or annotation weight is calculated as the odds ratio of a power weighting function. See 7- Stats for more info.

Genes

The input genes associated to each annotation separated by comma.

Visualisations

Next we find two interactive and downloadble plots that can be customized by moving the slider in the left menu or via the text box next to it set the number of annotations displayed. Leave the mouse on a node or bar to get more information. Plots can be downloaded as .png or .svg.

Gene-Annotation Clusters Network

The selected top annotations in the left menu, and their genes found are drawn in a network with a profuse force layout, in this way annotations nodes that share genes are close together. The size of the annotations node is proportional to -log10(Pval Adj) and the blue intensity goes in concordance with the number of genes found. You can hide the gene nodes and by clicking on nodes you show or hide their labels.

Bars Chart

The bars size represent the -log10(Pval Adj) and the red intensity the number of genes found.

4- Input allowed

GeneCodis supports the gene and protein nomenclatures that are common of NCBI and Ensembl, also accepts regulatory elements: TFs, miRNAs and CpGs. These will be converted to its associated genes. Additionally, we have incorporated direct annotations for miRNAs, see Garcia-Moreno, Adrian and Carmona-Saez, Pedro for more details.

Genes, for all organisms, users can input entrez, symbol, ensembl and uniprot ids, as well as synonyms symbols collected in the NCBI FTP ( gene_info.gz file) which are inlcuded in Ensembl databases.

CpG sites allowed derive from Infinium MethylationEPIC i.e.: cg00050873, extracted from ( the latest manifest file).

The miRNAs allowed, i.e: hsa-miR-122-5p, come from miRTarBase 8 derived from strong evidence techniques such as: reporter assay, western blot and qRT-PCR.

The TFs allowed derives from CollecTRI

5- Organisms supported

Currently GeneCodis provides annotations for 14 different organisms. Here we specify which different annotations are available for each one.

Homo sapiens

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways, Mouse Genomic Information, Reactome, WikiPathways, TFs (CollecTRI), miRNAs (miRTarBase), RNADisease, TAM2, HMDD3, PharmGKB, LINCS, HPO, OMIM

Caenorhabditis elegans

Gene Ontology: Biological Process, Cellular Component, Molecular Function KEGG Pathways, Panther Pathways, Reactome, WikiPathways

Canis familiaris

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways, Reactome, WikiPathways

Danio rerio

Gene Ontology: Biological Process, Cellular Component, Molecular Function KEGG Pathways, Panther Pathways, Mouse Genomic Information, Reactome, WikiPathways, miRNAs (miRTarBase)

Drosophila melanogaster

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways, Reactome, WikiPathways, miRNAs (miRTarBase)

Gallus gallus

Gene Ontology: Biological Process, Cellular Component, Molecular Function KEGG Pathways, Panther Pathways, Reactome, WikiPathways

Bos taurus

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways, Mouse Genomic Information, Reactome, WikiPathways

Mus musculus

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways, Mouse Genomic Information, Reactome, WikiPathways, TFs (CollecTRI), miRNAs (miRTarBase), RNADisease

Rattus norvegicus

Gene Ontology: Biological Process, Cellular Component, Molecular Function KEGG Pathways, Panther Pathways, Mouse Genomic Information, Reactome, WikiPathways, miRNAs (miRTarBase), RNADisease

Sus scrofa

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Reactome, RNADisease

Arabidopsis thaliana

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways, WikiPathways

Oryza sativa

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways

Saccharomyces cerevisiae

Gene Ontology: Biological Process, Cellular Component, Molecular Function KEGG Pathways, Panther Pathways, Reactome, WikiPathways

Escherichia coli

Gene Ontology: Biological Process, Cellular Component, Molecular Function, KEGG Pathways, Panther Pathways

