The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Molecular signatures database (MSigDB) 3.0
Open accessAbstract
MOTIVATION: Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets. RESULTS: We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site. AVAILABILITY AND IMPLEMENTATION: MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.
Overview
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
Verification
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Transcription factor activities can be estimated using enrichment of their downstream targets26,27.”
From Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease (2023) · cites this paper as [27]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Ground truth is annotated according to Ensembl Release 110 [13], as collected in the MSigDB [29] C1 gene set (c1.all.v2023.2.Hs.symbols.gmt).”
From LAB-Bench: Measuring Capabilities of Language Models for Biology Research (2024) · cites this paper as [29]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Since transcription factor gene expression levels do not always reflect their functional importance, we chose those with the highest or lowest activities based on the induction or suppression of their downstream targets (See methods for details)26,27.”
From Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease (2023) · cites this paper as [27]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“which has a clear relationship to the diet phenotype”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Liberzon et al. 2011
Experimental — accuracy not yet measuredIn the citing paper
“The limma authors maintain mouse and human versions of the Molecular Signatures Database collections (64) in R binary format that can be conveniently used with camera, mroast or romer”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [64]
In the citing paper
“This OXTR/CD38 module was also enriched in genesets reported in the Molecular Signatures Database34 that are associated with several behavioral and cognitive state processes including feeding behavior (p = 5.83 × 10−3), cognition (p = 7.44 × 10−3), and behavioral response to stimuli (p = 1.05 × 10−2).”
From Oxytocin pathway gene networks in the human brain (2019) · cites this paper as [34]
In the citing paper
“Biological process labels are stored in databases such as MSigDB (Liberzon et al, 2011), the Gene Ontology (Ashburner et al, 2000; The Gene Ontology Consortium, 2017), or the pathway databases KEGG (Kanehisa et al, 2017) and Reactome (Fabregat et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Liberzon et al, 2011
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citationGene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Self-citationTRANSFAC(R) and its module TRANSCompel(R): transcriptional gene regulation in eukaryotes
Matys, V. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj143 · Cited by 2,142
NCBI GEO: archive for high-throughput functional genomic data
Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I. F., Soboleva, A., Tomashevsky, M., Marshall, K. A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn764 · Cited by 1,043
Reactome knowledgebase of human biological pathways and processes
Matthews, L., Gopinath, G., Gillespie, M., Caudy, M., Croft, D., de Bono, B., Garapati, P., Hemish, J., Hermjakob, H., Jassal, B., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn863 · Cited by 873
Parkinson, H., Kapushesky, M., Kolesnikov, N. N., Rustici, G., Shojatalab, M., Abeygunawardena, N., Bérubé, H., Dyląg, M., Emam, I., Farne, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn889 · Cited by 418
Flagged first, then by citations
Cited by
Sorted by citations
Items 1,001–1,039 of 1,039
Habib, N., Wapinski, I., Margalit, H., Regev, A., & Friedman, N. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.50 · Cited by 50
A nonredundant role for T cell-derived interleukin 22 in antibacterial defense of colonic crypts
Zindl, C. L., Witte, S. J., Laufer, V. A., Gao, M., Yue, Z., Janowski, K. M., Cai, B., Frey, B. F., Silberger, D. J., Harbour, S. N., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.02.003 · Cited by 49
Wang, Q., & Xu, R. (2019) · BMC Genomics · doi:10.1186/s12864-019-5510-y · Cited by 46
Holzscheck, N., Söhle, J., Kristof, B., Grönniger, E., Gallinat, S., Wenck, H., Winnefeld, M., Falckenhayn, C., & Kaderali, L. (2020) · Aging · doi:10.18632/aging.103499 · Cited by 40
Spatiotemporal transcriptomics elucidates the pathogenesis of fulminant viral myocarditis
