A brief survey of tools for genomic regions enrichment analysis

Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.

1 Introduction

Pathway enrichment analysis (PEA) methods are a set of bioinformatics techniques and tools which associate biological pathways with gene lists by ranking these pathways based on their over-representation in the list of genes analyzed (Reimand et al., 2019; Mubeen et al., 2022; Chicco and Agapito, 2022). “Gene set enrichment analysis” is another name for PEA, while over-representation analysis (ORA) is an alternative to it (Mubeen et al., 2022; Wijesooriya et al., 2022) which emphasizes the importance of the biological functions which are overrepresented in a gene list with respect to their role in the whole human genome (Chicco and Agapito, 2022). There are many PEA tools (for example, DAVID (Sherman et al., 2022)); they all take genes as input, indicated as symbols or Ensembl ID’s (Spudich et al., 2007). Some other tools, however, read genomic regions and associate genes with these regions before performing PEA (Figure 1). These methods perform genomic regions enrichment analysis, also called chromosome regions enrichment analysis—instead of reading genes as do traditional PEA tools, they read chromosome regions represented as genomic coordinates that are triplets made of a chromosome number, a genomic starting position, and a genomic ending position. The string chr10:134221970-134222202, for example, indicates a genomic region starting at the 134,221,970 position and ending at the 134,222,202 position of chromosome 10 of the genome. Files containing chromosome regions are usually in browser extensible data (BED) format (Quinlan and Hall, 2010; University of California Santa Cruz Genome Browser, 2022). To work properly, genomic region enrichment analysis tools need to specify the genomic coordinates they use as references for genomic regions and the species of interest. GREAT (McLean et al., 2010), for example, accepts data in both the current GRCh38/hg38 genome assembly (National Library of Medicine, 2017b) and in the previous GRCh37/hg19 coordinate reference (National Library of Medicine, 2009) for Homo sapiens genomic regions.

FIGURE 1

FIGURE 1. Flowchart of a genomic regions enrichment analysis. The association of the genes corresponding to the query chromosome regions, here indicated with (x), is the phase where BEHST, GREAT, LOLA, and Poly-Enrich involve cis-regulatory elements’ data. As is evident, genomic regions enrichment analyses incorporate pathway enrichment analyses.

Most existing tools for genomic regions enrichment analysis not only associate genes with their corresponding chromosome regions but also consider cis-regulatory elements to select more precise and accurate genes, which in turn can lead to more precise and accurate biological pathways as output of functional enrichment analysis.

This short survey lists these tools, describes their main features, and explains how they work and what distinctive traits they have.

We organize the rest of the article as follows. After this Introduction, we list and describe the tools in Section 2 and discuss and compare their features in Section 3.

2 Tools

We have found six tools for genomic regions enrichment analysis that currently work: GREAT (McLean et al., 2010), LOLA (Sheffield and Bock, 2016), ReactomePA (Yu and He, 2016), Poly-Enrich (Lee et al., 2020), BEHST (Chicco et al., 2019), and g:Profiler g:GOSt (Raudvere et al., 2019). We found other tools that handle chromosome regions for PEA (Lee et al., 2012; Pageaud et al., 2018) but they seem to no longer work. We list these six tools in Table 1.

TABLE 1

TABLE 1. List of the genomic regions enrichment analysis tools mentioned in this study. We report each tool’s name, its statistical methods for the p-value calculation, its distinctive traits, its web URL, its year, its reference, a field stating whether it is available as web tool, and a field indicating the name of its standalone software package. FDR: false discovery rate. α: The LOLA web server is called LOLAweb (Nagraj et al., 2018). g:SCS is the statistical method for adjusted p-value calculation employed by g:Profiler g:GOSt by default (g:Profiler g:GOSt, 2022). The article by Raudvere et al. (2019) refers to the latest update of g:Profiler; its original release and previous updates were described in past studies (Reimand et al., 2007, 2011, 2016; Kolberg et al., 2020). The bottom table contains the lists of the supported organisms of the data for each tool.

