TABLE 1.

Strengths and weaknesses of the individual omics technologies described in this review.

Data set	Strengths	Limitations	Recent advances	Citations
Genomic	Immutable link to the organism; databases of reference genomes often available to aid reconstruction; provides a static image of genes of interest; high throughput sequencing as standard	Short read sequencing results in gaps in “hard to sequence” regions; impossible to determine the activity of the genetic elements sequenced; difficult reconstruction of genomes with bioinformatic software	Third generation sequencing; simultaneous epigenetic determination with genome sequencing; higher throughput for shotgun meta-genomics	Bovee et al., 2007; Sims et al., 2014; van Dijk et al., 2018
Transcriptomic	Robust data on the requirements of a microbe in a given environment; vast quantity of data is produced; effective combination with single cell technologies	RNA isolation and sequencing are susceptible to handling errors; the transient nature of RNA only provides a snapshot of the needs of the organisms; presence of RNA’s does not necessarily predict the translation into proteins	Higher throughput Next Gen Sequencers (NovaSeq 6000); meta-transcriptomics of large systems is now possible; more reliable software for integration and variant determination	Lohse et al., 2012; Vogel and Marcotte, 2012; Liu et al., 2016; Dagogo-Jack and Shaw, 2018
Proteomic	Significant database of known proteins provide a strong platform to predict function; robust link between an organisms proteomic profile and its phenotype; provides a more stable image of the current requirements of the organism than other omics technologies	Throughput capabilities of proteomics lags behind other omics; expensive MS machinery is required for proteomic research; concessions are made in order to analyze the vast array of proteins – Splitting large proteins into smaller sections to facilitate MS analysis	Orbitrap Mass Spec facilitates ionization of more complex proteins; combining liquid chromotography with multiple MS’s allows accurate depiction of specific groups of proteins; powerful analytical tools i.e., PECAN, facilitate more accurate predictions from untargeted proteomics	Pascal et al., 2008; Michalski et al., 2012; The Uniprot Consortium, 2014; Ting et al., 2017; Monaci et al., 2018
Metabolomic	Direct connection between phenotype and metabolomics profile; provides an image of many well-studied metabolites simultaneously; diverse range of applications across many fields	The transient nature of metabolites makes them susceptible to sampling artifacts; numerous costly LC/GC and MS machines needed for processing	Back to back LC or MS machines provide higher resolution of specific groups e.g., LC-MS/MS; new machinery such as High Temperature-Ultra High Performance LC are overcoming previously difficult to detect metabolites; single cell sorting advances are facilitating robust single-cell metabolomics in the near future	Wang et al., 2014; Chetwynd et al., 2015; Rebollar et al., 2016; Ferrocino and Cocolin, 2017; Zhang and Vertes, 2018

The recent advances in their respective fields are included to illustrate the frequent progress in this area. As discussed below, many of the weaknesses that are mentioned may be overcome by integration of several of these omics technologies together.