Li, H., Chen, X., Wang, J. J., Shen, J., Abuduwufuer, K., Zhang, Z., Dong, Z., Wen, Z., He, J., Chen, S., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02143-9 · Cited by 40
Mercado-Evans, V., Mejia, M. E., Zulk, J. J., Ottinger, S., Hameed, Z. A., Serchejian, C., Marunde, M. G., Robertson, C. M., Ballard, M. B., Ruano, S. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45336-6 · Cited by 40
Lung endothelium exploits susceptible tumor cell states to instruct metastatic latency
Jakab, M., Lee, K. H., Uvarovskii, A., Ovchinnikova, S., Kulkarni, S. R., Jakab, S., Rostalski, T., Spegg, C., Anders, S., & Augustin, H. G. (2024) · Nature Cancer · doi:10.1038/s43018-023-00716-7 · Cited by 39
Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration
Zhang, Z., Chen, L., Sun, B., Ruan, Z., Pan, P., Zhang, W., Jiang, X., Zheng, S., Cheng, S., Xian, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53239-9 · Cited by 37
Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways
Jansen, P. R., Watanabe, K., Stringer, S., Skene, N., Bryois, J., Hammerschlag, A. R., de Leeuw, C. A., Benjamins, J., Muñoz-Manchado, A. B., Nagel, M., et al. (2018) · doi:10.1101/214973 · Cited by 860
Jammihal, T., Saliby, R. M., Labaki, C., Soulati, H., Gallegos, J., Peris, A., McCurry, D., Yu, C., Shah, V., Poduval, D., et al. (2025) · Nature Cancer · doi:10.1038/s43018-024-00896-w · Cited by 38
Yap1 Mediates Trametinib Resistance in Head and Neck Squamous Cell Carcinomas
Mudianto, T., Campbell, K. M., Webb, J., Zolkind, P., Skidmore, Z. L., Riley, R., Barnell, E. K., Ozgenc, I., Giri, T., Dunn, G. P., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-4179 · Cited by 33
PAGER: constructing PAGs and new PAG-PAG relationships for network biology
Yue, Z., Kshirsagar, M. M., Nguyen, T., Suphavilai, C., Neylon, M. T., Zhu, L., Ratliff, T., & Chen, J. Y. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv265 · Cited by 30
Naguib, S., Lopez-Lee, C., Torres, E. R., Lee, S.-I., Zhu, J., Zhu, D., Ye, P., Norman, K., Zhao, M., Wong, M. Y., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.05.023 · Cited by 32
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31
Lin, A., Zhang, H., Meng, H., Deng, Z., Gu, T., Luo, P., & Zhang, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.667875 · Cited by 27
Jain, I., Brougham-Cook, A., & Underhill, G. H. (2023) · Acta Biomaterialia · doi:10.1016/j.actbio.2023.06.018 · Cited by 26
c-Rel gain in B cells drives germinal center reactions and autoantibody production
Kober-Hasslacher, M., Oh-Strauß, H., Kumar, D., Soberon, V., Diehl, C., Lech, M., Engleitner, T., Katab, E., Fernández-Sáiz, V., Piontek, G., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci124382 · Cited by 24
Miller, C. P., Fung, M., Jaeger-Ruckstuhl, C. A., Xu, Y., Warren, E. H., Akilesh, S., & Tykodi, S. S. (2023) · Neoplasia · doi:10.1016/j.neo.2023.100948 · Cited by 19
Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities
Shah, I., Bundy, J., Chambers, B., Everett, L. J., Haggard, D., Harrill, J., Judson, R. S., Nyffeler, J., & Patlewicz, G. (2022) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00245 · Cited by 19
Szadai, L., Velasquez, E., Szeitz, B., Almeida, N. P. D., Domont, G., Betancourt, L. H., Gil, J., Marko-Varga, M., Oskolas, H., Jánosi, Á. J., et al. (2021) · Cancers · doi:10.3390/cancers13236105 · Cited by 18
Sun, L., Liu, Y., Guo, X., Cui, T., Wu, C., Tao, J., Cheng, C., Chu, Q., Ji, C., Li, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49573-7 · Cited by 18
LAB-Bench: Measuring Capabilities of Language Models for Biology Research
Laurent, J. M., Janizek, J. D., Ruzo, M., Hinks, M. M., Hammerling, M. J., Narayanan, S., Ponnapati, M., White, A. D., & Rodriques, S. G. (2024) · arXiv · doi:10.48550/arxiv.2407.10362 · Cited by 21
“Ground truth is annotated according to Ensembl Release 110 [13], as collected in the MSigDB [29] C1 gene set (c1.all.v2023.”
BackgroundMethodArranz, M. J., Gallego-Fabrega, C., Martín-Blanco, A., Soler, J., Elices, M., Dominguez-Clavé, E., Salazar, J., Vega, D., Briones-Buixassa, L., & Pascual, J. C. (2021) · Translational Psychiatry · doi:10.1038/s41398-020-01139-z · Cited by 17
High-throughput identification of calcium-regulated proteins across diverse proteomes
Locke, T. M., Fields, R., Gizinski, H., Otto, G. M., MacEwen, M. J. S., Rusnac, D.-V., He, P., Shechner, D. M., McGann, C. D., Berg, M. D., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114879 · Cited by 17
Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus.