GREAT (Genomic Regions Enrichment of Annotations Tool) (McLean et al., 2010) is a software program available as a web tool that can be used with any internet browser and as an R package within the Bioconductor platform. GREAT defines a regulatory domain for each gene by using its nearest neighbor on either side or a specific genomic distance, annotated to regulatory elements of ChIP-seq data or others. GREAT then overlaps each query chromosome region with these regulatory domains and selects the genes present in them.

LOLA (Locus Overlap Analysis) (Sheffield and Bock, 2016; Nagraj et al., 2018) is a genomic regions enrichment analysis tool available both as a web program (Nagraj et al., 2018) and as an R package within the Bioconductor suite. The most relevant feature of LOLA is its inclusion of regulatory biological elements from the CODEX database (Sánchez-Castillo et al., 2015). These regulatory data include transcription factors that bind sites (as ChIP-seq data), histone modifications (as ChIP-seq data, too), DNase hypersensitivity sites (as DNase-seq data), and transcriptomic information (as RNA-seq data) (Sheffield et al., 2013). LOLA intersects the genomic regions queried by users with the chromosome regions of these regulatory elements, thus facilitating a more precise and accurate selection of genes having a regulatory role.

ReactomePA (Yu and He, 2016) is software package available within the Bioconductor platform which associates the query genomic regions with molecular reactions and pathways from the Reactome database (Gillespie et al., 2022). ReactomePA was built on DOSE (Yu et al., 2015), a disease ontology enrichment analysis package developed and released by the same team.

Poly-Enrich (Lee et al., 2020) is a tool available both online through a browser interface and as a set of software functions within the chipenrich R software package on Bioconductor. The authors built Poly-Enrich on its two predecessors, ChIP-Enrich (Welch et al., 2014) and Broad-Enrich (Cavalcante et al., 2014), which were previously released by the same team. The distinctive trait of Poly-Enrich is that it associates the genes with the query genomic regions by considering the strength of the peaks of the protein binding sites involved as ChIP-seq data. In a nutshell, Poly-Enrich takes into account the association between chromosome regions and genes by observing the strengths of the ChIP-seq peaks—the stronger the peak, the more important are the genes involved around it.

BEHST (Biological Enrichment of Hidden Sequence Targets) (Chicco et al., 2019) is available both as a web tool and as a standalone R package within the Bioconda suite (Grüning et al., 2018). It performs an enhanced association between the query chromosome regions and their corresponding genes by considering long-range interactions in chromatin, included as Hi-C data (Rao et al., 2014). Once BEHST filters in these genes, it sends them as input to g:Profiler g:GOSt, which performs a traditional pathway enrichment analysis through the g:SCS method.

g:Profiler g:GOSt is a popular tool for pathway enrichment analysis which has been mainly used for the association of query gene lists with biomolecular pathways since its first release (Reimand et al., 2007). In the 2019 update of the tool (Raudvere et al., 2019), its developers introduced the possibility of querying not only gene lists but also other data formats, such as chromosome regions, single nucleotide polymorphism (SNP) IDs, and annotation term IDs. g:Profiler g:GOSt therefore now accepts genomic regions, called chromosomal intervals, arranged in the BED format according to the GRCh38. p12/hg38 genome reference (National Library of Medicine, 2017a). When reading genomic regions, g:Profiler first retrieves their genes and then performs its traditional functional enrichment analysis. g:Profiler g:GOSt is available both as a web tool and as an R package within CRAN.

3 Discussion

The six tools listed and described in this brief survey share some common aspects and have several differences.

ReactomePA and g:Profiler g:GOSt are the only tools among the six in this study which do not make use of data of regulatory elements—BEHST, GREAT, LOLA, and Poly-Enrich all perform a precise selection of the genes involved in the query chromosome regions by using cis-regulatory data. ReactomePA and g:Profiler g:GOSt do not apply any filtering step to the input data for the association with the genes—all the genes associated with the input chromosome regions are considered for the functional enrichment analysis. BEHST, LOLA, GREAT, and Poly-Enrich instead use regulatory element data to filter the genes to be employed in the functional enrichment analysis (Table 1).

