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Application of ’omics technologies to biomarker discovery in inflammatory lung diseases
Open accessAbstract
Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis,i.e.define pathological phenotypes, and facilitate the monitoring of disease and therapy, and also, unravel underlying molecular pathways. Biomarker studies can either select predefined biomarker(s) measured by specific methods or apply an “unbiased” approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the ’omics field. The contributions of the individual ’omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease.
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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Electrospray Ionization for Mass Spectrometry of Large Biomolecules
Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., & Whitehouse, C. M. (1989) · Science · doi:10.1126/science.2675315 · Cited by 7,634
Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
KEGG for representation and analysis of molecular networks involving diseases and drugs
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322
Absolute Quantification of Proteins by LCMSE
Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C., & Geromanos, S. J. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500230-mcp200 · Cited by 1,529
Network‐based classification of breast cancer metastasis
Chuang, H., Lee, E., Liu, Y., Lee, D., & Ideker, T. (2007) · Molecular Systems Biology · doi:10.1038/msb4100180 · Cited by 1,431
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Exhaled breath condensate: methodological recommendations and unresolved questions
Horváth, I., Hunt, J., & Barnes, P. J. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00029705 · Cited by 1,284
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Metadata
- Authors
- Wheelock, C. E., Goss, V. M., Balgoma, D., Nicholas, B., Brandsma, J., Skipp, P. J., Snowden, S., Burg, D., D'Amico, A., Horvath, I., Chaiboonchoe, A., Ahmed, H., Ballereau, S., Rossios, C., Chung, K. F., Montuschi, P., Fowler, S. J., Adcock, I. M., Postle, A. D., Dahlén, S.-E., Rowe, A., Sterk, P. J., Auffray, C., Djukanović, R., & the U-BIOPRED Study Group
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2013-02-08
- Volume
- 42
- Issue
- 3
- Pages
- 802-825
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2111793469
- Cited by
- 272
- References
- 20
- FWCI
- 19.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wheelock, C. E., Goss, V. M., Balgoma, D., Nicholas, B., Brandsma, J., Skipp, P. J., Snowden, S., Burg, D., D'Amico, A., Horvath, I., Chaiboonchoe, A., Ahmed, H., Ballereau, S., Rossios, C., Chung, K. F., Montuschi, P., Fowler, S. J., Adcock, I. M., Postle, A. D., . . . the U-BIOPRED Study Group. (2013). Application of ’omics technologies to biomarker discovery in inflammatory lung diseases. European Respiratory Journal. https://doi.org/10.1183/09031936.00078812
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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.