Mathey, C. M., Maj, C., Eriksson, N., Krebs, K., Westmeier, J., David, F. S., Koromina, M., Scheer, A. B., Szabo, N., Wedi, B., et al. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.11.921 · Cited by 15
Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies
Lee, S. C., Quinn, T. P., Lai, J., Kong, S. W., Hertz‐Picciotto, I., Glatt, S. J., Crowley, T. M., Venkatesh, S., & Nguyen, T. (2019) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32701 · Cited by 14
Redlich, R., Kowalczyk, A., Tene, M., Sestili, H. H., Foley, K., Saputra, E., Clark, N., Chikina, M., Meyer, W. K., & Pfenning, A. R. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae210 · Cited by 14
T cell-dependent bispecific therapy enhances innate immune activation and antibody-mediated killing
Besla, R., Penuel, E., Del Rosario, G., Cosino, E., Myrta, S., Dillon, M., Lazar, G. A., Nickles, D., Spiess, C., Yu, S.-F., et al. (2024) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-23-0072 · Cited by 13
Single-cell and spatial detection of senescent cells using DeepScence
Qu, Y., Ji, B., Dong, R., Gu, L., Chan, C., Xie, J., Glass, C., Wang, X.-F., Nixon, A. B., & Ji, Z. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101035 · Cited by 30
Multi-modal quantification of pathway activity with MAYA
Landais, Y., & Vallot, C. (2023) · Nature Communications · doi:10.1038/s41467-023-37410-2 · Cited by 9
“…illustrated the relevance of the biological insight gained by using multimodal pathway analysis for another tissue with a dataset of colon and immune cells from Lee et al. 22 - from which we selected cells from 10 distinct cell types (n=1,415) - and using the MSigDB KEGG and REACTOME pathways 23 .”
MethodCellular senescence in human liver under normal aging and cancer
Karpova, A., Li, X., Peng, C.-W., Gallant, K. L., Rapp, D. R., Alligood, D. M., Houston, A. J., Park, A., Targino da Costa, A. L. N., Chou, W.-H., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101133 · Cited by 13
Interpreting noncoding genetic variation in complex traits and human disease
Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6
Jiang, C., Department of Cardiology, T. F. A. H. O. G. M. U., Jiang, W., & Department of Cardiology, N. T. C. M. H. (2022) · Mathematical Biosciences and Engineering · doi:10.3934/mbe.2022174 · Cited by 5
Lin, S., & Jia, P. (2024) · GigaScience · doi:10.1093/gigascience/giae108 · Cited by 4
He, S., Tan, Y., Ye, Q., M Gubin, M., Dede, M., Rafei, H., Peng, W., Rezvani, K., Mohanty, V., & Chen, K. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00060-4 · Cited by 3
Peterson, J. J., Chandel, S., James, K., Bennett, E. S., Wu, V., White, C. H., Allard, B., Clohosey, M., Whitaker, T., Baker, C., et al. (2026) · Journal of Clinical Investigation · doi:10.1172/jci196536 · Cited by 7
Fard, A. T., Leeson, H., Perez, J. A., Pietrogrande, G., Power, D., Inclan, C. L. G., Zheng, H., Nelson, C., Soheilmoghaddam, F., Glass, N., et al. (2022) · doi:10.1101/2022.01.24.476823 · Cited by 3
Kumar, N., Yang, M.-L., Sun, P., Hunker, K. L., Li, J., Jia, J., Fan, F., Wang, J., Ning, X., Gao, W., et al. (2024) · PLOS Genetics · doi:10.1371/journal.pgen.1011151 · Cited by 1
Integration of polygenic risk with single cell methylation data using met-scDRS
Li, X., Shi, Z., Hou, K., Eyring, K. W., Liu, C., Heffel, M. G., Luo, C., Geschwind, D. H., & Pasaniuc, B. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag751
Metadata
- Authors
- Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P.
- Affiliations
- Broad Institute (US)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-05-05
- Volume
- 27
- Issue
- 12
- Pages
- 1739-1740
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3106198
- OpenAlex
- W2010457001
- Cited by
- 7,111
- References
- 14
- Citations by year
- 2026: 675 · 2025: 1003 · 2024: 1086 · 2023: 942 · 2022: 825 · 2021: 774 · 2020: 602 · 2019: 374 · 2018: 203 · 2017: 151 · 2016: 141 · 2015: 117 · 2014: 102 · 2013: 64 · 2012: 38
- FWCI
- 27.81
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Cancer Institute (R01CA121941)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- MSigDB, gene sets, canonical pathways, experimental signatures, gene set annotation, biological process annotation, genomic data interpretation, molecular signatures
- MeSH terms
- Internet, Genomics, Databases, Genetic, Molecular Sequence Annotation
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