A user may select which tool to utilize by considering the data involved. GREAT, for example, has a precise definition of a cis-regulatory domain that can lead to a more precise selection of the genes that have a regulatory role among the query chromosome regions. Alternatively, LOLA can be selected by a user who would like to have a filtering phase based on multiple regulatory data (ChIP-seq, DNAse-seq, and RNA-seq). Poly-Enrich can be employed by a researcher who wants the filtering to be done not only through regulatory elements’ ChIP-seq data but also considering the strength of their peaks. Users can select BEHST if they are investigating the role of chromatin interactions in a particular study. ReactomePA and g:Profiler g:GOSt, as mentioned above, can be employed if a computational biologist would like to select all the genes related to the query chromosome regions, without any filtering.

Packages’ spread and usage: To better understand the impact of these six tools, we checked their citations in articles, the ratio of citations per month since their publication, and their position in the Bioconductor software package downloads ranking.

These quantitative characteristics show that the articles of g:Profiler g:GOSt, ReactomePA, and LOLA collected the highest amount of citations. In particular, g:Profiler g:GOSt obtained the highest ratio of citations per month since its publication (2,145 citations in total, equal to 53.62 citations per month since the publication of 2019 update study (Raudvere et al., 2019)). However, since g:Profiler g:GOSt can not only be used for genomic regions enrichment analysis but also for traditional pathway enrichment analysis on gene lists, we cannot consider its article the most cited on a genomic regions enrichment analysis tool solely. Regarding the other five tools listed in this survey, which all perform only genomic regions enrichment analysis, it is clear that GREAT has been the most used software program of this category, since it collected 23.65 citations per month and 3,477 citations in total since the publication of its article. ReactomePA was also highly cited (887 to date), its results being the most downloaded software package in Bioconductor among the four available on that platform (128th/2,140).

LOLA’s two articles together count 315 citations on Google Scholar to date (around four citations per month since the publication of the first article of the two), showing that this tool is quite utilized in bioinformatics analyses worldwide. LOLA’s Bioconductor library is 432nd position out of 2,140 in the ranking of the packages’ downloads, while GREAT’s package is ranked 300th in the same standing.

BEHST is the only package among the six listed here that is available on Bioconda and not on Bioconductor or on the Comprehensive R Archive Network (CRAN). Bioconda is a set of bioinformatics software packages available on the Anaconda, Inc. (2020) distributed software platform.

g:Profiler g:GOSt is the only tool among the six available as a software package on CRAN—gprofiler2 (Kolberg et al., 2020). These five Biocondocutor and CRAN software packages were developed in the R programming language (Tippmann, 2015) while BEHST was developed mainly in Python and bash, with a few scripts and a wrapper in R. All six packages were released under free and open-source licenses.

Regarding the enrichment analysis output generated by the six tools under consideration, we noticed that five of them (BEHST, g:Profiler g:GOSt, GREAT, Poly-Enrich, and ReactomePA) produce outcome annotations from similar or the same databases of biomolecular annotations while LOLA produces output terms related to regulatory data only.

g:Profiler g:GOSt generates output annotations from The Gene Ontology, KEGG, Reactome, WikiPathways, Human Protein Atlas, CORUM, TRANSFAC, and miRTarBase. BEHST does too, using g:Profiler g:GOSt as a last step (Table 1). Among the six tools presented in this survey, Poly-Enrich is the tool that finds annotations from the highest number of databases: Biocarta Pathway, KEGG Pathway, Panther Pathway, pFAM, Reactome, Literature Derived, MeSH, MSigDB Derived, Hallmark, Immunologic, Oncogenic, Targets, Comparative Toxicogenomics Database (CTD), Drug Bank, MicroRNA, Transcription Factors, Interaction, Protein Interaction BioGRID, Metabolite, and Cytoband. GREAT in its standard version associates genomic regions with annotations of The Gene Ontology, Ensembl genes, and annotations of Human Phenotype, while ReactomePA—as mentioned above—associates genomic regions only with Reactome pathways. All these five tools (g:Profiler g:GOSt, BEHST, GREAT, Poly-Enrich, and ReactomePA) can be grouped in the same category since they produce output annotations related to The Gene Ontology and other biomolecular databases.

