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Recent technological advances, combined with the development of bioinformatic tools, allow us to better address biological questions combining -omic approaches (i.e., genomics, metabolomics and proteomics). This novel comprehensive perspective addresses the identification, characterisation and quantitation of the whole repertoire of genes, proteins and metabolites occurring in living organisms. Here we provide an overview of recent significant advances and technologies used in genomics, metabolomics and proteomics. We also underline the importance and limits of mass accuracy in mass spectrometry-based -omics and briefly describe emerging types of fragmentation used in mass spectrometry. The range of instruments and techniques used to address the study of each -omic approach, which provide vast amounts of information (usually termed "high-throughput" technologies in the literature) is briefly discussed, including names, links and descriptions of the main databases, data repositories and resources used. Integration of multiple -omic results and procedures seems necessary. Therefore, an emerging challenge is the integration of the huge amount of data generated and the standardisation of the procedures and methods used. Functional data integration will lead to answers to unsolved questions, hopefully, applicable to clinical practice and management of patients.
Proteomics, Biomedical Research, Genomics, Medical Oncology, Models, Biological, Systems Integration, Neoplasms, Animals, Humans, Metabolomics, Algorithms
Proteomics, Biomedical Research, Genomics, Medical Oncology, Models, Biological, Systems Integration, Neoplasms, Animals, Humans, Metabolomics, Algorithms
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 35 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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