2016/10/18 by Claudia Manzoni, Demis A. Kia, Jana Vandrovcová +4 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Metabolomics and Mass Spectrometry Studies #Data science #Omics #Pharmacogenomics #Genomics #Data integration #Reductionism #Informatics #Proteomics #Systems biology #Epigenomics #Computer science #Computational biology #Biology #Bioinformatics #Genome #Data mining #Engineering #Genetics
paper · pdf · doi:10.1093/bib/bbw114
openalex publication_date 2016/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.