LOLA, on the other hand, is the only tool listed here which produces results related to cis-regulatory data, such as from ENCODE transcription factor binding data, DNase hypersensitivity data, Cistrome epigenome data, and CODEX regulatory data, related to the query genomic regions.

Each tool of the six described in this survey produces its output results in its specific way, making it somewhat difficult to compare them computationally. Users could, however, insert the same query genomic region set in all the tools and then study the significance of the shared overrepresented biomolecular annotations produced in the output, such as The Gene Ontology terms.

Applications: Several databases of biologically-relevant chromosome regions now exist for users interested in employing some genomic regions as queries for these tools. One worth mentioning is SlideBase, which provides genomic regions of enhancers and promoters related to specific human tissues and organs (Ienasescu et al., 2016) as GRCh37/hg19 coordinates. For example, chromosome regions of heart enhancers could be downloaded from SlideBase and input into GREAT: the first step of GREAT, as shown in Figure 1, would be associating these input genomic regions with genes near cis-regulatory regions. The genes found would then be used in traditional functional enrichment analysis.

We represented a schematic example of an application of GREAT to a set of chromosome regions of heart enhancers available on FANTOM5 (Andersson et al., 2014) downloaded as pre-defined tracks from SlideBase (SlideBase, 2014; Ienasescu et al., 2016) in GRCh37/hg19 coordinates in Figure 2. In the image, we reported some query sets of genomic regions of heart enhancers as input and a few outputs over-expressed as The Gene Ontology (GO) annotations (Ashburner et al., 2000) retrieved by the GREAT web tool. In this example, GREAT found “muscle structure development,” “cardiocyte differentiation”, and “striated muscle cell development” as the most overly expressed GO terms (Figure 2) with their corresponding p-values. We reaffirm the importance of using adjusted p-values (Chicco and Agapito, 2022) and of setting an adjusted p-value threshold to at least 0.005, as suggested by Benjamin et al. (2018). These GO annotations can then be employed to better understand heart biology or for additional computational analyses (Pinoli et al., 2013; Chicco and Masseroli, 2014).

FIGURE 2

FIGURE 2. Schematic example of application of GREAT to SlideBase enhancer genomic regions of the heart. Representation of the application of GREAT (McLean et al., 2010) to query genomic regions of the heart downloaded from SlideBase (SlideBase, 2014; Ienasescu et al., 2016) in GRCh37/hg19 coordinates. We obtained these results with GREAT default parameters (basal plus extension, proximal: 5 kilo base pairs upstream, 1 kilo base pairs downstream, plus distal: up to 1000 kilo base pairs). PEA: pathway enrichment analysis, functional enrichment analysis.

3.1 Recap

Below are some key findings arising from the analysis of the six tools listed in this survey.

Cis-regulatory elements provide relevant information for genomic regions enrichment analysis. As we already mentioned, four out of the six tools presented in this brief survey involve data of regulatory elements: the authors of these four thus believe that regulatory elements can improve the results of genomic regions enrichment analyses. We will probably see even further involvement of this kind of data in the future.

ChIP-seq data provide relevant information for genomic regions enrichment analysis. Three tools out of the six described in this survey (GREAT, LOLA, and Poly-Enrich) employ chromatin immunoprecipitation DNA sequencing data (ChIP-seq) to include data of the regulatory activities of the genes (Mardis, 2007). This result confirms the importance of ChIP-seq data in computational genomics.

