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Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0)
Open accessAbstract
The PANTHER classification system (http://www.pantherdb.org) is a comprehensive system that combines genomes, gene function classifications, pathways and statistical analysis tools to enable biologists to analyze large-scale genome-wide experimental data. The current system (PANTHER v.14.0) covers 131 complete genomes organized into gene families and subfamilies; evolutionary relationships between genes are represented in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models (HMMs)). The families and subfamilies are annotated with Gene Ontology (GO) terms, and sequences are assigned to PANTHER pathways. A suite of tools has been built to allow users to browse and query gene functions and analyze large-scale experimental data with a number of statistical tests. PANTHER is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. Since the protocol for using this tool (v.8.0) was originally published in 2013, there have been substantial improvements and updates in the areas of data quality, data coverage, statistical algorithms and user experience. This Protocol Update provides detailed instructions on how to analyze genome-wide experimental data in the PANTHER classification system. Here the authors provide an update to their 2013 protocol for using the PANTHER classification system, detailing how to analyze genome-wide experimental data with the newest version of PANTHER (v.14.0), with improvements in the areas of data quality, data coverage, statistical algorithms and user experience.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
The sequence of the human genome
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science · doi:10.1126/science.1058040 · Cited by 13,909
Self-citationCaspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395
Expansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850
Research works printed but not linked: 16
- Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim, and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019). · DOI
- Thomas, P. D. et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141 (2003). · DOI
- Thomas, P. D. et al. PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31, 334–341 (2003). · DOI · Paper page
- Thomas, P. D. et al. Applications for protein sequence-function evolution data: MRNA/protein expression analysis and coding SNP scoring tools. Nucleic Acids Res. 34, W645–W650 (2006). · DOI
- Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013). · DOI
- Gaudet, P., Livstone, M. S., Lewis, S. E. & Thomas, P. D. Phylogenetic-based propagation of functional annotations within the gene ontology consortium. Brief. Bioinform. 12, 449–462 (2011). · DOI
- UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699 (2018).. · DOI
- Fabregat, A. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018). · DOI
- Kanehisa, M., Sato, Y., Furumichi, M., Morishima, K. & Tanabe, M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 45, D353–D361 (2018). · DOI · Paper page
- Slenter, D. N. et al. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. 46, D661–D667 (2018). · DOI · Paper page
- Kerrien, S. et al. The intact molecular interaction database in 2012. Nucleic Acids Res. 40, D841–D846 (2012). · DOI · Paper page
- Chatr-Aryamontri, A. et al. The biogrid interaction database: 2017 update. Nucleic Acids Res. 45, D369–D379 (2017). · DOI · Paper page
- Finn, R. D. et al. InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Res. 45, D190–D199 (2017). · DOI
- Mi, H. & Thomas, P. PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol. Biol. 563, 123–140 (2009). · DOI · Paper page
- Clark, A. G. et al. Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science 302, 1960–1963 (2003). · DOI · Paper page
- Eddy, S. R. A new generation of homology search tools based on probabilistic inference. Genome Inform. 23, 205–211 (2009).
