Gene set knowledge discovery with enrichr
Open accessAbstract
Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high‐profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1 : Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2 : Searching Enrichr by a single gene or key search term Basic Protocol 3 : Preparing raw or processed RNA‐seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4 : Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5 : Using Enrichr in Geneshot Basic Protocol 6 : Using Enrichr in ARCHS4 Basic Protocol 7 : Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8 : Using the Enrichr API Basic Protocol 9 : Adding an Enrichr button to a website
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The genes most co-expressed with IGKV4-1 are associated with the microglia pathogen phagocytosis pathway (WP3937), and tend to be microglia enriched.
“The genes that are most co‐expressed with IGKV4‐1 are associated with the microglia pathogen phagocytosis pathway (WP3937), and tend to be microglia enriched, 34”
From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [34]
Basic Protocol 6 describes how to find co-expressed genes and upload them to Enrichr, but does not report results for IGKV4-1. Searches for IGKV4-1, WP3937, and microglia enrichment found no passage in our copy supporting the claimed pathway association or cell-type enrichment. (LLM, conf=0.73)
Experimental — accuracy not yet measuredThe IGKV4-1 gene on chromosome 2, proposed as the functional gene for the GFAP pQTL, is implicated in the immune system and is associated with the complement cascade and B-cell receptor signaling.
“With respect to the novel plasma pQTL, the IGKV4‐1 gene on chromosome 2, proposed as the functional gene for the GFAP pQTL, is implicated in the immune system and is associated with the complement cascade and B‐cell receptor signaling. 34”
From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as [34]
The Basic Protocol 2 and Basic Protocol 5 passages describe how Enrichr finds gene associations, but do not report findings for IGKV4-1. Searches for IGKV4-1, GFAP pQTL, the complement cascade, and B-cell receptor signaling found no passage in our copy establishing the claimed associations. Page numbers were not available for these passages. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288 · see citing works
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814 · see citing works
Self-citationExpansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J.-P., Gargano, M., Harris, N. L., Matentzoglu, N., McMurry, J. A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1105 · Cited by 677 · see citing works
Flagged first, then by citations
Research works printed but not linked: 31
- Al-Shahrour F, Minguez P, Tárraga J, Montaner D, Alloza E, Vaquerizas JM, Conde L, Blaschke C, Vera J, & Dopazo J (2006). BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments. Nucleic acids research, 34(suppl_2), W472– W476. 16845052 PMC1538844
- Bahcall OG (2015). GTEx pilot quantifies eQTL variation across tissues and individuals. Nature Reviews Genetics, 16(7), 375– 375. 26077371
- Bostock M, Ogievetsky V, & Heer J (2011). D3 data-driven documents. IEEE transactions on visualization and computer graphics, 17(12), 2301– 2309. 22034350
- Chen J, Bardes EE, Aronow BJ, & Jegga AG (2009). ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic acids research, 37(suppl_2), W305– W311. 19465376 PMC2703978
- Consortium G (2019). The gene ontology resource: 20 years and still GOing strong. Nucleic acids research, 47(D1), 39. PMC6323945 30395331
- Eden E, Navon R, Steinfeld I, Lipson D, & Yakhini Z (2009). GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC bioinformatics, 10(1), 1– 7. 19192299 PMC2644678
- Fernandez NF, Gundersen GW, Rahman A, Grimes ML, Rikova K, Hornbeck P, & Ma’ayan A (2017). Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Scientific data, 4, 170151. 28994825 PMC5634325
- Gundersen GW, Jones MR, Rouillard AD, Kou Y, Monteiro CD, Feldmann AS, Hu KS, & Ma’ayan A (2015). GEO2Enrichr: browser extension and server app to extract gene sets from GEO and analyze them for biological functions. Bioinformatics, 31(18), 3060– 3062. 25971742 PMC4607730
- Kamburov A, Stelzl U, Lehrach H, & Herwig R (2013). The ConsensusPathDB interaction database: 2013 update. Nucleic acids research, 41(D1), D793– D800. 23143270 PMC3531102
- Kuleshov MV, Diaz JE, Flamholz ZN, Keenan AB, Lachmann A, Wojciechowicz ML, Cagan RL, & Ma’ayan A (2019). modEnrichr: a suite of gene set enrichment analysis tools for model organisms. Nucleic acids research, 47(W1), W183– W190. 31069376 PMC6602483
- Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, & Ma’ayan A (2016, 7 8). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44(W1), W90– 97. 10.1093/nar/gkw377 27141961 PMC4987924 · DOI · Paper page