R and Bioconductor are the key programming language and software suite for genomic regions enrichment analysis. As mentioned above, the four tools presented here are available as R software packages on the Bioconductor platform. This aspect proves that R and Bioconductor are key resources for this kind of bioinformatics analysis. Only one of the six software packages mentioned in this study was released on the CRAN official catalogue of R packages (The R Foundation, 2022).

In the future, we envision the development of new tools for genomic regions enrichment analysis, which will likely involve data of regulatory elements such as ChIP-seq, developed both as web tools via an internet browser and as R standalone packages within Bioconductor.

Author contributions

DC conceived the study, performed the literature search, and wrote most of the article. GJ contributed to the writing of the manuscript and reviewed it.

Acknowledgments

The authors thank Mehran Karimzadeh (Vector Institute) for his helpful feedback.

Conflict of interest

The reviewer GJF declared a shared affiliation with the author DC to the handling editor at the time of review. DC was a developer of BEHST and an author of its preprint (Chicco et al., 2019).

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

BED, browser extensible data; BEHST, biological enrichment of hidden sequence targets; ChIP, chromatin immunoprecipitation; CRAN, Comprehensive R Archive Network; FDR, false discovery rate; GREAT, Genomic Regions Enrichment of Annotations Tool; GSEA, gene set enrichment analysis; LOLA, locus overlap analysis; PEA, pathway enrichment analysis; Seq, DNA sequencing; URL, Uniform Resource Locator.

References

Anaconda, Inc. (2020). Anaconda software distribution. URL: https://docs.anaconda.com/(Accessed May 10, 2022).

Google Scholar

Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., et al. (2014). An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461. doi:10.1038/nature12787

PubMed Abstract | CrossRef Full Text | Google Scholar

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene ontology: Tool for the unification of biology. Nat. Genet. 25, 25–29. doi:10.1038/75556

PubMed Abstract | CrossRef Full Text | Google Scholar

Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., et al. (2018). Redefine statistical significance. Nat. Hum. Behav. 2, 6–10. doi:10.1038/s41562-017-0189-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavalcante, R. G., Lee, C., Welch, R. P., Patil, S., Weymouth, T., Scott, L. J., et al. (2014). Broad-enrich: Functional interpretation of large sets of broad genomic regions. Bioinformatics 30, i393–i400. doi:10.1093/bioinformatics/btu444

PubMed Abstract | CrossRef Full Text | Google Scholar

Chicco, D., and Agapito, G. (2022). Nine quick tips for pathway enrichment analysis. PLoS Comput. Biol. 18, 1010348. doi:10.1371/journal.pcbi.1010348

CrossRef Full Text | Google Scholar

Chicco, D., Bi, H. S., Reimand, J., and Hoffman, M. M. (2019). Behst: Genomic set enrichment analysis enhanced through integration of chromatin long-range interactions. bioRxiv 168427, 1–29. doi:10.1101/168427

CrossRef Full Text | Google Scholar

Chicco, D., and Masseroli, M. (2014). Software suite for gene and protein annotation prediction and similarity search. IEEE/ACM Trans. Comput. Biol. Bioinform. 12, 837–843. doi:10.1109/tcbb.2014.2382127

PubMed Abstract | CrossRef Full Text | Google Scholar

g:Profiler g:GOSt (2022). Profiler g:GOSt (2022). g:SCS algorithm. URL: https://biit.cs.ut.ee/gprofiler/page/docs URL visited on 13th (Accessed May, 2022).

