Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Abstract

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.

Figure 1.

Overview of the progressive advance in the methods to study genes, transcripts and proteins in the informatics sciences. The arrow represents the development, over time, of the many disciplines now involved in biomedical science accompanied by the fundamental advances in informatics and community resources. The broad roots of the omics revolution are represented by the wider start of the arrow before the year ‘1950’, when the foundations for a paradigm shift in science (from single observations to systems dynamics) were laid.

Figure 2.

Overview of the types of variants in the genome, their potential consequences and the methods/techniques to untangle them.

Figure 3.

Summary of various features associated with either RNA-microarrays or RNA-sequencing data generation and analysis.

Figure 4.

Summary of protein structural features and methods to generate and analyse proteomics data. The crystal structure of the haeme cavity of the haemoglobin of Pseudoalteromonas haloplanktis (4UUR [75]) was downloaded from PDB and visualized by RasMol (http://www.openrasmol.org/Copyright.html#Copying).

Figure 5.

Scheme of a typical functional enrichment analysis. A sample and reference set are compared to highlight the most frequent (i.e. enriched) features within the sample set.

Figure 6.

Overview on a global approach for the study of health and disease. Ideally, for individual samples, comprehensive metadata (0) should be recorded. To date, (1), (2) and (3) are being studied mainly as compartmentalized fields. A strategy to start integrating these fields currently relies on functional annotation analyses (4) that provide a valuable platform to start shedding light on disease or risk pathways (5). The influence of other elements such as epigenomics, pharmacogenomics, metabolomics and environmental factors on traits is important to have a better and more comprehensive understanding of their pathobiology. The assessment and integration of all such data will allow for the true development of successful personalized medicine (6). Color codes: green = addressed and in progress; orange = in progress; red = not yet addressed; yellow = ideal but not yet fully implemented. The gradually darker shades of green and increased font sizes indicate the expected gradual increase in the translational power of global data integration.