- Lachmann A, Schilder BM, Wojciechowicz ML, Torre D, Kuleshov MV, Keenan AB, & Ma’ayan A (2019). Geneshot: search engine for ranking genes from arbitrary text queries. Nucleic acids research, 47(W1), W571– W577. 31114885 PMC6602493
- Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, & Ma’ayan A (2018). Massive mining of publicly available RNA-seq data from human and mouse. Nature communications, 9(1), 1366. PMC5893633 29636450
- Mi H, Muruganujan A, Casagrande JT, & Thomas PD (2013). Large-scale gene function analysis with the PANTHER classification system. Nature protocols, 8(8), 1551– 1566. 23868073 PMC6519453
- Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, & Vilo J (2016). g: Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic acids research, 44(W1), W83– W89. 27098042 PMC4987867
- Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, & Ma’ayan A (2016). The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database, 2016. PMC4930834 27374120
- Sherman BT, & Lempicki RA (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols, 4(1), 44. 19131956
- Subramanian A, Kuehn H, Gould J, Tamayo P, & Mesirov JP (2007). GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics, 23(23), 3251– 3253. 17644558
- Tan CM, Chen EY, Dannenfelser R, Clark NR, & Ma’ayan A (2013). Network2Canvas: network visualization on a canvas with enrichment analysis. Bioinformatics, 29(15), 1872– 1878. 23749960 PMC3712222
- Torre D, Lachmann A, & Ma’ayan A (2018). BioJupies: Automated Generation of Interactive Notebooks for RNA-seq Data Analysis in the Cloud. Cell Systems, 7(5), 556– 561. 30447998 PMC6265050
- Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW, Rouillard AD, Jenkins SL, Feldmann AS, Hu KS, McDermott MG, Duan Q, Clark NR, Jones MR, Kou Y, Goff T, Woodland H, Amaral FMR, Szeto GL, Fuchs O, Schüssler-Fiorenza Rose SM, Sharma S, Schwartz U, Bausela XB, Szymkiewicz M, Maroulis V, Salykin A, Barra CM, Kruth CD, Bongio NJ, Mathur V, Todoric RD, Rubin UE, Malatras A, Fulp CT, Galindo JA, Motiejunaite R, Jüschke C, Dishuck PC, Lahl K, Jafari M, Aibar S, Zaravinos A, Steenhuizen LH, Allison LR, Gamallo P, de Andres Segura F, Dae Devlin T, Pérez-García V, & Ma’ayan A (2016, 9 26). Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat Commun, 7, 12846. 10.1038/ncomms12846 PMC5052684 27667448 · DOI
- Wiedemann A, Foucat E, Hocini H, Lefebvre C, Hejblum BP, Durand M, Krüger M, Keita AK, Ayouba A, Mély S, Fernandez JC, Touré A, Fourati S, Lévy-Marchal C, Raoul H, Delaporte E, Koivogui L, Thiébaut R, Lacabaratz C, & Lévy Y (2020, 7 24). Long-lasting severe immune dysfunction in Ebola virus disease survivors. Nat Commun, 11(1), 3730. 10.1038/s41467-020-17489-7 32709840 PMC7381622 · DOI
- Zhang B, Kirov S, & Snoddy J (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic acids research, 33(suppl_2), W741– W748. 15980575 PMC1160236
- Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, & Chanda SK (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature communications, 10(1), 1– 10. PMC6447622 30944313
- Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, & Ma’ayan A (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC bioinformatics, 14, 128. 10.1186/1471-2105-14-128. 23586463 PMC3637064 · DOI · Paper page
- Clarke DJB, Jeon M, Stein DJ, Moiseyev N, Kropiwnicki E, Dai C, Xie Z, Wojciechowicz ML, Litz S, Hom J, Evangelista JE, Goldman L, Zhang S, Yoon C, Ahamed T, Bhuiyan S, Cheng M, Karam J, Jagodnik JM, Shu I, Lachmann A, Ayling S, Jenkins SL, Ma’ayan A (2021). Appyters: Turning Jupyter Notebooks into Data Driven Web Apps. Patterns (Accepted). PMC7961182 33748796
- Kuleshov MV, Diaz JE, Flamholz ZN, Keenan AB, Lachmann A, Wojciechowicz ML, Cagan RL, & Ma’ayan A (2019). modEnrichr: a suite of gene set enrichment analysis tools for model organisms. Nucleic acids research, 47(W1), W183– W190. 31069376 PMC6602483
- Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, & Ma’ayan A (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44(W1), W90– 97. 10.1093/nar/gkw377 27141961 PMC4987924 · DOI · Paper page
- Lachmann A, Schilder BM, Wojciechowicz ML, Torre D, Kuleshov MV, Keenan AB, & Ma’ayan A (2019). Geneshot: search engine for ranking genes from arbitrary text queries. Nucleic acids research, 47(W1), W571– W577. 31114885 PMC6602493
- Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, & Ma’ayan A (2018). Massive mining of publicly available RNA-seq data from human and mouse. Nature communications, 9(1), 1366. PMC5893633 29636450
- Torre D, Lachmann A, & Ma’ayan A (2018). BioJupies: Automated Generation of Interactive Notebooks for RNA-seq Data Analysis in the Cloud. Cell Systems, 7(5), 556– 561. 30447998 PMC6265050
Cited by
Sorted by citations
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 751 · see citing works
“Cluster identities were thenmanually annotated using a combination of these automated annotations, manual gene searches, and gene set enrichment analyses conducted using Enrichr (Xie et al., 2021).”
Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2024) · Nature · doi:10.1038/s41586-023-06837-4 · Cited by 225 · see citing works
A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma
Pozniak, J., Pedri, D., Landeloos, E., Van Herck, Y., Antoranz, A., Vanwynsberghe, L., Nowosad, A., Roda, N., Makhzami, S., Bervoets, G., et al. (2024) · Cell · doi:10.1016/j.cell.2023.11.037 · Cited by 199 · see citing works
Chromatin remodelling drives immune cell–fibroblast communication in heart failure
Alexanian, M., Padmanabhan, A., Nishino, T., Travers, J. G., Ye, L., Pelonero, A., Lee, C. Y., Sadagopan, N., Huang, Y., Auclair, K., et al. (2024) · Nature · doi:10.1038/s41586-024-08085-6 · Cited by 140 · see citing works
McMillan, M., Gomez, N., Hsieh, C., Bekier, M., Li, X., Miguez, R., Tank, E. M. H., & Barmada, S. J. (2023) · Molecular Cell · doi:10.1016/j.molcel.2022.12.019 · Cited by 112 · see citing works
Hepatic stellate cells control liver zonation, size and functions via R-spondin 3
Sugimoto, A., Saito, Y., Wang, G., Sun, Q., Yin, C., Lee, K. H., Geng, Y., Rajbhandari, P., Hernandez, C., Steffani, M., et al. (2025) · Nature · doi:10.1038/s41586-025-08677-w · Cited by 110 · see citing works
Ergir, E., Oliver-De La Cruz, J., Fernandes, S., Cassani, M., Niro, F., Pereira-Sousa, D., Vrbský, J., Vinarský, V., Perestrelo, A. R., Debellis, D., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-22225-w · Cited by 99 · see citing works
B cell depletion therapy does not resolve chronic active multiple sclerosis lesions
Maggi, P., Bulcke, C. V., Pedrini, E., Bugli, C., Sellimi, A., Wynen, M., Stölting, A., Mullins, W. A., Kalaitzidis, G., Lolli, V., et al. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104701 · Cited by 92 · see citing works
Anderson, A. G., Rogers, B. B., Loupe, J. M., Rodriguez-Nunez, I., Roberts, S. C., White, L. M., Brazell, J. N., Bunney, W. E., Bunney, B. G., Watson, S. J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100263 · Cited by 82 · see citing works
Endothelial sensing of AHR ligands regulates intestinal homeostasis
Wiggins, B. G., Wang, Y.-F., Burke, A., Grunberg, N., Vlachaki Walker, J. M., Dore, M., Chahrour, C., Pennycook, B. R., Sanchez-Garrido, J., Vernia, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06508-4 · Cited by 89 · see citing works
Welch, G. M., Boix, C. A., Schmauch, E., Davila-Velderrain, J., Victor, M. B., Dileep, V., Bozzelli, P. L., Su, Q., Cheng, J. D., Lee, A., et al. (2022) · Science Advances · doi:10.1126/sciadv.abo4662 · Cited by 84 · see citing works
DNA damage contributes to neurotoxic inflammation in Aicardi-Goutières syndrome astrocytes
Giordano, A. M. S., Luciani, M., Gatto, F., Abou Alezz, M., Beghè, C., Della Volpe, L., Migliara, A., Valsoni, S., Genua, M., Dzieciatkowska, M., et al. (2022) · Journal of Experimental Medicine · doi:10.1084/jem.20211121 · Cited by 76 · see citing works
Early SRC activation skews cell fate from apoptosis to senescence
Anerillas, C., Herman, A. B., Rossi, M., Munk, R., Lehrmann, E., Martindale, J. L., Cui, C.-Y., Abdelmohsen, K., De, S., & Gorospe, M. (2022) · Science Advances · doi:10.1126/sciadv.abm0756 · Cited by 71 · see citing works
Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis
Mantri, M., Hinchman, M. M., McKellar, D. W., Wang, M. F. Z., Cross, S. T., Parker, J. S. L., & De Vlaminck, I. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00138-1 · Cited by 74 · see citing works
Proteotoxic stress response drives T cell exhaustion and immune evasion