Google Scholar

Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., Senff-Ribeiro, A., et al. (2022). The Reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692. doi:10.1093/nar/gkab1028

CrossRef Full Text | Google Scholar

Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., et al. (2018). Bioconda: Sustainable and comprehensive software distribution for the life sciences. Nat. Methods 15, 475–476. doi:10.1038/s41592-018-0046-7

CrossRef Full Text | Google Scholar

Ienasescu, H., Li, K., Andersson, R., Vitezic, M., Rennie, S., Chen, Y., et al. (2016). On-the-fly selection of cell-specific enhancers, genes, miRNAs and proteins across the human body using SlideBase. Database. baw144, baw144–10. doi:10.1093/database/baw144

PubMed Abstract | CrossRef Full Text | Google Scholar

Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J., and Peterson, H. (2020). gprofiler2–an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Research 9, ELIXIR-709. doi:10.12688/f1000research.24956.2

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, C. T., Cavalcante, R. G., Lee, C., Qin, T., Patil, S., Wang, S., et al. (2020). Poly-enrich: Count-based methods for gene set enrichment testing with genomic regions. Nar. Genom. Bioinform. 2, lqaa006. doi:10.1093/nargab/lqaa006

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, P. H., O’Dushlaine, C., Thomas, B., and Purcell, S. M. (2012). Inrich: Interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28, 1797–1799. doi:10.1093/bioinformatics/bts191

PubMed Abstract | CrossRef Full Text | Google Scholar

Mardis, E. R. (2007). ChIP-seq: Welcome to the new frontier. Nat. Methods 4, 613–614. doi:10.1038/nmeth0807-613

PubMed Abstract | CrossRef Full Text | Google Scholar

McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., et al. (2010). GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501. doi:10.1038/nbt.1630

PubMed Abstract | CrossRef Full Text | Google Scholar

Mubeen, S., Tom Kodamullil, A., Hofmann-Apitius, M., and Domingo-Fernández, D. (2022). On the influence of several factors on pathway enrichment analysis. Brief. Bioinform. 23, bbac143. doi:10.1093/bib/bbac143

PubMed Abstract | CrossRef Full Text | Google Scholar

Nagraj, V., Magee, N. E., and Sheffield, N. C. (2018). LOLAweb: A containerized web server for interactive genomic locus overlap enrichment analysis. Nucleic Acids Res. 46, W194–W199. doi:10.1093/nar/gky464

PubMed Abstract | CrossRef Full Text | Google Scholar

National Library of Medicine (2017a). Genome assembly GRCh38.p12/hg38. URL: https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_000001405.38/URL visited on 16th (Accessed September, 2022).

Google Scholar

National Library of Medicine (2017b). Genome assembly GRCh38.p14/hg38. URL: https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_000001405.40/URL visited on 16th (Accessed September, 2022).

Google Scholar

National Library of Medicine (2009). GRCh37/hg19. URL: https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_000001405.13/URL visited on 16th (Accessed September, 2022).

Google Scholar

Pageaud, Y., Plass, C., and Assenov, Y. (2018). Enrichment analysis with EpiAnnotator. Bioinformatics 34, 1781–1783. doi:10.1093/bioinformatics/bty007

PubMed Abstract | CrossRef Full Text | Google Scholar

Pinoli, P., Chicco, D., and Masseroli, M. (2013). “Enhanced probabilistic latent semantic analysis with weighting schemes to predict genomic annotations,” in Proceedings of the BIBE 2013 – the 13th IEEE International Conference on, Chania, Greece, 10-13 November 2013, 1–4. Bioinformatics and Bioengineering (IEEE).

CrossRef Full Text | Google Scholar

Quinlan, A. R., and Hall, I. M. (2010). BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842. doi:10.1093/bioinformatics/btq033

PubMed Abstract | CrossRef Full Text | Google Scholar

Rao, S. S., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., et al. (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680. doi:10.1016/j.cell.2014.11.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., et al. (2019). g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198. doi:10.1093/nar/gkz369

PubMed Abstract | CrossRef Full Text | Google Scholar

Reimand, J., Arak, T., Adler, P., Kolberg, L., Reisberg, S., Peterson, H., et al. (2016). g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 44, W83–W89. doi:10.1093/nar/gkw199