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Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321
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Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L., & Mi, H. (2022) · Protein Science · doi:10.1002/pro.4218 · Cited by 2,010
Self-citationMi, H., Ebert, D., Muruganujan, A., Mills, C., Albou, L.-P., Mushayamaha, T., & Thomas, P. D. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1106 · Cited by 1,332
Self-citationChen, C., Liao, J., Xia, Y., Liu, X., Jones, R., Haran, J., McCormick, B., Sampson, T. R., Alam, A., & Ye, K. (2022) · Gut · doi:10.1136/gutjnl-2021-326269 · Cited by 491
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Panni, S., Lovering, R. C., Porras, P., & Orchard, S. (2020) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194417 · Cited by 479
Novais, E. J., Tran, V. A., Johnston, S. N., Darris, K. R., Roupas, A. J., Sessions, G. A., Shapiro, I. M., Diekman, B. O., & Risbud, M. V. (2021) · Nature Communications · doi:10.1038/s41467-021-25453-2 · Cited by 440
APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease
Nguyen, A. T., Wang, K., Hu, G., Wang, X., Miao, Z., Azevedo, J. A., Suh, E., Van Deerlin, V. M., Choi, D., Roeder, K., et al. (2020) · Acta Neuropathologica · doi:10.1007/s00401-020-02200-3 · Cited by 241
Genetic and epigenetic characteristics of inflammatory bowel disease-associated colorectal cancer
Rajamäki, K., Taira, A., Katainen, R., Välimäki, N., Kuosmanen, A., Plaketti, R.-M., Seppälä, T. T., Ahtiainen, M., Wirta, E.-V., Vartiainen, E., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.04.042 · Cited by 230
The rapidly evolving view of lysosomal storage diseases
Parenti, G., Medina, D. L., & Ballabio, A. (2021) · EMBO Molecular Medicine · doi:10.15252/emmm.202012836 · Cited by 230
Loomis, K. H., Wu, S. K., Ernlund, A., Zudock, K., Reno, A., Blount, K., & Karig, D. K. (2021) · Microbiome · doi:10.1186/s40168-020-00963-1 · Cited by 74
Stallmeyer, B., Bühlmann, C., Stakaitis, R., Dicke, A.-K., Ghieh, F., Meier, L., Zoch, A., MacKenzie MacLeod, D., Steingröver, J., Okutman, Ö., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50930-9 · Cited by 71
Kelley, C. P., Haerle, M. C., & Wang, E. T. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111226 · Cited by 61
Kim, J. W., Yin, X., Jhaldiyal, A., Khan, M. R., Martin, I., Xie, Z., Perez-Rosello, T., Kumar, M., Abalde-Atristain, L., Xu, J., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.08.002 · Cited by 56
Kim, J. Y., Rhim, W.-K., Cha, S.-G., Woo, J., Lee, J. Y., Park, C. G., & Han, D. K. (2022) · Nano Convergence · doi:10.1186/s40580-022-00349-z · Cited by 54
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Linard, B., Ebersberger, I., McGlynn, S. E., Glover, N., Mochizuki, T., Patricio, M., Lecompte, O., Nevers, Y., Thomas, P. D., Gabaldón, T., et al. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab098 · Cited by 45
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Smith, G. A., Padmanabhan, A., Lau, B. H., Pampana, A., Li, L., Lee, C. Y., Pelonero, A., Nishino, T., Sadagopan, N., Xia, V. Q., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abj8670 · Cited by 23
Zhang, Z., Hebert, A. S., Westphall, M. S., Coon, J. J., & Dovichi, N. J. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00244 · Cited by 22
Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases
Badonyi, M., & Marsh, J. A. (2023) · doi:10.1101/2023.09.08.556798 · Cited by 6
A Pilot Detection and Associate Study of Gene Presence-Absence Variation in Holstein Cattle
Boschiero, C., Neupane, M., Yang, L., Schroeder, S. G., Tuo, W., Ma, L., Baldwin, R. L., Van Tassell, C. P., & Liu, G. E. (2024) · Animals · doi:10.3390/ani14131921 · Cited by 5
Allgoewer, K., Wu, S., Choi, H., & Vogel, C. (2023) · Molecular Omics · doi:10.1039/d2mo00258b · Cited by 5
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Canner, S. W., Schnaar, R. L., & Gray, J. J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2523342123 · Cited by 4
Li, Q., Nichols, C., Welner, R. S., Chen, J. Y., Ku, W.-S., & Yue, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag244
“These include Gorilla [ 31], DAVID [ 32], WebGestalt [ 33], EnrichR [ 34], PANTHER Gene List Analysis [ 35], TermMapper [ 36], and g:profiler [ 37], which are frequently used to provide valuable interpretations into high-throughput omics.”
Metadata
- Authors
- Mi, H., Muruganujan, A., Huang, X., Ebert, D., Mills, C., Guo, X., & Thomas, P. D.
- Venue
- Nature Protocols
- Published
- 2019-02-25
- Volume
- 14
- Issue
- 3
- Pages
- 703-721
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2917101543
- Cited by
- 1,493
- References
- 5
- FWCI
- 52.06
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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