6- Annotations included

GeneCodis4 gather 21 annotation sources. These are updated every three months. You can download, for each one, a two columns table with the pairs gene-annotation directly from our database. Genes are in by default in Symbol nomenclature, but you can choose the one that suits you: Symbol Ensembl Entrez Uniprot By default, you will download the information from all the organisms in the database, however you can choose a concrete one: All Arabidopsis thaliana Caenorhabditis elegans Drosophila melanogaster Danio rerio Gallus gallus Homo sapiens Canis familiaris Sus scrofa Bos taurus Mus musculus Rattus norvegicus Oryza sativa Escherichia coli Saccharomyces cerevisiae

7- Stats

Tests

Hypergeometric distribution

Considering the following:

• An input of N elements. N = input size
• From which, k elements are associated to an annotation. k = genes found
• A total of n genes are under that annotation. n = term genes
• And the total number of genes annotated is M. M = genes universe

Source from Scipy

Wallenius noncentral hypergeometric distribution

Adding to the hypergeometric parameters w:

• The weight of a annotation being enriched is w. w = annotation odds
• w is obtained via the Power Weighting Function

Note here x is k from the normal hypergeometric distribution

Source from Scipy

Power Weighting Function

Based on GOseq, this is a function used to calculate the differential weights of genes used later for the odds of the tested annotations by Wallenius. This function derives from a logistic GAM model that fits a cubic spline with a monotonicity constraint to a binary data series where a value of 1 refers to an input gene or TF-, miRNA-, CpG-target gene and 0 if none. The fitting is against different variables depending on the type of input. If genes are introduced, GeneCodis uses their length, in the case of CpGs the bias is given by the number of CpGs associated to each gene, likewise, for TFs and miRNAs is the number of TFs or miRNAs per target gene. Finally, the Wallenius test uses the odds ratio for each term calculated as the average gene bias score of the annotated elements in the category divided by the average of those that are not.

p-value Correction (FDR)

Via the False Discovery Rate (FDR) method of Benjamini and Hochberg (J R Stat Soc 1995, 57:289-300).

Source from statsmodels

Relative Enrichment

^{(Genes Found / Input Size)}⁄_{(Term Genes / Genes Universe)}

8- Cite

Please cite our latest article:

1. A. García-Moreno, R. López-Domínguez, A. Ramirez-Mena, A. Pascual-Montano, E. Aparicio-Puerta, M. Hackenberg, P. Carmona-Saez. GeneCodis 4: Expanding the modular enrichment analysis to regulatory elements bioRxiv 2021.04.15.439962

Previous articles:

2. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A: GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Research 2012
3. Nogales-Cadenas R, Carmona-Saez P, Vazquez M, Vicente C, Yang X, Tirado F, Carazo JM, Pascual-Montano A: GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Research 2009
4. Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A: GENECODIS: A web-based tool for finding significant concurrent annotations in gene lists. Genome Biology 2007 8(1):R3

9- Browser compatibility

Browser	Version
Mozilla Firefox	82
Google Chrome	86
Safari	14
Microsoft Edge	87
Opera	72

10- API documentation

The GeneCodis API is a set of endpoints focused for developers who want to programmatically use the GeneCodis features. The documentation of the GeneCodis API is published by Postman, an API platform that helps users and simplifies processes like design or testing. This documentation is available in C#, cURL, Dart, GO, Java, Javascript, C, NodeJS, Objective-C, Ocaml, PHP, Powershell, Python, R, Ruby, Shell and Swift. Through the API we can launch an analysis, obtain the results from an existent job, transform microRNAs between precursors and mature nomenclatures, download the annotation databases, query genes and terms in the databases, among other characteristics. See the details in the GeneCodis API documentation.

