Abstract
The innovations in genome sequencing technologies have emanated in better understanding of biosystems leading to the dawn of the “omics” era. Proteomics has been an integral interface in the post-genomic era, and has allowed researchers to explore other omics-based platforms like metabolomics, transcriptomics, phenomics, etc. In pursuit of obtaining a systemic understanding of biosystems, the scientific community is now largely incorporating a multi-omics-based workflow, with genomics and proteomics at the centre of this integrated approach. Techniques such as gel-based proteomics, mass spectrometry, protein microarrays and label-free platforms have emerged as powerful tools for high-throughput screening and discovery-based studies in many of these multi-omics disciplines. However, with increased throughput, large amount of data is generated, and analysis of huge data often poses a challenge to researchers. The automation in specialized software has been immensely helpful to researchers in data acquisition; however, the downstream workflow of these sophisticated technologies continues to disconcert scientists, embracing an integrated multi-omics approach. This chapter aims at providing an overview of various proteomics-based technologies and their data evaluation strategies in context to biological studies. Data storage in specialized databases also requires attention, but is beyond the scope of this chapter. Gel-based proteomics, mass spectrometry, protein microarrays and label-free technologies are some of the commonly employed techniques in metabolomics, interactomics, genomics and transcriptomics, thus encompassing a multi-omics perspective on data analysis.
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Ghantasala, S. et al. (2016). Omics: Data Processing and Analysis. In: Srivastava, S. (eds) Biomarker Discovery in the Developing World: Dissecting the Pipeline for Meeting the Challenges. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2837-0_3
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DOI: https://doi.org/10.1007/978-81-322-2837-0_3
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