Wang, Y., Ma, A., Song, N.-J., Shannon, A. E., Amankwah, Y. S., Chen, X., Wu, W., Wang, Z., Saadey, A. A., Yousif, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09539-1 · Cited by 68 · see citing works
Stachulski, A. V., Knausenberger, T. B.-A., Shah, S. N., Hoyles, L., & McArthur, S. (2023) · Tissue Barriers · doi:10.1080/21688370.2022.2073175 · Cited by 61 · see citing works
Boyer, S., Lee, H.-J., Steele, N., Zhang, L., Sajjakulnukit, P., Andren, A., Ward, M. H., Singh, R., Basrur, V., Zhang, Y., et al. (2022) · eLife · doi:10.7554/elife.73796 · Cited by 56 · see citing works
Moszyńska, A., Jaśkiewicz, M., Serocki, M., Cabaj, A., Crossman, D. K., Bartoszewska, S., Gebert, M., Dąbrowski, M., Collawn, J. F., & Bartoszewski, R. (2022) · The FASEB Journal · doi:10.1096/fj.202101987r · Cited by 53 · see citing works
Huang, Y., Wang, M., Ni, H., Zhang, J., Li, A., Hu, B., Junqueira Alves, C., Wahane, S., Rios de Anda, M., Ho, L., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01664-w · Cited by 42 · see citing works
Zhang, X., Kim, B., Singh, A., Sankararaman, S., Durvasula, A., & Lohmueller, K. E. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad001 · Cited by 29 · see citing works
Mazan-Mamczarz, K., Tsitsipatis, D., Childs, B. G., Carr, A. E., Dos Santos, C. R., Anerillas, C., Romero, B., Gregg, J. M., Henry-Smith, C., Okereke, A. N., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00889-z · Cited by 37 · see citing works
Buttarelli, M., Ciucci, A., Palluzzi, F., Raspaglio, G., Marchetti, C., Perrone, E., Minucci, A., Giacò, L., Fagotti, A., Scambia, G., et al. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02265-w · Cited by 32 · see citing works
“Over Representation Analysis (ORA) To functionally annotate the 42 DEGs, we performed an ORA over the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, using the Enrichr web-based tool [30].”
MethodLymphoid gene expression supports neuroprotective microglia function
Ayata, P., Crowley, J. M., Challman, M. F., Sahasrabuddhe, V., Gratuze, M., Werneburg, S., Ribeiro, D., Hays, E. C., Durán-Laforet, V., Faust, T. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09662-z · Cited by 32 · see citing works
Jain, I., Brougham-Cook, A., & Underhill, G. H. (2023) · Acta Biomaterialia · doi:10.1016/j.actbio.2023.06.018 · Cited by 26 · see citing works
Liu, H., Yin, G., Franco Leonardi, B., Lan, T., Ait Ahmed, Y., Berger, H., Kohlhepp, M. S., Amiridze, N., Martagón Calderón, N., Frau, C., et al. (2025) · Gut · doi:10.1136/gutjnl-2024-334425 · Cited by 23 · see citing works
Taylor, S. J., Stauber, J., Bohorquez, O., Tatsumi, G., Kumari, R., Chakraborty, J., Bartholdy, B. A., Schwenger, E., Sundaravel, S., Farahat, A. A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01911-7 · Cited by 22 · see citing works
Jang, J., Yeo, S., Baek, S., Jung, H. J., Lee, M. S., Choi, S. H., & Choe, Y. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01637-3 · Cited by 20 · see citing works
“And they were delivered to the R package Enrichr [40, 79] and processed by enrichment analysis using the biological process of GO.”
MethodSantos, M., Lima, L., Carvalho, S., Mota-Pereira, J., Pimentel, P., Maia, D., Correia, D., Barroso, M. F., Gomes, S., Cruz, A., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076758 · Cited by 20 · see citing works
“…enrichment analysis together with the genes identified as associated with TRD in the present study (BDNF, SYN1, and PTEN) to perform GO and KEGG Pathway analyses with Enrichr (https://maayanlab.cloud/Enrichr/ accessed on 26 march 2023), according to the protocol described by Xie, 2021 et al. [68].”
