The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Majorbio Cloud 2024: Update single‐cell and multiomics workflows
Open accessAbstract
Majorbio Cloud (https://cloud.majorbio.com/) is a one-stop online analytic platform aiming at promoting the development of bioinformatics services, narrowing the gap between wet and dry experiments, and accelerating the discoveries for the life sciences community. In 2024, three single-omics workflows, two multiomics workflows, and extensions were newly released to facilitate omics data mining and interpretation.
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Cites
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis
Tang, Z., Kang, B., Li, C., Chen, T., & Zhang, Z. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz430 · Cited by 5,083
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984
Self-citationMetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights
Pang, Z., Chong, J., Zhou, G., de Lima Morais, D. A., Chang, L., Barrette, M., Gauthier, C., Jacques, P.-É., Li, S., & Xia, J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab382 · Cited by 3,958
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Majorbio Cloud: A one‐stop, comprehensive bioinformatic platform for multiomics analyses
Ren, Y., Yu, G., Shi, C., Liu, L., Guo, Q., Han, C., Zhang, D., Zhang, L., Liu, B., Gao, H., et al. (2022) · iMeta · doi:10.1002/imt2.12 · Cited by 906
Self-citationQuantitative metabolomics using NMR
Wishart, D. S. (2008) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2007.12.001 · Cited by 582
Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell
Ayyaz, A., Kumar, S., Sangiorgi, B., Ghoshal, B., Gosio, J., Ouladan, S., Fink, M., Barutcu, S., Trcka, D., Shen, J., et al. (2019) · Nature · doi:10.1038/s41586-019-1154-y · Cited by 573
iNAP: An integrated network analysis pipeline for microbiome studies
Feng, K., Peng, X., Zhang, Z., Gu, S., He, Q., Shen, W., Wang, Z., Wang, D., Hu, Q., Li, Y., et al. (2022) · iMeta · doi:10.1002/imt2.13 · Cited by 428
Learning representations of microbe–metabolite interactions
Morton, J. T., Aksenov, A. A., Nothias, L. F., Foulds, J. R., Quinn, R. A., Badri, M. H., Swenson, T. L., Van Goethem, M. W., Northen, T. R., Vazquez-Baeza, Y., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0616-3 · Cited by 299
Noecker, C. A., Eng, A., Muller, E., & Borenstein, E. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac003 · Cited by 86
Sun, Y., Lü, L., Zhou, Y., Ge, W., Wang, H., Wu, R., Liu, W., Chen, H., Xiao, Q., Cai, X., et al. (2022) · Molecular Oncology · doi:10.1002/1878-0261.13198 · Cited by 34
Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
Yi, X., Zhu, J., Liu, W., Peng, L., Lu, C., Sun, P., Huang, L., Nie, X., Huang, S., Guo, T., et al. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100604 · Cited by 25
Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases
Lei, Y., Guo, J., He, S., & Yan, H. (2022) · Cells · doi:10.3390/cells12010103 · Cited by 15
Metadata
- Authors
- Han, C., Shi, C., Liu, L., Han, J., Yang, Q., Wang, Y., Li, X., Fu, W., Gao, H., Huang, H., Zhang, X., & Yu, K.
- Venue
- iMeta
- Publisher
- Wiley
- Published
- 2024-06-25
- Volume
- 3
- Issue
- 4
- Pages
- e217-e217
- ISSN
- 2770-596X (ISSN-L)
- 2770-5986
- OpenAlex
- W4400001588
- Cited by
- 331
- References
- 14
- FWCI
- 38.46
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.