PubMed Abstract | CrossRef Full Text | Google Scholar

Reimand, J., Arak, T., and Vilo, J. (2011). g:Profiler—a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 39, W307–W315. doi:10.1093/nar/gkr378

PubMed Abstract | CrossRef Full Text | Google Scholar

Reimand, J., Isserlin, R., Voisin, V., Kucera, M., Tannus-Lopes, C., Rostamianfar, A., et al. (2019). Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517. doi:10.1038/s41596-018-0103-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Reimand, J., Kull, M., Peterson, H., Hansen, J., and Vilo, J. (2007). g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193–W200. doi:10.1093/nar/gkm226

PubMed Abstract | CrossRef Full Text | Google Scholar

Sánchez-Castillo, M., Ruau, D., Wilkinson, A. C., Ng, F. S., Hannah, R., Diamanti, E., et al. (2015). Codex: A next-generation sequencing experiment database for the haematopoietic and embryonic stem cell communities. Nucleic Acids Res. 43, D1117–D1123. doi:10.1093/nar/gku895

CrossRef Full Text | Google Scholar

Sheffield, N. C., and Bock, C. (2016). Lola: Enrichment analysis for genomic region sets and regulatory elements in R and bioconductor. Bioinformatics 32, 587–589. doi:10.1093/bioinformatics/btv612

PubMed Abstract | CrossRef Full Text | Google Scholar

Sheffield, N. C., Thurman, R. E., Song, L., Safi, A., Stamatoyannopoulos, J. A., Lenhard, B., et al. (2013). Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res. 23, 777–788. doi:10.1101/gr.152140.112

PubMed Abstract | CrossRef Full Text | Google Scholar

Sherman, B. T., Hao, M., Qiu, J., Jiao, X., Baseler, M. W., Lane, H. C., et al. (2022). David: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221. doi:10.1093/nar/gkac194

CrossRef Full Text | Google Scholar

SlideBase (2014). FANTOM5 human enhancer tracks, enhancers specifically expressed in organs/tissues: Heart. URL: https://slidebase.binf.ku.dk/human_enhancers/presets/serve/heart URL visited on 14th (Accessed August, 2022).

Google Scholar

Spudich, G., Fernández-Suárez, X. M., and Birney, E. (2007). Genome browsing with Ensembl: A practical overview. Briefings Funct. Genomics Proteomics 6, 202–219. doi:10.1093/bfgp/elm025

PubMed Abstract | CrossRef Full Text | Google Scholar

The R Foundation (2022). The comprehensive R archive Network. URL: https://cran.r-project.org/URL visited on 17th (Accessed May, 2022).

Google Scholar

Tippmann, S. (2015). Programming tools: Adventures with R. Nature 517, 109–110. doi:10.1038/517109a

PubMed Abstract | CrossRef Full Text | Google Scholar

University of California Santa Cruz Genome Browser (2022). BED file format. URL: http://genome.cse.ucsc.edu/FAQ/FAQformat.html#format1 URL visited (Accessed May 13th, 2022).

Google Scholar

Welch, R. P., Lee, C., Imbriano, P. M., Patil, S., Weymouth, T. E., Smith, R. A., et al. (2014). ChIP-enrich: Gene set enrichment testing for ChIP-seq data. Nucleic Acids Res. 42, e105. doi:10.1093/nar/gku463

PubMed Abstract | CrossRef Full Text | Google Scholar

Wijesooriya, K., Jadaan, S. A., Perera, K. L., Kaur, T., and Ziemann, M. (2022). Urgent need for consistent standards in functional enrichment analysis. PLoS Comput. Biol. 18, e1009935. doi:10.1371/journal.pcbi.1009935

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, G., and He, Q.-Y. (2016). ReactomePA: An R/bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479. doi:10.1039/C5MB00663E

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, G., Wang, L.-G., Yan, G.-R., and He, Q.-Y. (2015). Dose: An R/bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609. doi:10.1093/bioinformatics/btu684

PubMed Abstract | CrossRef Full Text | Google Scholar