MethodDeep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting
Wei, J., Lotfy, P., Faizi, K., Baungaard, S., Gibson, E., Wang, E., Slabodkin, H., Kinnaman, E., Chandrasekaran, S., Kitano, H., et al. (2021) · doi:10.1101/2021.09.14.460134 · Cited by 67 · see citing works
“32 ● Long short-term memory recurrent neural network (LSTM): LSTM units – [16, 32,64,128], dense layer units – [8, 16, 32], number of recurrent dense layes – [0,1,2,3], dropout rate – [0.”
BackgroundStaley, H. A., Jernigan, J. E., Bolen, M. L., Titus, A. M., Neighbarger, N., Cole, C., Menees, K. B., Wallings, R. L., & Tansey, M. G. (2024) · Journal of Neuroinflammation · doi:10.1186/s12974-024-03271-9 · Cited by 9 · see citing works
“Pathway analysis was performed using Enrichr for GO biological process analysis [36–38].”
MethodSevere traumatic injury is associated with profound changes in DNA methylation
Eskesen, T. O., Almstrup, K., Elgaard, L., Arleth, T., Lassen, M. L., Creutzburg, A., Jensen, A. H., Breindahl, N., Dinesen, F., Vang, M., et al. (2024) · npj Genomic Medicine · doi:10.1038/s41525-024-00438-4 · Cited by 7 · see citing works
A genetic and proteomic comparison of key AD biomarkers across tissues
Marsh, T. W., Western, D., Timsina, J., Gorijala, P., Yang, C., Pastor, P., Liu, M., Morris, J. C., Bateman, R. J., Schindler, S. E., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14139 · Cited by 6 · see citing works
O-GlcNAcylation of FOXK1 orchestrates the E2F pathway and promotes oncogenesis
Masclef, L., Ahmed, O., Iannantuono, N., Gagnon, J., Gushul-Leclaire, M., Boulay, K., Estavoyer, B., Echbicheb, M., Poy, M., Ali Boubacar, K., et al. (2024) · doi:10.1101/2024.03.01.582838 · Cited by 6 · see citing works
Balestra, C., Maj, C., Müller, E., & Mayr, A. (2023) · PLOS ONE · doi:10.1371/journal.pone.0282699 · Cited by 4 · see citing works
Kadyrov, F. F., Koenig, A. L., Amrute, J. M., Dun, H., Li, W., Weinheimer, C. J., Nigro, J. M., Kovacs, A., Bredemeyer, A. L., Lai, L., et al. (2022) · doi:10.1101/2022.08.04.502542 · Cited by 3 · see citing works
Transcriptomic Signatures of Single-Suture Craniosynostosis Phenotypes
Lapehn, S., Gustafson, J. A., Timms, A. E., Cunningham, M. L., & Paquette, A. G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065353 · Cited by 3 · see citing works
Zhang, Y., Cheng, Z., Huang, S., Sharma, T., Barbosa, D., Yu, G., & Sun, L. O. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116252 · Cited by 3 · see citing works
dos Santos, E. C., Rohan, P., Binato, R., & Abdelhay, E. (2023) · Cancers · doi:10.3390/cancers15225374 · Cited by 3 · see citing works
Linden, A. K., Affaneh, A., Aylward, A., Wong, Y., Kessler, J. A., & Peng, C.-Y. (2025) · doi:10.64898/2025.12.14.694212 · Cited by 1 · see citing works
Tiwari, K., Mahajan, G., Bellad, R., Dhruw, N. K., & Jha, A. (2026) · doi:10.64898/2026.10.16.759604
- Authors
- Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma'ayan, A.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Department of Pharmacological Sciences Mount Sinai Center for Bioinformatics Icahn School of Medicine at Mount Sinai New York New York
- ORCID
- Venue
- Current Protocols
- Publisher
- Wiley
- Published
- 2021-03
- Volume
- 1
- Issue
- 3
- Pages
- e90-e90
- ISSN
- 2691-1299 (ISSN-L)
- PMC
- PMC8152575
- OpenAlex
- W3145969289
- Cited by
- 3,451
- References
- 4
- Citations by year
- 2026: 718 · 2025: 871 · 2024: 726 · 2023: 601 · 2022: 433 · 2021: 100
- FWCI
- 152.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Protocols (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (U24CA224260, U54HL127624)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene set enrichment analysis, differentially expressed genes, transcriptomics, proteomics, phosphoproteomics, genome-wide association study, model organisms, interactive visualization
- MeSH terms
- RNA-Seq, Animals, Humans, Software, Computational Biology, Genomics, Knowledge Discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
