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Review
. 2018 Jan;24(1):85-101.
doi: 10.1016/j.molmed.2017.11.001. Epub 2017 Dec 5.

Panomics for Precision Medicine

Charanjit Sandhu  1 , Alia Qureshi  2 , Andrew Emili  3
Affiliations
Review

Panomics for Precision Medicine

Charanjit Sandhu et al. Trends Mol Med. 2018 Jan.

Abstract

Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.

Keywords: genomics; metabolomics; panomics; precision medicine; proteomics; transcriptomics.

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Figures

Figure 1
Figure 1. Proposed Model of Precision Medicine Approaches
Data from –omics subfields are integrated (Panomics) to guide patient care in a manner that accounts for each patient’s genetic variation.
Figure 2
Figure 2. Targeted Chromatin Immunoprecipitation
The technique involves covalent stabilization of protein-DNA interactions with formaldehyde. The DNA-protein complexes are immunoprecipitated and the DNA can be analyzed by microarray or whole genome sequencing.
Figure 3
Figure 3. Proteomic Analysis
Proteins undergo digestion with trypsin. Tryptic fragments are analysed by Tandem Mass Spectrometry (MS/MS). Mass to charge (m/z) ratios are determined for the parent and daughter fragments. Spectra are then matched with an in-silico database for protein identification.
Figure 4
Figure 4. Proposed Panomics Integration of Electronic Medical Records (EMR) with Biobanks using Artificial Intelligence for Biomarker Discovery
Data pipelines connecting EMR with repositories of –omics data may be required to facilitate big data analysis. Artificial intelligence algorithms may be able to search the multi-dimensional data to identify biomarker networks and targets for therapy relevant to disease types.
See this image and copyright information in PMC

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