11- Database sources

Bioplanet

https://tripod.nih.gov/bioplanet/download/pathway.csv

DoRothEA

https://github.com/saezlab/dorothea/raw/master/data/dorothea_hs.rda

https://github.com/saezlab/dorothea/raw/master/data/dorothea_mm.rda

https://github.com/saezlab/dorothea/tree/master/data (mus_musculus, homo_sapiens)

EBI

ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz

EPIC

https://webdata.illumina.com/downloads/productfiles/methylationEPIC/infinium-methylationepic-v-1-0-b5-manifest-file-csv.zip

Ensembl

ftp://ftp.ensembl.org/pub/current_gtf (canis_lupus_familiaris, bos_taurus, caenorhabditis_elegans, danio_rerio, drosophila_melanogaster, gallus_gallus, homo_sapiens, mus_musculus, rattus_norvegicus, sus_scrofa)

EnsemblGenomes

ftp://ftp.ensemblgenomes.org/pub/bacteria/current/species_EnsemblBacteria.txt

ftp://ftp.ensemblgenomes.org/pub/bacteria/current/gtf (escherichia_coli_str_k_12_substr_mg1655_gca_000005845)

ftp://ftp.ensemblgenomes.org/pub/plants/current/gtf (arabidopsis_thaliana, oryza_sativa)

ftp://ftp.ensemblgenomes.org/pub/fungi/current/gtf (aspergillus_nidulans, saccharomyces_cerevisiae, candida_albicans_sc5314_gca_000784635)

Gene Ontology

http://purl.obolibrary.org/obo/go.obo

http://geneontology.org/data/sars-cov-2_targets.gaf

http://current.geneontology.org/annotations/ (all organisms)

Human Phenotype Ontology

https://ci.monarchinitiative.org/view/hpo/job/hpo.annotations/lastSuccessfulBuild/artifact/rare-diseases/util/annotation/genes_to_phenotype.txt

Human microRNA Disease Database

http://www.cuilab.cn/static/hmdd3/data/alldata.xlsx

Infinium MethylationEPIC

http://webdata.illumina.com.s3-website-us-east-1.amazonaws.com/downloads/productfiles/methylationEPIC/infinium-methylationepic-v-1-0-b5-manifest-file-csv.zip

KEGG

https://www.kegg.jp/kegg/rest/keggapi.html

LINCS

https://clue.io/touchstone

Mammalian ncRNA-Disease Repository

https://www.rna-society.org/mndr/download/miRNA-disease%20information.zip

Mouse Genome Informatics

http://www.informatics.jax.org/downloads/reports/HOM_AllOrganism.rpt

http://www.informatics.jax.org/downloads/reports/VOC_MammalianPhenotype.rpt

http://www.informatics.jax.org/downloads/reports/MRK_ENSEMBL.rpt

http://www.informatics.jax.org/downloads/reports/MGI_PhenoGenoMP.rpt

NCBI

https://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz

ftp://ftp.ncbi.nih.gov/gene/DATA/gene_info.gz

ftp://ftp.ncbi.nih.gov/gene/DATA/gene2refseq.gz

ftp://ftp.ncbi.nih.gov/gene/DATA/mim2gene_medgen

ftp://ftp.ncbi.nlm.nih.gov/pub/medgen/NAMES.RRF.gz

ftp://ftp.ncbi.nih.gov/gene/DATA/gene_refseq_uniprotkb_collab.gz

ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2ensembl.gz

https://ftp.ncbi.nih.gov/pub/taxonomy/taxdmp.zip

Panther

http://data.pantherdb.org/ftp/pathway/current_release/

PharmGKB

https://s3.pgkb.org/data/drugs.zip

https://api.pharmgkb.org/v1/download/file/data/genes.zip

https://api.pharmgkb.org/v1/download/file/data/relationships.zip

Reactome

https://reactome.org/download/current/NCBI2Reactome_All_Levels.txt

TAM2

http://www.lirmed.com/tam2/Public/static/data/mirset_v9.txt

WikiPathways

http://data.wikipathways.org/current/gmt/ (Arabidopsis_thaliana, Bos_taurus, Caenorhabditis_elegans, Canis_familiaris, Danio_rerio, Drosophila_melanogaster, Gallus_gallus, Homo_sapiens, Mus_musculus, Oryza_sativa, Rattus_norvegicus, Saccharomyces_cerevisiae, Sus_scrofa)

miRBase

https://www.mirbase.org/download/CURRENT/miRNA.dat

miRTarBase

https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2019/cache/download/8.0/miRTarBase_SE_WR.xls